Decomposition in multi-component AlCoCrCuFeNi high-entropy alloy

Trans. Nonferrous Met. Soc. China 29(2019) 349−357Self-organized layered growth phenomena ofdiffusion couples with spinodal decomposition in binary alloysYong LU1,2, Yuan-yuan CUI1, Qiao-qiao TANG1,Cui-ping WANG1,2, Zhen-bang WEI1, Shui-yuan YANG1,2, Xing-jun LIU1,21. College of Materials, Xiamen University, Xiamen 361005, China;2. Fujian Key Laboratory of Materials Genome, Xiamen University, Xiamen 361005, ChinaReceived 21 March 2018; accepted 23 August 2018Abstract: In order to investigate the formation mechanisms of the layered growth phenomena in diffusion couples with spinodal decomposition, a phase field model combined with elastic strain field was employed. Microstructure evolutions of diffusion couple with spinodal decomposition in binary alloys were numerically simulated by considering concentration fluctuation and elastic anisotropy. The simulation results indicate that the number of the periodical layers decreases with the increase of initial concentration fluctuation, even with large elastic anisotropy. The growth of layered microstructures can be attributed to the directional diffusion enhanced by initially discontinuous chemical potential at the interface.Key words: spinodal decomposition; concentration fluctuation; phase field method; layered structure; diffusion couple1 IntroductionThe concentration gradient, mismatch degree andmicrostructure of interfaces have essential effects on thephysical properties for various kinds of materials, such asmetals [1], semiconductors [2,3] and ceramics [4]. Thedirectionally layered structures formed at the interfaceare widely reported especially in those materials withspinodal decomposition [5,6]. Due to the fast atomdiffusion and defects relaxation at the interfaces, phaseseparation process near the surface can be stronglyinfluenced [7]. Accordingly, there are several types ofmicrostructures formed at the interface by spinodaldecomposition, e.g., spherical, interconnected, layeredand mixed [5]. The spherical and interconnectedmorphologies are the typical spinodal patterns and thelayered morphology has been found in binary polymermixture films accounting for surface-directed spinodaldecomposition [8,9]. It is pointed out that the nano-scalelayered structure is ultrahard, ultrastable and with highcoarsening temperature [10]. Other than that, theelectrochemical performance of lithium ion batteries canbe enhanced by reasonably designing layer-by-layerstructure of electrode [11]. During spinodaldecomposition, it is suggested that the concentrationfluctuation has significant effects on the mechanisms ofphase separation kinetics, even for very superficialquenches [12]. Thus, the transformation progresses ofspinodal decomposition triggered by different initialconcentration fluctuations at/near the weld interface andinternal bulk materials can result in differentmorphological structures. Therefore, it is necessary tostudy the effects of concentration fluctuation onmicrostructure evolutions near the interface of thediffusion couples.Spinodal decomposition is a process from a uniform,thermodynamic non-equilibrium state to a non-uniform,equilibrium state by aging the system below criticaltemperature (T C). The former state is a single phase,while the latter one is composed of two coexisting phases.During annealing, the uniformity could be broken by thegrowth of concentration fluctuation [5]. The growthdynamics of concentration fluctuation in the early stageof spinodal decomposition in polymer blend [13,14]and alloys [15,16] have been studied by many authors.Foundation item: Project (2017YFB0702401) supported by the National Key R&D Program of China; Project (51301146) supported by the National Natural Science Foundation of China; Projects (20720170038, 20720170048) supported by the Fundamental Research Funds for theCentral Universities, ChinaCorresponding author: Yong LU, Tel: +86-592-2187966, E-mail: luyong@; Xing-jun LIU, E-mail: lxj@DOI:10.1016/S1003-6326(19)64944-7Yong LU, et al/Trans. Nonferrous Met. Soc. China 29(2019) 349−357350 However, few researches are focused on the effect of initial concentration fluctuation on spinodal decomposition occurring in binary alloy diffusion couples. The concentration fluctuation is related to the factors such as the temperature, the interactions between elements [17,18]. In diffusion couples, the sharp change of concentration at the weld interface can induce sharp interface of chemical potential and the microstructure near the weld interface may have directional growth tendency [19,20]. In addition, the elastic strain induced by lattice misfit was reported to have considerable effects on the spinodal decomposition [21,22]. Thus, by controlling the process condition (e.g., temperature, composition and interface), layered structures as well as the excellent properties could be acquired.In this work, we restricted ourselves to simulate the microstructure evolutions in diffusion couples with spinodal decomposition by setting different values of initial concentration fluctuation and composition of the diffusion couples. Considering the elastic misfit strain caused by coherency in the solid alloys, we chose various sets of elastic constants to describe the elastically isotropic and anisotropic systems. Thus, the phase-field method was used to study the morphological evolution in diffusion couples of binary alloy with spinodal decomposition. The microstructure evolutions influenced by the concentration fluctuation with/without the elastic strain field were simulated, where the elastic energy was introduced by combining the microelasticity theory and discrete lattice diffusion equation into the system.2 Simulation methods2.1 Phase-field model2.1.1 Concentration fieldAssuming the mobility of species is independent oftheir positions, the governing equation for a A −B binaryalloy can be written by [23]c δ[()](,)δc FM t t cξ∂=∇∇+∂r (1)where c is the molar fraction of species B, F is the total free energy of the system. If the chemical mobility of alloys A and B are assumed to be equal, M is given by [24]()1Dc c M RT -=(2)where D is the inter-diffusion coefficient, R is the gasconstant and T is the temperature. To further simplify thecalculation, the factor c is a constant given by the initial composition, c 0. ξc (r , t ) is thermal fluctuation at position r and time t in the microstructure which represents concentration fluctuation in this simulation [25]. A Gaussian random noise term satisfying thefluctuation-dissipation theorem, in this case:2c c B (,)(,)2δ()δ()t t k TM t t ξξ''''=-∇--r r r r (3)In this model, the interfacial energy is introduced by gradient term of field variables c and considered isotropic. If the elastic energy is neglected, the total chemical free energy for the A −B binary system is given by()2c ,d 2V F G c T c V κ⎡⎤=+∇⎢⎥⎣⎦⎰ (4)where G (c ,T ) is the molar Gibbs energy density. In order to study the effect of concentration fluctuations induced by temperature on formation of interfacial layered structure, the following simulations are performed by a constant free energy hump. Thus, the parameters G (c ,T ) can be expressed as G (c ). κ is the gradient energy density. By applying the double-well potential function to the solution phases of the A −B binary system, the molar Gibbs free energy of the system becomeseq eq 22012()()()G c G c c c c =∆-- (5)where c represents the concentration field; ΔG 0represents the potential barrier; eq 1c and eq2c represent the equilibrium compositions . 2.1.2 Elastic energy modelElastic energy grows out of the lattice mismatch between different phases. The lattice parameters are considered solely related to the composition in the cubic system that is elastically homogeneous. Therefore theintrinsic strain tensor 0()ij εr can be expressed as 000()δ()ij ij c εε=r r (6) where ()000d ()/d ij ij a c a c εδ= represents the composition coefficient of crystal lattice parameter and δij is the Kronecker −Delta function. a (c ) is the lattice parameter of a solid solution of composition c and a 0 is the lattice parameter of pure solvent. According to the Hooke’s law, the stress σij can be written as a function of elastic strain: 0(()())ij ijkl kl kl C σεε=-r r (7)where ()kl εr is the total strain measured about a reference lattice, and ijklC is the elastic constant of thecubic system. The factor 11124444(2)/C C C C ζ=-- is defined to characterize the elastic anisotropy of alloys incubic crystal structure [26]. Under the above conditions, the elastic strain energy related to the configuration is expressed in the reciprocal space [27]:el 12E =323[()|()|](2π)d k B c ⎧⎫⎪⎪⎨⎬⎪⎪⎩⎭n k (8)where ()c k is the Fourier transform of ()c r . TheYong LU, et al/Trans. Nonferrous Met. Soc. China 29(2019) 349−357 351integral in the infinite reciprocal space is evaluated as a principal value excluding a volume 3(2π)/V around the point n =0.000000()()ijkl ij kl i ij jk kl l B C n n εεσΩσ=-n n (9)In the expression, n =k /k represents a unit vector in reciprocal space, ()jk Ωn is the Green tensor which is inverse to the tensor ijkl k l C n n .Thus, the total free energy function of coherent inhomogeneous system can be expressed asF =F c +E el (10)3 Simulation results3.1 Numerical proceduresFor numerical convenience, the governing Eq. (1) can be rewritten as the following form: 22c '(')(('))(,)c F M c r t t c κξ∂∂'''=∇-∇+∂∂ (11)0equilibrium compositions (c eq 1 and c eq2) are 0.1 and 0.9, respectively. The simulation started in the several diffusion couples e.g. AB10/AB50 (which means c B =0.1 at the left side of the diffusion couple, while c B =0.5 at the right side of the diffusion couple), AB40/AB60,while the serials of concentration fluctuation cξ'=10−2, 10−3, 10−4, 10−5 and 10−6 for the system where elastic anisotropy factor (ζ=−1, 0 and 1 ) for elastic system. In the numerical calculation, the dimensionless time Δt =0.001 and the max count of time step is 2×105, while the simulation results were output at appropriate time step. We use periodic boundary conditions in this simulation and three sets of elastic constants are given by (C 11=400, C 12=200, C 44=100), (C 11=350, C 12=200, C 44=150) and (C 11=250, C 12=100, C 44=50) to acquire the different ζ, for 0, −1 and 1.3.2 Microstructure evolution in diffusion coupleswithout elastic fieldTo investigate the influences of the initialconcentration fluctuation on the microstructure evolutionin diffusion couples without elastic field, the simulations are initiated with the different values of concentration fluctuation, e.g. 10−2, 10−3, 10−4, 10−5 and 10−6. The concentration fluctuation on the basis of Gaussian distribution to a given initial composition leads to a spontaneous phase separation. The atomic sizes of A and B atoms in the couple are assumed to be the same, while the elastic stress fields induced by lattice misfit are ignored. Therefore, the spinodal decomposition will be driven by the chemical free energy and the interfacial energy. The simulated microstructure evolutions of the diffusion couples AB10/AB50 and AB40/AB60, initiated with different concentration fluctuations are shown in Figs. 1 and 2. In the figures, dark blue indicates the B-rich phase and dark red indicates the A-rich phase. Because the periodic boundary conditions are used in this work, the interface between the left and right ends of the diffusion couple is equivalent to the interface between the diffusion couples.Fig. 1 Simulated microstructures of diffusion couple AB10/AB50 at t =25 with different initial concentrationfluctuations: (a) |ξ′c |=10−2; (b) |ξ′c |=10−3; (c) |ξ′c |=10−4; (d) |ξ′c |= 10−5; (e) |ξ′c |=10−6The simulated microstructures of diffusion couple AB10/AB50 at t =25 with initial concentration fluctuationof 10−2, 10−3, 10−4, 10−5 and 10−6 are shown in Figs. 1(a)−(e). Since the composition at the left side of the diffusion couple is the equilibrium composition, according to Eq. (4), no spinodal decomposition occurs. At the right side, it can be obviously seen that the interconnected structures appear in the central part andYong LU, et al/Trans. Nonferrous Met. Soc. China 29(2019) 349−357 352Fig. 2Simulated microstructures of diffusion couple AB40/ AB60 at t=25 with different initial concentration fluctuations: (a) |ξ′c|=10−2; (b) |ξ′c|=10−3; (c) |ξ′c|=10−4; (d) |ξ′c|= 10−5; (e) |ξ′c |=10−6layered structures prefer to form near the interface. The number of the layers gradually increases with the decrease of concentration fluctuation, ξ′c. Due to the various values of the initial concentration fluctuation, the stages of the spinodal decomposition in the central part of the right side are different. Figure 2 shows the simulated microstructure evolution of diffusion couple AB40/AB60, where the initial compositions on both sides of the diffusion couples are in the spinodal region. By considering different values of initial concentration fluctuation, one can clearly see that layered structures can form on both sides of the interface and the number of the layers increases with the decrease of the concentration fluctuation. Comparing Fig. 2 with Fig. 1, the spherical and rod structures formed instead of interconnected structures. With the increase of concentration fluctuation, the layered structures are gradually broken and disappear due to the coarsening of the particles, as shown in Figs. 2(a) and (b).The relationships between the number of layers and the concentration fluctuation in different diffusion couples at t=25 are shown in Fig. 3, where N L represents the number of layers and t represents the dimensionless time. One can clearly see that N L decreases with the increase of the concentration fluctuation. The relationship curves for the AB10/AB40 and AB10/AB60 are coincident. The values of N L of AB10/AB50 diffusion couple are larger than the previously mentioned two diffusion couples except for ξ′c=10−4. The N L of the diffusion couple AB40/AB60 is exactly the summation of the N L of AB10/AB40 and AB10/AB60, which indicates that the concentration gradient between two sides of diffusion couple has little effect on the value of N L. The number of layers in the diffusion couple AB10/AB50 has linear relationship with the logarithm of the initial concentration fluctuation, while the values of N L for other three diffusion couples increase sharply between 10−4and 10−2of concentration fluctuation. It should be mentioned that, according to the simulation results under long time aging, the number of layers increases first and then decreases to a stable value.Fig. 3Variation of number of layers (N L) with logarithms of initial concentration fluctuations in different diffusion couples at t=253.3 Microstructure evolution with elastic strain fieldDuring phase separation, if there is large coherent strain between decomposed phases, the elastic strain field should be considered. The different values of elastic anisotropic factor are set to investigate the influences of elastic anisotropy on microstructure evolution, e.g. ζ=0, −1 and 1. The simulation results for AB10/AB50 and AB40/AB60 with different ζ and ξ′c are shown in Figs. 4−9. The simulation grid size is 512×64.As can be seen in Figs. 4(a)−(e), by introducing the elastic strain field with anisotropic factor ζ=0 to the diffusion couple AB10/AB50, the microstructure evolutions are similar with the results shown in Fig. 1, where the value of N L gradually increases with the decrease of concentration fluctuation in the right side. Comparing Fig. 4 with Fig. 1, the alloy decomposed slower than that of the elastically free system.Figure 5 shows the simulated microstructure of AB10/AB50 with anisotropic factorζ=−1. The structures of the interface are similar to the previous simulation results, while the internal structures are orderly arranged. The growth of phases during the spinodal decomposition inclined to be directional compared with Fig. 4. As shown in Figs. 5(a)−(e), the elastic anisotropy affects the distribution and growth orientation of the structure, thatYong LU, et al/Trans. Nonferrous Met. Soc. China 29(2019) 349−357 353Fig. 4 Simulated microstructures of diffusion couple AB10/ AB50 for ζ=0 at t=90 with different initial concentration fluctuations: (a) |ξ′c|=10−2; (b) |ξ′c|=10−3; (c) |ξ′c|=10−4; (d) |ξ′c|= 10−5; (e) |ξ′c|=10−6Fig. 5Simulated microstructures of diffusion couple AB10/ AB50 for ζ=−1 at t=70 with different initial concentration fluctuations: (a) |ξ′c|=10−2; (b) |ξ′c|=10−3; (c) |ξ′c|= 10−4; (d) |ξ′c |=10−5; (e) |ξ′c|=10−6Fig. 6Simulated microstructures of diffusion couple AB10/ AB50 for ζ=1 at t=70 with different initial concentration fluctuations: (a) |ξ′c|=10−2; (b) |ξ′c|=10−3; (c) |ξ′c|=10−4; (d) |ξ′c|= 10−5; (e) |ξ′c|=10−6is, the structure prefers to grow in parallel or perpendicular to the x direction.By given anisotropic factor ζ=1, the simulation results with the same initial composition and the otherFig. 7 Simulated microstructures of diffusion couple AB40/ AB60 for ζ=0 at t=160 with different initial concentration fluctuations: (a) |ξ′c|=10−2; (b) |ξ′c|=10−3; (c) |ξ′c|= 10−4; (d) |ξ′c |=10−5; (e) |ξ′c|=10−6Fig. 8Simulated microstructures of diffusion couple AB40/ AB60 for ζ=−1 at t=120 with different initial concentration fluctuations: (a) |ξ′c|=10−2; (b) |ξ′c|=10−3; (c) |ξ′c|= 10−4; (d) |ξ′c |=10−5; (e) |ξ′c|=10−6Fig. 9Simulated microstructures of diffusion couple AB40/ AB60 for ζ=1 at t=75 with different initial concentration fluctuations: (a) |ξ′c|=10−2; (b) |ξ′c|=10−3; (c) |ξ′c|=10−4; (d) |ξ′c|= 10−5; (e) |ξ′c|=10−6variable parameters are shown in Fig. 6. Compared with the previous results, lamellar structures are unfavorable to form near the interface of the diffusion couple, due to the growth preferable direction along the 45°or 135°Yong LU, et al/Trans. Nonferrous Met. Soc. China 29(2019) 349−357 354from the x direction. There are no lamellar structures but more obvious interconnected structures formed in Figs. 6(a) and (b). However, the layered structures still appear in case of small initial concentration fluctuation, as can be seen in Figs. 6(c)−(e).Figures 7−9 show the simulated microstructure of AB40/AB60 with anisotropic factorζ=0, −1 and 1. As can be seen, the internal structure tends to be spherical or rod-like. Both sides of the interface form the layered structure and the number of layers are less for ζ=1.The relationships between the number of layers and aging time in the diffusion couple AB10/AB50 for ζ=0, −1 and 1 are shown in Fig. 10. One can see that theFig. 10Variation of number of layers (N L) with time (t) in diffusion couple AB10/AB50 with different elastic anisotropic factors: (a) ζ=0; (b) ζ=−1; (c) ζ=1 number of layers linearly increases with time and reaches a stable value. Figure 11 shows the similar relationship curves of diffusion couple AB40/AB60. The N L−t curves of the different initial concentration fluctuations were coincident in the early stage. The layers are paired to appear and disappear simultaneously at later stage, so that there are some steps in the curves. The number of layers over time increases at first then decreases as the aging proceeds. The maximum number of layers corresponding to different anisotropic factors in diffusion couples AB10/AB50/ and AB40/AB60 are shown in Table 1.Fig. 11Variation of number of layers (N L) with time (t) in diffusion couple AB40/AB60 with different elastic anisotropic factors: (a) ζ=0; (b) ζ=−1; (c) ζ=1Yong LU, et al/Trans. Nonferrous Met. Soc. China 29(2019) 349−357355Table 1 Maximum number of layers versus different initial concentration fluctuations ξ′c for different anisotropic factors ζ in diffusion couples AB10/AB50 and AB40/AB60 Diffusion couple ξ′cζ=0 ζ=−1 ζ=1 AB10/AB5010−4 7 9 5 10−5 11 12 8 10−6 13 14 9 AB40/AB6010−414 20 10 10−5 22 22 16 10−62630184 DiscussionThe microstructure evolutions of binary alloydiffusion couples with spinodal decomposition were investigated by considering the effect of initial concentration fluctuation and elastic anisotropy. According to the simulation results, several types of interface microstructures were obtained: spherical, layered, rod-like and interconnected structures. The cross-over morphology of typical spinodal decomposition is the competition of definitive solution of the model equation and inherent concentration fluctuation.The typical structures of spinodal decomposition, e.g. spherical, rod and interconnected structures are due to the continuous concentration modulation during annealing. However, the progress of the spinodal decomposition process is remarkably affected by the sharp interface between the two diffusion couples. In the diffusion couple AB10/AB50, the B-rich layer forms firstly at the weld interface of the right side that is based on interfacial energy reduction like the surface-directed mechanism [20]. Once the B-rich layer grows near the interface, it is difficult for the A atoms to diffuse through the B-rich layer to the right side, leading to the formation of B-lean layer at the right side of the B-rich layer [28]. Due to the same growth mechanism, the A-rich layer or B-rich layer forms in the new interface constantly, the A-rich and B-rich alternating layers will form. This result is consistent with the reported experimental observations in Fe −Si −Ge diffusion couple, where the self-organized Si-rich and Ge-rich layers formed at the weld interface ascribing to inhomogeneous interdiffusion and spinodal decomposition [6]. However, the spinodal decomposition in the right of diffusion couple continuously proceeds from the beginning. The formation of layered structures near the surface can be interrupted by the normal decomposition far from the interface. In order to apply the present results to real binary alloys, a parameter*222cB ()/(2||)l t k TM ξξ'=∆∇⋅=ρ can be used to normalize the concentration fluctuation. Accordingly, thelamellar structure could be designed by calculating ξ* with aging temperature and chemical mobility of the alloy. When ξ′c is large enough, the layered structures will be broken by the local fast spinodal decomposition especially the coarsening stage, while the system with small ξ′c is preferable to form more layers due to the longer early stage of the decomposition process far from the interface. BALL et al [5] studied the effects of temperature gradients and boundary conditions at the surface of a system separating into two phases via spinodal decomposition, and found that increasing the noise strength the layers formed by slow cooling are roughened. In addition, the volume fraction also has significant effect on the number of layers that smaller volume fraction results in fewer layers by comparing Fig. 1 and 2. In case of smaller fraction of the second phase, the layered structure could form at the early stage and be broken during coarsening [19]. Due to the competing relationship between the directional diffusion at the interface and the typical spinodal decomposition far from the interface, layered structure can only form within a certain range of materials. When the typical spinodal decomposition far away from the interface is developed, only a few layers can form. Therefore, controlling external conditions is of great significance for the formation of layered structure.According to Figs. 4−9, the elastic anisotropy leads to the obvious difference of microstructure evolution. The evolution of elastic materials decomposed slower than the elastically free system [29]. The slow decomposition kinetics in the diffusion couple with the coherency strain energy are due to the decrease in the driving force of spinodal decomposition as it has been pointed out long time ago by CAHN [30]. The elastically anisotropic materials decompose slightly faster than the isotropic materials because the elastic anisotropy provides soft directions (when ζ<0, the soft direction is 〈100〉; When ζ >0, the soft direction is 〈111〉) that have low strain energy [29]. In the present work, even under elastic strain field, the layered structures still form in case of small initial concentration modulation, which indicated that the chemical potential could dominate the diffusion process. The formation mechanisms are the same as elastic-free system. However, due to the effect of the elastic anisotropy, orientation of the structure for ζ=−1 is parallel/perpendicular to the interface, more layers can form near the interface, and accompanied with the cross-over structure far from the weld interface. In case of ζ=1, the rod-like structures prefer to grow along the direction 45° from the interface, resulting in the discontinuous layered structures near the interface of the diffusion couple. This kind of layered structures can be broken and finally disappear in case of larger differenceYong LU, et al/Trans. Nonferrous Met. Soc. China 29(2019) 349−357 356of phase fractions due to the combined effects of the elastic anisotropy and coarsening.5 Conclusions(1) The self-organized periodical layered structure can form near the interface of the diffusion couple during spinodal decomposition. The number of the periodical layers decreases with the increase of initial concentration fluctuation. During long time aging, the number of layers increases first and then decreases to a stable value.(2) The elastic strain field has remarkable effect on the number of layers near the weld interface and the distribution of internal structures. The layered growth can be enhanced when the preferable growth direction of the spinodal decomposition is parallel to the weld surface.(3) The growth periodical layered microstructures near the interface of the diffusion couple can be ascribed to the directional diffusion dominated by the discontinuity of the chemical potential at the interface and the following alternate growth of the two phases in spinodal decomposition.References[1]HODAJ F, DESRE P J. Effect of a sharp gradient of concentration onnucleation of intermetallics at interfaces between polycrystalline layers [J]. Acta Materialia, 1996, 44: 4485−4490.[2]ZHANG S, CAO X, WU J, ZHU L W, GU L. Preparation ofhierarchical CuO@ TiO2nanowire film and its application in photoelectrochemical water splitting [J]. Transactions of Nonferrous Metals Society of China, 2016, 26(8): 2094−2101.[3]WANG X Q, PEI Y L, MA Y. The effect of microstructure atinterface between coating and substrate on damping capacity of coating systems [J]. 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《材料科学基础》名词解释AOrowan mechanism （奥罗万机制）位错绕过第二相粒子，形成包围第二相粒子的位错环的机制。

Austenite(奥氏体)碳在γ-Fe中形成的间隙固溶体称为奥氏体。

B布拉菲点阵除考虑晶胞外形外，还考虑阵点位置所构成的点阵。

Half-coherent interface（半共格相界）两相邻晶体在相界面处的晶面间距相差较大，则在相界面上不可能做到完全一一对应，于是在界面上将产生一些位错，以降低界面弹性应变能。

这时两相原子部分保持匹配，这样的界面称为半共格界面。

Sheet texture（板织构）轧板时形成的组织的择优取向。

Peritectic reaction（包晶反应）固相和液相生成另一成分的固溶体的反应Peritectic segregation（包晶偏析）新生成的固相的芯部保留残余的原有固相，新相本身成分也不均匀。

Peritectic phase diagram（包晶相图）具有包晶反应的相图Peritectoid reaction（包析反应）由两个固相反应得到一个固相的过程为包析反应。

Cellular structure（胞状结构）成分过冷区很小时，固相突出部分局限在很小区域内，不生成侧向枝晶。

Intrinstic diffusion coefficient（本征扩散系数）依赖热缺陷进行的扩散的扩散系数。

Transformed ledeburite（变态莱氏体）渗碳体和奥氏体组成的莱氏体冷却至727℃时奥氏体发生共析反应转变为珠光体，此时称变态莱氏体。

Deformation twins（变形孪晶）晶体通过孪生方式发生塑性变形时产生的孪晶（BCC，HCP）Chill zone（表层细晶区）和低温铸模模壁接触，强烈过冷形成的细小的方向杂乱的等轴晶粒细晶区。

Burger’s vector（柏氏矢量）表征位错引起的晶格点阵畸变大小和方向的物理量。

Asymmetric tilt boundary（不对称倾斜晶界）晶界两侧晶粒不对称的小角度晶界，界面含两套垂直的刃型位错。

Software DesignTheoretical principlesDesign methodsThe Programmer’s Approach to Software EngineeringSkip the requirements engineering and design phases, start writing code3Why this Programmer’s Approach?Requirements engineering and design are a waste of timeThe wish (or need) to show something as soon as possibleAssessment on the basis of LOC/monthReal or perceived time pressureWhat is design?Decomposition of the system into a set of interacting componentsQuestion: How to do it?5Preliminary remarksDesign is an iterative processThere often is interaction between requirements engineering, design and (formal) specificationSystem-oriented approachThe central question: how to decompose a system into parts such that each part has lowercomplexity than the system as a whole, while the parts together solve the user’s problem?In addition, the interactions between the components should not be too complicated7Design criteriaProperties of a system which make it flexible, maintainable, … Abstraction ModularityProper system structure Information hidingComplexityAbstractionProcedural abstraction: natural consequence of stepwise refinement; the name of a procedure denotes a sequence of actionstimeProcedural abstraction Problem decomposition9Abstraction (cont.)Data abstraction: aimed at finding a hierarchy in the dataApplication-oriented data structuresSimpler data structuresGeneral data structuresModularityStructural criteria which tell something about individual modules and their interconnectionsCohesion and couplingCohesion: glue inside the moduleCoupling: strength of connections between modules11Functional cohesion Coincidental cohesion Logical cohesion Temporal cohesion Procedural cohesionCommunicational cohesion Sequential cohesion Functional cohesionData cohesionModules that encapsulate an ADT (OO)How to determine the cohesiontype?Describe the purpose of a module in one sentenceIf the sentence is compound, contains a comma or more than one verb→it probably has more than one function: logical or communicational cohesionIf the sentence contains time-related words like: “first”, “then”, “after” →temporal cohesionif the verb is not followed by a specific object →probably logical cohesion (example: edit all data)words like “startup”, “initialize”imply temporal cohesion13Content coupling Common coupling Control coupling Stamp couplingData couplingCoupling levels are technology(data type) dependentData coupling assumes scalars or arrays, no recordsControl coupling assumes passing of scalar dataNowadaysModules may pass complex data structuresModules may allow some modules access to their data, and deny it to others (so there are many levels of visibility)Coupling need not be commutative (A may be data coupled to B, while B is control coupled to A)15Strong cohesion, weak coupling→simple interfaces→Simpler communication Simpler correctness proofsChanges influence other modules less often Reusability increasesComprehensibility improvesInformation hidingEach module has a secretDesign involves a series of decisions. For each such decision, questions are: who needs to know about these decisions? And who can be kept in the dark?Information hiding is strongly related toAbstraction: if you hide something, the user may abstract from that factCoupling: the secret decreases the coupling between a module and its environmentCohesion: the secret is what binds the parts of a module17ComplexityMeasure certain aspects of the software Use these numbers as criterion to assess a decomposition, or guide decompositionInterpretation: higher value →higher complexity →more effort requiredTwo kinds:intra-modular: inside one module inter-modular: between modulesComplexity measures: summaryFor small programs, the various measures correlate well with programming timeHowever, a simple length measure such as LOC does equally wellComplexity measures are not very context sensitiveComplexity measures take into account few aspectsLook at complexity density19System structureLook at complexity of dependencies between modulesDraw modules and their dependencies in a graphThen arrows may denote several things A contains B A precedes B A uses BWe are mostly interested in the latter type of relationSystem structureThis is the uses relationIn a well-structured piece of software, the dependencies show up as procedure calls. Therefore this graph is known as the call-graphPossible shapes:Chaos (directed graph) Hierarchy (acyclic graph) Strict hierarchy TreeIn a picturechaos hierarchystrict hierarchy tree21Design methodsFunctional decompositionData flow design (SA/SD)Design based on data structures (JSD/JSP)*Finite state machine (FSM) and StatechartsObject-Oriented design(*) Jackson System Development/Structured Programming23Functional decompositionTwo extremes: top-down and bottom-upIn their pure form, none of these are used (design is not a rational process)Clients do not know what they want, they are not able to tell everything they know Changes influence earlier decisions People make errorsPeople assume certain knowledge to be present Projects do not start from scratchRather, design exhibits a yoyo characterData flow designYourdon and Constantine (early 70s)Nowadays version: two-step processStructured Analysis (SA) resulting in a logical design drawn as a set of data flow diagrams Structured Design (SD) transforming the logical design into a program structure drawn as a set of structure charts25Top-level DFD: context diagrammanagementclientlibrary systemdirection reportrequest ackFirst-level decompositionmanagementclientprelim.proc.directionreportrequestackborrow titlemanage reportlog filelog data returnrequestborrow requestcatalog administrationtitletitlelog data return title27Second-level DFD for “preliminaryprocessing”check client datarequestprocess requestlog file log datareturnrequestnot OKclient data baseborrow requestOK Client infoExample minispecIdentification: process request Description:1. Enter type of request1.1. If invalid, issue warning andrepeat step 11.2. If step 1 repeated 5 times,terminate transaction 2. Enter book identification2.1. If invalid, issue warning andrepeat step 22.2. If step 2 repeated 5 times,terminate transaction3. Log client identifier, request type and book identification4. ...29Data dictionary entriesborrow-request = client-id + book-id return-request = client-id + book-idlog-data = client-id + [borrow | return] + book -idbook-id = author-name + title + (isbn) + [proc | series | other]Conventions:[ ] : include one of the enclosed options | : separates options + : AND( ) : enclosed items are optionalDataflow diagrams ÆStructure chartsResult of SAlogical model consisting of a set of DFDsaugmented by minispecs, data dictionary, etc.Structured DesignTransition from DFDs to structure chartsHow to carry out transition?Heuristics for this transition are based on notions of coupling and cohesionThe major heuristic concerns the choice for the top-level structure chart (as most systems are transform-centered)31Transformation guidelinesFor top-level structure: as most system aretransformation-based (i.e. they take some input, apply elaboration that transforms this input, and produce some output) one way to define the top-level structure is1.To trace the input through the data flow diagram until it cannot be considered input anymore2.The same applies in the other direction for the output3.Then every process between input and output belongs to the top-level process4.Processes in the DFD become modules in the Structure chart5.Data flows become module flows (in the opposite direction)6.If a central transformation is not identifiable, a dummy root module is definedTransform-centered designA BCHKDEF GInput Transform Output B do jobACDEFGH KSummaryEssence of design process: decompose system into partsDesirable properties of a decomposition: coupling/cohesion, information hiding, (layers of) abstractionAttempts to express these properties in numbers (metrics)Design Methods: many different techniques available …33。

TA431Deconvolution of Thermal Analysis Data usingCommonly Cited Mathematical ModelsKeywords: TGA, DSC, Curve Fitting, Deconvolution, Pearson IV , peak deconvolution, chemometrics ABSTRACTOverlapping thermal transitions observed in TGA and DSC experiments can be resolved to varying levels of success using numerical deconvolution methods. In this work, we demonstrate deconvolution with two examples of thermal data fitted using somecommonly cited mathematical models used for deconvolution of asymmetric and tailing data. Despite good quality of the data fit, there is significant scatter in the calculated results depending onthe model chosen, so independent assessment of the bias of theanalysis is necessary.INTRODUCTIONOverlapping transitions like those sometimes encountered inspectroscopy and chromatography data are also common inthermal analysis data. Consider the TGA data shown in Figure 1 of the mass loss of an engine oil ‘A’. The oil shows two apparentsingle step changes in mass. The relative symmetry of the derivative curves (shown in blue) are a good indicator of twomonotonous mass losses although it is a subjective observation.A common approach to analyzing TGA mass loss data is to usethe local minimum in the derivative curve as a guide for choosingthe analysis limits. In the example shown in Figure 1 a result of 92% for the first mass loss, 8% for the second, and a negligible amount of residue was obtained. When the derivative curve is wellresolved as in the case of engine oil ‘A’, good precision can be expected assuming consistency choosing limits.Figure 1. TGA Mass Loss and Mass Loss Rate for Engine Oil ‘A’The analysis for engine oil ‘B’ shown in Figure 2 is not straightforward. Notice the inflection in the derivative curve.One simple interpretation is that the inflection is due to differentrates of mass loss caused by different decomposition kinetics inthis first mass loss event. This may imply the presence of morethan one component, interaction of two components, possible transformation of one component into a form with differentdecomposition kinetics, etc. Estimating the beginning of the smaller transition centered around 300 °C is also not as simple in oil ‘B’. Figure 2. TGA Mass Loss and Mass Loss Rate for Engine Oil ‘B’The differences in between oils A and B are more apparent when the mass loss derivatives are overlaid in Figure 3.Figure 3. TGA Mass Loss Comparisons for Engine Oils ‘A’ and ‘B’One approach to analyze overlapping transitions is to apply a deconvolution method to the data to determine the relative areacontribution of the components. In the case of TGA, the mass loss data is the summation or integralof the decomposition by way of mass loss of the componentspresent, so the derivative with respect to either time or temperatureserves as the function for deconvolution.W e i g h t (%)T (ºC)d(Weight) / d(T ) (%/ºC)120 1.51.00.50.0-0.5204060801000-2001002003004.69 mg 92.2 %0.392 mg 7.72 %4.20e-4 mg 8.27e-3 %400500W e i g h t (%)T (ºC)d(Weight) / d(T ) (%/ºC)W e i g h t (%) —T (ºC)d(Weight) / d(T ) (%/ºC) - - -120 1.51.00.00.5-0.5204060801000-200100200300— Engine Oil ‘A’— Engine Oil ‘B’400500In the case of DSC data, this is simply the heat flow curve which is a differential (Equation 1)(1)Where dq/dt is the differential heat flow rate, C P is the specific heat capacity, dT/dt is the heating rate (sometimes abbreviated as β), and f(T,t) are temperature and time dependent functions. A complication of deconvolution of DSC data is the possibility of simultaneous endothermic and exothermic transitions often prevalent in polymorphic transformations as an example. Careful evaluation of these potential kinetic dependent transitions should be undertaken before attempting any deconvolution. BACKGROUNDThe problem of resolving overlapping peaks is certainly not new. Asymmetry, significant fronting, and sometimes tailing often observed in thermal data results in distributions that are not modeled ideally with Gaussian or Lorentzian functions often used to deconvolve spectroscopic and chromatographic data. Several approaches have been published in the literature using functions that fit asymmetric data. Koga and coworkers [1] fit TGA data using 9 different mathematical functions including asymmetric double sigmoid, asymmetric logistic, extreme value 4 parameter fronted, Fraser-Suzuki, log normal 4 parameter, logistic power peak, Pearson I V , Pearson I V (a3 = 2), and Weibull with fitting criterion that r 2 > 0.99.Michael et al. developed a function for deconvolving DSC nickel titanium phase transformation heat flow data [2]. Deconvolution has been used to separate complex chemical processes for kinetic analysis shown in Figure 4 by Khachani et al. [3]Figure 4. Peak Deconvolution Using Frazer-Suzuki Function; β=7 °C/min [3]For this work, we fit examples using the following models: Pearson IV , Asymmetric Logistic, Extreme Value 4 Parameter Fronted, Log Normal 4 Area, Logistic Power Peak, and Exponentially Modified Gaussian (EMG).EXPERIMENTAL1. Example 1 – Comparison of Two Motor Oils a. Instrument – TA Instruments Discovery 5500 TGA b. Heating Rate 1 °C / min (modulated TGA Experiment)c. Crucible – Ptd. Purge – N 2 at 25 mL / mine. Sample Mass - 5 mg nominal 2. Example 2 – Dynamic Curing of Epoxya. Instrument – TA Instruments Discovery 2500 DSCb. Heating Rate - 10 °C / minc. Purge – N 2 at 50 mL / mind. Crucible – Tzero Aluminume. Sample Mass – 2 mg nominal DATA REDUCTIONTGA and DSC data were exported as text files and analyzed using PeakFit v4.12 (Systat). The models used for fitting the data are contained in the software. For conciseness, we show illustrated data only from the Pearson IV fit.RESULTS and DISCUSSION 1. Comparison of Two Motor Oilsa. Oil ‘A’ – Figure 5 (same data as Figure 1) shows a typical TGA data reduction for this sample. The limits chosen where based on the position of the relative minima of the derivative curves.Figure 5. TGA Mass Loss Data for Oil ‘A’Figure 6 shows the deconvolution of the derivative of mass loss with respect to temperature signal using the Pearson I V model. Other models and the resultant area fractions are summarized in Table 1dq dt= C p+ ƒ (T, t)dT dtd α/d t (m i n -1)Temperature (ºC)0,25Experimental reaction rate First ProcessSecond Process Total Process0,10,150,20,050110120130140150160170190200180W e i g h t (%)T (ºC)d(Weight) / d(T ) (%/ºC)120 1.51.00.50.0-0.5204060801000-2001002003004.66 mg 91.7 %0.420 mg 8.26 %4.20e-4 mg 8.27e-3 %400500Figure 6. Deconvolution of Derivative of Mass Loss for Oil ‘A’ Using Pearson IV ModelResiduals for Oil ‘A’ are shown in Figure 7Figure 7. Deconvolution Residuals for Oil ‘A’Table 1. Area % Calculations for Oil ‘A’ by ModelThe quality of the data fit using the models is good but in this case the results obtained using the using the derivative minimum as a guideline lie withing the scatter range of the results obtained using the models. This is explained by the relatively good separation of the mass loss events. The scatter of the data set is shown in Figure 8.Figure 8. Scatter of Peak 1 Data in Oil ‘A’ Deconvolutionb. Oil ‘B’ – Figure 9 (same data as Figure 2) shows the data reduction for this sample. As stated previously, the first mass loss does not appear to be monotonous and deconvolution should make improvements to more accurately analyze the data.Figure 9. TGA Mass Loss Data for Oil ‘B’Figure 10 shows the deconvolution of the derivative of mass losswith respect to temperature using the Pearson IV model.Pearson IV 89.3710.63 1.0000.0072Asymmetric Logistic 93.30 6.700.9970.0202Extreme Value 4 Area Fronted 91.128.8840.9990.0084Log Normal-4-Area90.829.179 1.0000.0079Logistic Power Peak93.38 6.6220.9970.0211Exponentially Modified Gaussian 92.067.940.9980.0154Derivative Minimum91.78.26--d W /d T (% / m i n )d W /d T (% / m i n )Temperature ºCdW/dT (% / min)dW/dT (% / min)R e s i d u a l sTemperature ºCResidualsA r e a %Data PointW e i g h t (%)T (ºC)d(Weight) / d(T ) (%/ºC)120 1.21.00.80.60.40.20.0-0.240608010020001002003001.65 mg 28.2 %3.67 mg 62.9 %0.513 mg 8.80 %6.03e-4 mg 0.0103 %400500Figure 10. Deconvolution of Derivative of M ass Loss for Oil ‘B’ Using Pearson IV ModelResiduals for Oil ‘B’ are shown in Figure x.Figure 11. Deconvolution Residuals for Oil ‘B’Table 2 compares area fraction differences in oil sample ‘B’ using the models and the derivative minimum method.Table 2. Area % Calculations for Oil ‘B’ by Modeld W /d T (% / ºC )d W /d T (% / ºC )Temperature ºCdW/dT (% / ºC)dW/dT (% / ºC)R e s i d u a l sTemperature ºCResidualsPearson IV 84.837.048.13 1.0000.0040Asymmetric Logistic 83.638.617.760.9990.0107Extreme Value 4 Area Fronted 79.167.5513.29 1.0000.0081Log Normal-4-Area82.25 6.1111.64 1.0000.0034Logistic Power Peak87.52 6.01 6.47 1.0000.0060ExponentiallyModified Gaussian 83.82 6.469.72 1.0000.0070Derivative Minimum62.928.28.80--In this case the quality of the data fit is also good and likely does improve the accuracy of the data relative to the derivative minimum. There is also significant scatter within the results obtained using the models and is shown in Figure 12.Figure 12. Scatter of Peak 1 Data in Oil ‘B’ Deconvolution2. DSC Evaluation of Epoxy Curing – This example shows two dynamic temperature ramps of a common epoxy. At time t 0, the DSC analysis is straightforward and is shown in Figure 13.Figure 13. Heat Flow of Epoxy Cure at Initial Time (t 0) at Heating Rate 10 °C / minAt time t as the epoxy cures, a second diffusion driven exothermictransition becomes apparent shown in Figure 14.A r e a %Data PointQ (m W )T (ºC)Exo Up2-11-202040608010012014069.3 ºC152.70 J/g 32.6 ºCFigure 14. Heat Flow of Epoxy Cure at Time (t) at Heating Rate 10 °C / minIn this case where the peaks are poorly resolved, deconvolution is a means to a more accurate value for the relative contribution of the reaction and diffusion heat flow. Deconvolution of the epoxy at time t shown in Figure 15 and residuals are shown in Figure 16.Figure 15. Deconvolution of Derivative of M ass Loss for Partially Cured Epoxy Using Pearson IV ModelFigure 16. Deconvolution Residuals for Partially Cured EpoxyTable 3 summarizes and compares the relative peak areas of the reaction and diffusion heat flow due to the curing of the epoxy.Q (W g )T (ºC)Exo UpH e a t F L o w (W /g )H e a t F l o w (W /g )Temperature ºCHeat FLow (W/g)Heat Flow (W/g)Table 3. Area % Calculations for Partially Cured Epoxy by ModelThe quality of the data fit using the models in this example is also good and likely approaches accurate results. We expect that the uncertainty in this particular example would be partially mitigated by choosing fit parameters for the reaction (1st exotherm) and diffusion exotherms (2nd exotherm) based on the apparent symmetry of the reaction exotherm at time t0 assuming that the corresponding reaction exotherm at time t (Figure 14) is similar.Despite the advantage of obtaining the t 0 data, the models yield significantly different values. Scatter of the first peak of the epoxy data is shown in Figure 17. The choice of the initial fitting parameters is subjective, but the PeakFit software makes this easy with an interactive interface. A derivative minimum approach was not attempted for this sample.Figure 17. Scatter of Peak 1 Data in Partially Cured Epoxy DeconvolutionCONCLUSIONSPeak deconvolution is a practical tool to determine a more accurate relative contribution of unresolved thermal analysis events and is often used to resolve spectroscopic and chromatographic data. The mathematical models used for deconvolution in our examplesR e s i d u a l sTemperature ºCResidualsPearson IV51.4248.580.9990.0030Asymmetric Logistic 51.6048.400.9980.0032Extreme Value 4 Area Fronted71.1528.850.9970.0044Log Normal-4-Area67.7932.210.9970.0045Logistic Power Peak56.9343.070.9970.0046Exponentially Modified Gaussian 67.7232.280.9970.0038Derivative Minimum62.928.28.80-A r e a %Data Pointshow good correlation and standard error, but also show considerable scatter so that it is difficult to determine which yields the most accurate result. Independent assessment of the bias and error of the utilized model is needed especially in the user’s choice of initial fitting parameters.We used the same model for fitting each of the peaks in the examples and this may be a good starting point but is likely too simplistic an approach.ACKNOWLEDGMENTGray Slough, Ph.D., Product Marketing Specialist at TA Instruments. REFERENCES1. Koga, N., Goshi, Y., Yamada, S., Perez-Maqueda, L.; KineticApproach to Partially Overlapped Thermal Decomposition Processes; J Thermal Analytical Calorimetry (2013) 111:1463–14742. Michael, A., Zhou, Z., Yavuz, M., Khan, M.; Deconvolutionof Overlapping Peaks from Differential Scanning Calorimetry Analysis for Multi-Phase NiTi Alloys; Thermochimica Acta 665(2018) 53-593. Khachani, M., Hamidi, A., et al; Kinetic Approach of Multi-Step Thermal Decomposition Processes of Iron(III) Phosphate Dihydrate (FePO4.2H2O); Thermochimica Acta 610 (2015) 29-364. Heinrich, J.; A Guide to the Pearson Type IV Distribution; TheCollider Detector and Fermilab Website Notes on Statistics;https://www /physics/statistics/notes/cdf6820_ pearson4.pdf 5. Wang, T., Arbestain, M., Hedley, M., Singh, B., Pereira, R.,Wang, C.; Determination of Carbonate-C in Biochars; Soil Research, 2014, 52, 495–504, /10.1071/SR131776. QL Yan, Zeman, S., et al.; Multi-stage Decomposition of5-aminotetrazole Derivatives: Kinetics and Reaction Channels for the Rate-limiting Steps; Phys.Chem.Chem.Phys., 2014, 16, 242827. Leitao, M., Canotilho, J.,Cruz, M., Pereira, J., Sousa, A.,Redinha, J.; Study of Polymorphism from DSC Melting Curves – Polymorphs of Terfenadine; Journal of Thermal Analysis and Calorimetry, Vol 68 (2002) 397-4128. Carbon Component Estimation with TGA and MixtureModeling; https://smwindecker.github.io/mixchar/articles/Background.html9. Zhao, S.F., et al. Curing Kinetics, Mechanism andChemorheological Behavior of Methanol Etherified Amino / Novolac Epoxy Systems; Polymer Letters Vol.8, No.2 (2014) 95–106For more information or to request a product quote, please visit / to locate your local sales office information.。

•342.海军航空工程学院学报第34卷Chinese)[12] 崔伟成，李伟，孟凡磊，等.局部特征尺度分解与局部均值分解的对比研究[J].机械传动,2017,4(10):10-15.CUI WEICHENG, LI WEI , MENG FANLEI, et al. Study on the contrast between local characteristic-scale decom­position and local mean decomposition[J]. Journal of Me­chanical Transmission,2017,4(10)：10-15. (in Chinese) [13] WU Z, HUANG N E. A study of the characteristics ofwhite noise using the empirical mode decomposition method[J]. Proceedings of the Royal Society of London A：Mathematical, Physical and Engineering Sciences,2004,460:1597-1611.[14] 周德忠.互相关系数的分解研究m.武汉钢铁学院学报，1995,18(4) :442-443.ZHOU DEZHONG. On decomposition of cross-correla­tion coefficientsfJ]. Journal of Wuhan Iron and Steel Insti­tute ,1995,18(4)： 442-443. (in Chinese)[15] HUANG N E. Computing instantaneous frequency bynormalizing Hilbert transform： U.S., Patent 6901353[P].2005-5-31.[16] DEERING R, KAISER J F. The use of a masking signalto improve empirical mode decomposition[C]//Intema- tional Conference on Acoustics, Speech, and Signal Pro­cessing. Philadelphia ： IEEE, 2005 ： 485-488.[17] DELECHELLE E, LEMOINE J, NIANG O. Empiricalmode decomposition：an analytical approach for sifting process[J]. IEEE Signal Processing Letters, 2005, 12(10 : 764-767.Improved Algorithm of Local Characteristic-Scale DecompositionSHENG Pei1, CUI Weicheng1, YANG Yongbin2, XU Aiqiang1(1. Naval Aviation University, Yantai Shandong 264001, China;2. The 91291s1 Unit of PLA, Sanya Hainan 572000, China)Abstract: An improved algorithm was proposed focusing on the problem of envelope curves estimated without considering convexity when local characteristic-scale decomposition was used. Firstly, the cubic spline interpolation method was used to estimate the mean points. Secondly, the implementation steps of the improved algorithm were given by referring to the standard algorithm. Finally, the effectiveness was illustrated by simulation examples.Key words ：local characteristic-scale decomposition; empirical mode decomposition; local mean decomposition简讯：海军航空大学学员勇夺2019年全国大学生物联网设计竞赛桂冠8月23日，2019年全国大学生物联网设计竞赛(华为杯)全国总决赛在四川大学落下帷幕，海军航空大学岸 防兵学院信息安全俱乐部研究生和本科生组成的2支参赛队伍摘得桂冠，双双荣获全国一等奖。

化学缩略词ab. absolute 绝对的addn. addition 添加alc. alcohol 醇alk. alkali 碱amt. amount 量A.P. analytically pure 分析纯app. apparatus 装置approx. approximate 大约aqu. aqueous 水的asym. asymmetric 不对称的atm. atmospheric 大气压av. average 平均的b.p. boiling point 沸点ca. about 大约cal. caloric 卡路里calc. calculate 计算cf. compare 比较chem. chemistry 化学conc. concentrated 浓缩的const. constant 常数contg. containing 含有…的compd. compound 化合物C.P. chemically pure 化学纯cryst. crystalline 晶体decomp. decompose 分解deriv. derivative 衍生detn. determination 测定dil. dilute 稀释的distd. distilled 蒸馏的e.g. for example 例如elec. electric 电的eq. equation 方程equil. equilibrium 平衡equiv. equivalent 等价的et. al. and others 以及其他人etc. et cetera 等等evap. evaporation 蒸发expt. experimental 实验的fig. figure 图hyd. hydrous 水的ibid. in the same place在同一地方lab. laboratory 实验室liq. liquid 液体L.R. laboratory reagent实验试剂manf. manufacture 制造max. maximum 最大的min. minute 最小的mixt. mixture 混合物mol.wt. molecular weight 分子量m.p. melting point 熔点org. organic 有机的ppm. parts per million百万分之一ppt. precipitated 沉淀的prep. prepare 制备resp. respectively 分别地sec. second 第二soln. solution 溶液solv. solvent 溶剂sp.gr. specific gravity 比重sq. square 平方sub. sublime 升华susp. suspended 悬浮地tech. technical 技术的Tech.P. technically pure 技术纯temp. temperature 温度vol. volume 体积wt. Weight 重量1.有机化合物的官能团和重要的基团官能团functional group双键double bond三键triple bond烃基hydroxy group琉基mercapto硫轻基sulfhydryl group羰基carbonyl group氨基amino group亚氨基imino group硝基nitro group亚硝基nitroso group氰基cyano group羧基carboxyl group磺基sulpho group烷基alkyl group苯基phenyl group卡基benzyl group芳基aryl group烯基allyl group烷氧基alkoxyl group酰基acyl group活性亚甲基active methylene group2.有机化合物的类型烃hydrocarbon石蜡paraffin脂肪烃aliphatic hydrocarbon烷烃alkane烯烃alkene炔烃alkyne共扼二烯烃conjugated diene脂环烃alicyclic hydrocarbon螺环化合物spiro compound桥环化合物bridged ring compound芳烃aromatic hydrocarbon非苯芳烃nonbenzenoid aromatic hydrocarbon稠环芳烃condensed aromatics 卤代烃halohydrocarbon醇alcohol酚phenol醚ether环氧化合物epoxide冠醚crown ether硫醇thiol硫酚thiophenol硫醚sulfide二硫化物disulfide亚磺酸sulfinic acid磺酸sulfonic acid亚砜sulfoxide砜sulfone醛aldehyde酮ketone半缩醛hemiacetaI半缩酮hemiketal缩醛acetal缩酮ketal西佛碱shiff's base肟oxime腙hydrozone缩氨脲semicarbazoneα,β-不饱和酮α,β--unsaturated ketone 醌quinone羧酸carboxylic acid酰卤acid halide酸酐acid anhydride酯ester酰胺amide内酯lactone内酰胺lactam月青nitrile取代酸substituted acid羟基酸hydroxy acid醇酸alcoholic acid酚酸phenolic acid酮酸keto acidB-酮酸酯B-ketone ester乙酰乙酸乙醋ethyl acetoacetate亚硝基化合物nitroso compoundA ampere安(培)Angstrom unit(s)埃(长度单位，10-10米)abs．absolute绝对的abs．EtOH absolute alcohol无水乙醇abstr．abstract文摘Ac acetyl(CH3CO，not CH3COO)乙酰基a c alternating current交流电(流)Ac．H．acetaldehyde 乙醛AcOH acetic acid乙酸Ac2O acetic anhydride乙酸酐AcOEt ethyl acetate乙酸乙酯AcONa乙酸钠add additive 附加物addn addition加成，添加addnl additional添加的alc．alcohol，alcoholic醇aliph．aliphatic 脂族的Al．Hg．Aluminum amalgam铝汞齐alk．alkaline(not alkali)碱性的alky alkalinity(alhys．for alkalinities is not approved)碱度，碱性am amyl(not ammonium)戊基amorph amorphous无定形的amp ampere(s)安(培)amt．amount(as a noun)数量anal．analysis分析anhyd．anhydrous无水的AO atomic orbital原子轨(道)函数app．apparatus仪器，装置approx approximate(as an adjective)，approximately近似的，大概的approxn approximation近似法，概算aq．aqueous水的，含水的arom．aromatic芳族的as．asymmetric不对称的assoc．associate(s)缔合assocd associated缔合的assocn association缔合at．atomic(not atom)原子的atm atmosphere(s)，atmospheric大气压=1.01325×105帕ATP adenosine triphosphatae三磷酸腺苷酶at．wt．atomic weight原子量av．average(except as a verb)平均b．(followed by a figure denoting temperature)boils at，boiling at(similarlyb13，at1.3mm，pressure)沸腾(后面的数字表示温度，同样b13表示在13毫米压力下沸腾)bbl barrel桶[液体量度单位=163.5升(英国)，=119升(美国)]BCC．body-centred cubic立方体心BeV or GeV billion electronvolts10亿电子伏，吉电子伏，109电子伏BOD biochemical oxygen demand生化需氧量μB Bohr magneton玻尔磁子[物]b．p．boiling point沸点Btu British thermal unit(s)英热单位=1055.06焦Bu butyl(normal)丁基bu．bushel蒲式耳=36.368升(英)=35.238升(美)Bz benzoyl(not benzyl)苯甲酰BzH benzaldehyde苯(甲)醛BzOH benzoic acid苯甲酸C concentration浓度Cal．calorie(s)千卡，大卡=4186.8焦cal．卡=4.1868焦calc．calculate计算calcd calculated计算的calcg calculating计算calcn calculation计算CC cubic centimeter(s)立方厘米CD circurlar dichroism圆二色性(物)c．d．current density电流密度cf．参见compare比较cubic feet per minute立方英尺/分钟(1立方英尺=2.831685×10-2米3)chem．chemical(as an adjective)(not chemistry nor chemically)化学的Ci curie居里(放射单位)=3.7×1010贝可clin．clinical(ly)临床的cm centimeter(s)厘米CoA coenzyme A辅酶AC．O．D．chemical oxygen demand化学需氧量coeff．coefficient系数col．colour，coloration颜色com．commercial工业的，商业的，商品的comb．combustion燃烧compb．compound化合物，复合物compn．composition组成，成分conc．concentrate(as a verb)提浓，浓缩concd．concentrated浓的concg．concentrating浓缩(的) concn．concentration浓度cond conductivity导电率，传导性const．constant常数，常量contg containing包含，含有cor corrected校正的，改正的，正确的cp．constant pressure恒压C．P．Chemically pure化学纯的crit．critical临界的cryst．crystalline(not crystallize)结晶crystd crystallized使结晶crystg crystallizing结晶crystn crystallization结晶，结晶化cu．m．cubic meter(s)立方米Cv constant volume恒容d density密度(d13 相对于水在4℃时的比重；d2020相对于水在20℃时的比重)D Debye unit德拜单位，电偶极矩单位d．dextrorotatory右旋(不译)dl-外消旋(不译)d．c．direct current直流电decomp．decompose(s)分解decompd decomposed分解的decompg decomposing分解decompn decomposition分解degrdn degradation降解deriv．derivative衍生物，导数(数)det．determine 测定detd determined 测定的detg determining测定detn determination 测定diam．diameter直径dil．dilute稀释，冲淡dild diluted稀释的diltg diluting稀释diln dilution稀释diss．dissolves，dissolved溶解dissoc dissociate(s)离解dissocd．dissociated 离解的dissocn dissociation 离解dist．distil．distillation 蒸馏distd distilled蒸馏的distg distilling 蒸馏distn distillation蒸馏dl分升dm．decimeter(s)分米DMF dimetbylformamide二甲基甲酰胺DNase deoxyribonuclease脱氧核糖核酸酶d．p．degree of polymerization聚合度dpm disintegrations per minute分解量/分钟DTA differential thermal analysis 差热分析E．D．effective dose有效剂量EEG electroencephalogram脑电流描记术e．g．for example例如elec electric，electrical(not electrically)电的e．m．f．electromoctive force电动势e．m．u．electromagnetic unit电磁单位en．ethylenediamine(used in formulas only)乙二胺equil equilibrium(s)平衡equiv．equivalent当量，克当量esp．especially 特别，格外est．estimate(as a verb)估计estd estimated估计的estg estimating估计estn estimation估计Et ethyl乙基Et2O ethyl ether乙醚ηviscosity粘度eV electron volt(s)电子伏[特] evac．evacuated抽空的evap．evaporate蒸发evapd evaporated 蒸发的evapg evaporating蒸发evapn evaporation蒸发examd examined检验过的，试验过的examg examining检验，试验examn examination检验，试验expt．experiment(as a noun)实验exptl experimental实验的ext．extract提取物，萃，提取extd extracted提取的extg extracting提取extn extraction 提取F farad法[拉](电容)fcc face centered cubic面心立方体fermn fermentation发酵f．p．freezing point冰点，凝固点FSH follicle-stimulating hormone促卵泡激素ft．foot，feet 英尺=0.3048米ft-lb foot-pound 英尺磅=0.3048米×0.453592千克g.gram(s)克gal gallon加仑=4.546092升(英)=3.78543升(美) geol.geological地质的gr.grain(weight unit)谷(1谷=1/7000磅=0.64799克) h hour小时H henry亨[利]ha.hectare(s)公顷=6.451600×10-4米2homo-均匀-，单相h hour小时hyd.hydrolysis，hydrolysed水解Hz hertz(cycles/sec)赫[兹]，周/秒ID infective dose无效剂量in.inch(es)英寸=0.0254米inorg.incrganic无机的insol.insoluble不溶的IR infrared红外线irradn irradiation照射iso-Bu，isobutyl异丁基iso-Pr，isopropyl异丙基IU国际单位J joule焦[耳](能量单位)K kelvin开[尔文]，绝对温度Kcal.kilocalorie(s)千卡=418.6焦kg kilogram(s)千克kV kilovolt(s)千伏kV-amp.kilovolt-ampere(s)千伏安kW.kilowatt(s)千瓦kWh kilowatthour 千瓦小时=3.6×106焦l.liter(s)升boratory实验室lb pound(s)磅=0.453592千克LCAO linear combination of atomic orbitals原子轨道的线性组合LD Lethal dose致死剂量LH Luteinizing hormone促黄体发生激素liq.liquid液体，液态Lm lumen流明(光通量单位)LX lux勒[克斯](照度单位)m.meter(s)；also(followed by a figure denoting temperature)米，熔融(注明温度时)M.mega-(106)兆M molar(as applied to concn.)摩尔m.melts at，melting at熔融m molal摩尔的ma milliampere(s)毫安manuf.manufacture制造manufd manufactured制造的manufg.manufacturing制造math.mathematical数学的max maximum(s)最大值，最大的Me methyl(MeOH,methanol)甲基mech.mechanical机械的metab.metabolism新陈代谢m.e.v million electron volts兆电子伏mg milligram(s)毫克mi mile英里=1609.344米min minimun[also minute(s)]最小值，最小的min minute分钟misc miscellaneous其它mixt.mixture混合物ml milliliter(s)毫升mm millimeter(s)毫米nm millimicron(s)纳米MO molecular orbital分子轨道函数mol molecule，molecular分子，分子的mol.wt.molecular weight分子量m.p.melting point熔点mph miles per hour英里(=1609.344米)/小时μmicron(s)微米mV millivolt(s)毫伏N newton牛[顿](力的单位)N normal(as applied to concn.)当量(浓度)neg.negative(as an adjective)阴性的，负的no number号，数obsd observed观察，观测anic有机的oxidn oxidation氧化oz．ounce盎司(常衡=28.349523克)P．d．potential difference势差，电位差Pet．Et．petroleum ether石油醚Ph．phenyl苯基phys．physical物理的physiol．physiological生理学的p．m．post meridiem午后polymd polymerized聚合polymg polymerizing聚合ploymn polymerization聚合pos．positive(as an adjective)阳性的，正的powd．powdered粉末的，粉状的p．p.b．(ppb)parts per billion亿万分之(几) p.p.m．(ppm)parts per million百万分之(几) ppt．precipitate沉淀，沉淀物pptd．precipitated沉淀出的pptg．precipitating沉淀pptn precipitation沉淀Pr propyl (normal)丙基prac．practically实际上prep．prepare制备press．pressure压力prepd prepared制备的prepg preparing制备prepn preparation制备psi pounds per square inch磅/英寸2[=0.453592千克/(6．45100×10-4米2)]psia pounds per square inch alsolute磅/英寸2(绝对压力) pt pint品脱(=0.5682615升)purifn purification精制py pyridine(used only in formulas)吡啶qt．quality质量qual.qualitative(not qualitatively)定性的quant．quantitative(not quantitatively)定量的γ希文，消旋(不译)red．reduce，还原red reduction还原，减小ref．reference 参考文献rem roentgen equivalent man人体伦琴当量，雷姆rep roentgen equivalent physical物理伦琴当量repr.reproduction再生产，再生res.resolution分辨，分解，离析resp.respectively分别地rpm revolution per minute每分钟转数RNase ribonuclease核糖核酸酶sapon.saponification皂化sapond saponified皂化过的sapong saponifying皂化sat.saturate使饱和satd.saturated饱和的satg saturating饱和的satn.saturation饱和，饱和度sec second(s)秒，仲，第二的sep.separate分离sepd separated分离出的sepg separating分离的sepn separation分离sol.soluble可溶的soln solution溶液soly solubility(solys.for solubilities is not approved)可溶性，溶解度sp.gr.specific gravity比重sp.ht.specific heat比热sp.vol.specific volume比容std. standard标准suppl. supplement补篇sym. symmetrical对称的tech. technical技术的temp. temperature温度tert. Tertiary叔(指CH3…C(CH3)2—型烃基) thermodyn. Thermodynamics热力学titrn titration滴定unsym. unsymmetrical偏，不对称U. V. ultraviolet紫外线V volt(s)伏[特]vac.vacuun真空vapor vaporization汽化vol.volume (not volatile)体积vs versus对W.watt(s)瓦[特]wt.weight重量wk week星期。

Listing Aabsolute abs. abstract abstr. #addition addn. additional(ly) addnl. adenosine 5'-diphosphate ADP adenosine 5'-monophosphate AMP adenosine triphosphatase ATPase adenosine 5'-triphosphate ATP adrenocorticotropin ACTH #alcohol(ic) alc. #aliphatic aliph. alkaline alk. alkalinity* alky. all valence electron AVE alternating current a.c. amount amt. ampere (unit) A #analysis* anal. #analytical(ly) anal. angstrom unit Å**anhydrous anhyd. apparatus app. approximate(ly) approx. approximation approxn. #aqueous aq. #aromatic arom. associate assoc. associated assocd. associating assocg. #association assocn. asymmetric(al)(ly) asym. atmosphere (unit) atm. atmospheric atm. #atomic at. atomic mass unit amu atomic orbital AO augmented plane wave APW average av. Bardeen-Cooper-Schrieffer BCS barrel (unit) bbl becquerel (unit) Bq billion electron volts (unit) GeV biochemical oxygen demand BOD body centered cubic bcc.Bohr magneton (unit) mB** boiling point b.p. British thermal unit Btu bushel (unit) bu butyl (normal) Bu #calculate calc. #calculated calcd. #calculating calcg. #calculation calcn. calorie (unit) cal carboxymethyl cellulose CM- cellulose #chemical(ly) chem. chemical oxygen demand COD chemically pure CP #chemistry chem. circular dichroism CD #clinical(ly) clin. coefficient coeff. #coenzyme A CoA coherent potential approximation CPA commercial(ly) com. complete neglect ofdifferential overlap CNDO composition compn.compound compd. concanavalin A ConA #concentrate conc. #concentrated concd. #concentrating concg. #concentration concn. #conductivity* cond. configuration interaction CI constant const. containing contg. corrected cor. coulomb (unit) C coupled electron pair approximation CEPA #critical crit. #crystalline cryst. #crystallization crystn. #crystallized crystd. #crystallizing crystg. cubic feet per minute (unit) cfm curie (unit) Ci current density c.d. cyclic AMP cAMP cyclic GMP cGMP cytidine 5'-diphosphate CDPcytidine 5'-monophosphate CMP cytidine 5'-triphosphate CTP debye unit D decompose decomp. decomposed decompd. decomposing decompg. #decomposition decompn. #degradation degrdn. degree Celsius centigrade (unit) °C** degree Fahrenheit (unit) °F** degree Kelvin °K degree of polymerization d.p. density d. deoxyribonuclease DNase deoxyribonucleic acid DNA derivative deriv. #determination detn. determine det. #determined detd. determining detg. diameter diam. diatomics-in-molecules DIM diethylaminoethyl cellulose DEAE-cellulose differential thermal analysis DTAdilute dil. diluted dild. diluting dilg. dilution diln. dimethylformamide DMF diphosphopyridine nucleotide NAD direct current d.c. disintegrations per minute (unit) dpm dissociate dissoc. dissociated dissocd. dissociating dissocg. #dissociation dissocn. #distillation distn. #distilled distd. #distilling distg. effective dose ED #electric(al)(ly) elec. electrocardiogram ECG electroencephalogram EEG electromagnetic unit emu electromotive force emf. electron paramagnetic resonance ESR electron spin resonance ESR electron volt (unit) eVelectrostatic unit esu enzyme-linked immunosorbent assay ELISA #equilibrium(s) equil. equivalent (unit) equiv. #equivalent equiv. especially esp. estimate est. estimated estd. estimating estg. estimation estn. ethyl Et ethylenediaminetetraacetic acid EDTA evaporate evap. evaporated evapd. evaporating evapg. evaporation evapn. #examination examn. #examined examd. #examining examg. experiment expt. experimental(ly) exptl. extended Hueckel molecular orbital EHMO extract ext. extracted extd.extracting extg. #extraction extn. face centered cubic fcc. farad (unit) F fermentation fermn. flavin adenine dinucleotide FAD flavin mononucleotide FMN floating spherical Gaussian orbital FSGO foot (unit) ft foot-pound (unit) ft-lb follicle-stimulating hormone FSH freezing point f.p. gallon (unit) gal gauss (unit) G Gaussian-type orbital GTO gram (unit) g gravitational constant g gray (absorbed radiation dose) (unit) Gy guanosine 5'-diphosphate GDP guanosine 5'-monophosphate GMP guanosine 5'-triphosphate GTP hectare ha henry H #hemoglobin Hbhertz (cycles/sec) (unit) Hz hexagonal close-packed hcp. highest occupied molecular orbital HOMO hour (unit) h Hueckel molecular orbital HMO hundredweight (unit) cwt hydrogenic atoms in molecules HAM immunoglobulin Ig inch (unit) in. independent electron pair approximation IEPA infrared IR inhibitory dose ID inosine 5'-diphosphate IDP inosine 5'-monophosphate IMP inosine 5'-triphosphate ITP intermediate neglect of differential overlap INDO international unit IU interstitial cell-stimulating hormone ICSH intramuscular(ly) i.m. intraperitoneal(ly) i.p. intravenous(ly) i.v. #irradiation irradn. iterative extended Hueckel molecularorbital IEHMOjoule (unit) J kelvin (unit) K laboratory lab. lethal dose LD linear combination of atomic orbitals LCAO linear combination of fragmentconfiguration LCFC liquid liq. liter (unit) L low energy electron diffraction LEED lowest unoccupied molecular orbital LUMO lumen (unit) lm luteinizing hormone LH lux (unit) lx magnetohydrodynamics MHD manufacture manuf. manufactured manufd. manufacturing manufg. mathematical(ly) math. maximum(s) max. maxwell (unit) Mx #mechanical(ly) mech. melanocyte-stimulating hormone MSH melting at m.melting point m.p. melts at m. messenger RNA mRNA metabolism metab. meter (unit) m methyl Me mile (unit) mi miles per hour (unit) mph minimum(s) min. minute (unit) min miscellaneous misc. #mixture mixt. modified neglect of diatomic overlap MNDO molal (unit) m molar (unit) M mole (unit) mol #molecular mol. molecular orbital MO #molecule mol. molecules-in-molecules MIM month (unit) mo. multiconfigurational self-consistentfield MC-SCF #negative(ly) neg.neglect of diatomic differential overlap NDDO neglect of nonbonded differentialoverlap NNDO nicotinamide adenine dinucleotide NAD nicotinamide adenine dinucleotidephosphate NADP nicotinamide mononucleotide NMN nuclear magnetic resonance NMR nuclear quadrupole resonance NQR number no. observed obsd. oersted (unit) Oe ohm (unit) Ω ** optical rotatory dispersion ORD #organic org. ounce (unit) oz #oxidation oxidn. Pariser-Parr-Pople PPP partial neglect of differential overlap PNDO parts per billion (unit) ppb parts per million (unit) ppm pascal (unit) Pa perturbational molecular orbital PMO phenyl Ph#physical(ly) phys. pint (unit) pt poise (unit) P #polymerization polymn. #polymerized polymd. #polymerizing polymg. #positive(ly) pos. potential difference p.d. pound (unit) lb pounds per square inch (unit) psi pounds per square inch absolute (unit) psia pounds per square inch gage (unit) psig #powdered powd. #precipitate ppt. #precipitated pptd. #precipitating pptg. #precipitation pptn. preparation prepn. prepare prep. prepared prepd. preparing prepg. #production prodn. propyl (normal) Pr #purification purifn.qualitative(ly) qual. #quantitative(ly) quant. quart (unit) qt radioimmunoassay RIA random phase approximation RPA #reduction redn. reference ref. reflection high energy electrondiffraction RHEED reproduction reprodn. resolution resoln. #respective(ly) resp. respiratory quotient RQ restricted Hartree-Fock RHF revolutions per minute (unit) rpm ribonuclease RNase ribonucleic acid RNA ribosomal RNA rRNA roentgen (unit) R roentgen equivalent man (unit) rem roentgen equivalentphysical (unit) rep saponification sapon. saponified sapond.saponifying sapong. saturate sat. #saturated satd. saturated calomel electrode SCE saturating satg. #saturation satn. scanning electron microscopy SEM second (unit) s self-consistent field SCF separate(ly) sep. separated sepd. separating sepg. separation sepn. siemens (unit) S Slater-type orbital STO #solubility* soly. #soluble sol. #solution soln. specific gravity sp. gr. specific volume sp. vol. specific weight sp. wt. standard std. steradian (unit) sr stokes (unit) Stsubcutaneous(ly) s.c. #symmetric(al)(ly) sym. tablespoon (unit) tbs teaspoon (unit) tsp #technical(ly) tech. temperature temp. tesla (unit) T tetrahydrofuran THF theoretical(ly) theor. thermodynamic(s) thermodn. thyroid-stimulating hormone TSH titration titrn. triethylaminoethyl cellulose TEAE-cellulose triphosphopyridine nucleotide NADP ultraviolet UV United States Pharmacopeia USP unrestricted Hartree-Fock UHF uridine 5'-diphosphate UDP uridine 5'-monophosphate UMP uridine 5'-triphosphate UTS UV photoelectron spectroscopy UPS volt (unit) V volume vol. watt (unit) Wweber (unit) Wbweek (unit) wkweight wt.x-ray photoelectron spectroscopy XPSyard (unit) ydyear (unit) yrzero differential overlap ZDO* Terms whose plurals are not automatically abbreviated by adding "s."# If prefixed, these words are generally abbreviated. For examples, see the list of "Abbreviations for some common prefixed terms" which precedes Listing A.** For representation in computer-readable files see "Listing of Characters and Symbols in Computer-Readable Files."Listing BA ampere (unit)Å angstrom (unit)abs. absoluteabstr. abstracta.c. alternating currentACTH adrenocorticotropinaddn. additionaddnl. additional(ly)ADP adenosine 5'-diphosphatealc. alcohol(ic)aliph. aliphaticalk. alkalinealky. alkalinityAMP adenosine 5'-monophosphate amt. amountamu atomic mass unitanal. analysis, analytical(ly) anhyd. anhydrousAO atomic orbitalapp. apparatusapprox. approximate(ly)approxn. approximationAPW augmented plane waveaq. aqueousarom. aromaticassoc. associateassocd. associatedassocg. associatingassocn. associationasym. asymmetric(al)(ly)at. atomicatm atmosphere (unit)atm. atmosphericATP adenosine 5'-triphosphateATPase adenosine triphosphataseav. averageAVE all valence electronbbl barrel (unit)bcc. body centered cubicBCS bardeen-Cooper-SchriefferBOD biochemical oxygen demandb.p. boiling pointBq becquerel (unit)Btu British thermal unitBu butyl (normal)bu bushel (unit)mB Bohr magneton (unit)C coulomb (unit)°C degree Ce lsius (centigrade) (unit) cal calorie (unit)calc. calculatecalcd. calculatedcalcg. calculatingcalcn. calculationcAMP cyclic AMPc.d. current densityCD circular dichroismCDP cytidine 5'-diphosphateCEPA coupled electron pair approximationcfm cubic feet per minute (unit)cGMP cyclic GMPchem. chemical(ly), chemistryCI configuration interactionCi curie (unit)clin. clinical(ly)CM-cellulose carboxymethyl celluloseCMP cytidine 5'-monophosphateCNDO complete neglect of differential overlap CoA coenzyme ACOD chemical oxygen demandcoeff. coefficientcom. commercial(ly)compd. compoundcompn. compositionConA concanavalin Aconc. concentrateconcd. concentratedconcg. concentratingconcn. concentrationcond. conductivityconst. constantcontg. containingCP chemically pureCPA coherent potential approximation crit. criticalcryst. crystallinecrystd. crystallizedcrystg. crystallizingcrystn. crystallizationCTP cytidine 5'-triphosphatecwt hundred weight (unit)D debye unitd. densityd.c. direct currentDEAE-cellulose diethylaminoethyl cellulose decomp. decomposedecompd. decomposeddecompg. decomposingdecompn. decompositiondegrdn. degradationderiv. derivativedet. determinedetd. determineddetg. determiningdetn. determinationdil. dilutedild. diluteddilg. dilutingdiln. dilutionDIM diatomics-in-moleculesdissoc. dissociatedissocd. dissociateddissocg. dissociatingdissocn. dissociationdistd. distilleddistg. distillingdistn. distillationDMF dimethylformamideDMSO dimethyl sulfoxideDNA deoxyribonucleic acidDNase deoxyribonucleased.p. degree of polymerizationdpm disintegrations per minute (unit) DTA differential thermal analysis ECG electrocardiogramED effective doseEDTA ethylenediaminetetraacetic acid EEG electroencephalogramEHMO extended Hueckel molecular orbital elec. electric(al)(ly)ELISA enzyme-linked immunosorbent assay emf. electromotive forceemu electromagnetic unitequil. equilibriumequiv equivalent (unit)equiv. equivalentesp. especiallyESR electron spin resonance,electron paramagnetic resonance est. estimateestd. estimatedestg. estimatingestn. estimationesu electrostatic unitEt ethyleV electron volt (unit)evap. evaporateevapd. evaporatedevapg. evaporatingevapn. evaporationexamd. examinedexamg. examiningexamn. examinationexpt. experimentexptl. experimental(ly)ext. extractextd. extractedextg. extractingextn. extractionF farad°F degree Fahrenheit (unit)FAD flavin adenine dinucleotidefcc. face centered cubicfermn. fermentationFMN flavin mononucleotidef.p. freezing pointFSGO floating spherical Gaussian orbital FSH follicle-stimulating hormoneft foot (unit)ft-lb foot-pound (unit)g gram (unit)g gravitational constantG gauss (unit)gal gallon (unit)GDP guanosine 5'-diphosphateGeV billion electron volts (unit)GMP guanosine 5'-monophosphateGTO Gaussian-type orbitalGTP guanosine 5'-triphosphateGy gray (absorbed radiation dose) (unit)h hour (unit)H henry (unit)ha hectare (unit)HAM hydrogenic atoms in moleculesHb hemoglobinhcp hexagonal close-packedHMO Hueckel molecular orbitalHOMO highest occupied molecular orbitalHz hertz (cycles/sec) (unit)ICSH interstitial cell-stimulating hormoneID inhibitory doseIDP inosine 5'-diphosphateIEHMO iterative extended Hueckel molecular orbital IEPA independent electron pair approximationIg immunoglobulini.m. intramuscular(ly)IMP inosine 5'-monophosphatein. inch (unit)INDO intermediate neglect of differential overlap i.p. intraperitoneal(ly)IR infraredirradn. irradiationITP inosine 5'-triphosphateIU international uniti.v. intravenous(ly)J joule (unit)K kelvin (unit)L liter (unit)lab. laboratorylb pound (unit)LCAO linear combination of atomic orbitalsLCFC linear combination of fragment configuration LD lethal doseLEED low energy electron diffractionLH luteinizing hormoneliq. liquidlm lumen (unit)LUMO lowest unoccupied molecular orbitallx lux (unit)m meter (unit)m molal (unit)M molar (unit)m. melts at, melting atmanuf. manufacturemanufd. manufacturedmanufg. manufacturingmath. mathematical(ly)max. maximumMC-SCF multiconfigurational self-consistent field Me methyl (not metal)mech. mechanical(ly) (not mechanism)metab. metabolismMHD magnetohydrodynamicsmi mile (unit)MIM molecules-in-moleculesmin minute (unit)min. minimum(s)misc. miscellaneousmixt. mixtureMNDO modified neglect of diatomic overlapmo month (unit)MO molecular orbitalmol mole (unit)mol. molecule, molecularm.p. melting pointmph miles per hour (unit)mRNA messenger RNAMSH melanocyte-stimulating hormoneMx maxwell (unit)NAD nicotinamide adenine dinucleotideNADP nicotinamide adenine dinucleotide phosphate NDDO neglect of diatomic differential overlap neg. negative(ly)NMN nicotinamide mononucleotideNMR nuclear magnetic resonanceNNDO neglect of nonbonded differential overlap no. numberNQR nuclear quadruple resonanceobsd. observedOe oersted (unit)Ω ohm (unit)ORD optical rotatory dispersionorg. organicoxidn. oxidationoz ounceP poise (unit)Pa pascal (unit)p.d. potential differencePh phenylphys. physical(ly)PMO perturbational molecular orbitalPNDO partial neglect of differential overlappolymd. polymerizedpolymg. polymerizingpolymn. polymerizationpos. positive(ly)powd. powderedppb parts per billion (unit)ppm parts per million (unit)PPP Pariser-Parr-Popleppt. precipitatepptd. precipitatedpptg. precipitatingpptn. precipitationPr propyl (normal)prep. prepareprepd. preparedprepg. preparingprepn. preparationprodn. productionpsi pounds per square inch (unit)psia pounds per square inch absolute (unit) psig pounds per square inch gage (unit)pt pint (unit)purifn. purificationqt quart (unit)qual. qualitative(ly)quant. quantitative(ly)R roentgen (unit)redn. reductionref. referencerem roentgen equivalent man (unit)rep roentgen equivalent physical (unit) reprodn. reproductionresoln. resolutionresp. respective(ly)RHEED reflection high energy electron diffraction RHF restricted Hartree-FockRNA ribonucleic acidRNase ribonucleaseRPA random phase approximationrRNA ribosomal RNArpm revolutions per minute (unit)RQ respiratory quotients second (unit)S siemens (unit)sapon. saponificationsapond. saponifiedsapong. saponifyingsat. saturatesatd. saturatedsatg. saturatingsatn. saturations.c. subcutaneous(ly)SCE saturated calomel electrode SCF self-consistent fieldSEM scanning electron microscopy sep. separate(ly)sepd. separatedsepg. separatingsepn. separationsol. solublesoln. solutionsoly. solubilitysp. gr. specific gravitysp. vol. specific volumesp. wt. specific weightsr steradian (unit)St stokes (unit)std. standardSTO Slater-type orbitalsym. symmetric(al)(ly)T tesla (unit)tbs tablespoon (unit)TEAE-cellulose triethylaminoethyl cellulose tech. technical(ly)temp. temperaturetheor. theoretical(ly)thermodn. thermodynamic(s)THF tetrahydrofurantitrn. titrationTSH thyroid-stimulating hormonetsp teaspoon (unit)UDP uridine 5'-diphosphateUHF unrestricted Hartree-FockUMP uridine 5'-monophosphateUPS UV photoelectron spectroscopy USP United States PharmacopeiaUTP uridine 5'-triphosphateUV ultravioletV volt (unit)vol. volumeW watt (unit)Wb weber (unit)wk week (unit)wt. weightXPS x-ray photoelectron spectroscopy yd yard (unit)yr year (unit)ZDO zero differential overlap。

Decomposition in multi-component AlCoCrCuFeNi high-entropy alloyS.Singh a ,N.Wanderka a ,⇑,B.S.Murty b ,U.Glatzel c ,J.Banhart aaHelmholtz Centre Berlin for Materials and Energy,Hahn-Meitner-Platz,14109Berlin,GermanybDepartment of Metallurgical and Materials Engineering,Indian Institute of Technology Madras,Chennai 36,IndiacMetallic and Alloys,University Bayreuth,Ludwig-Thoma Str.36b,95447Bayreuth,GermanyReceived 29March 2010;received in revised form 9September 2010;accepted 14September 2010Available online 14October 2010AbstractThe decomposition of an equiatomic AlCoCrCuFeNi high-entropy alloy produced by splat quenching and casting was investigated by the analytical high resolution methods:transmission electron microscopy and three-dimensional atom probe.It could be shown that splat-quenched alloy consisted of an imperfectly ordered body-centred cubic phase with a domain-like structure,whereas normally cast alloy formed several phases of cubic crystal structure.The cast alloy decomposed into both dendrites and interdendrites.A detailed local compositional analysis carried out by atom probe within the dendrites revealed that the alloying elements in the Ni–Al-rich plates and Cr–Fe-rich interplates are not randomly distributed,but segregate and form areas with pronounced compositional fluctuations.Cu-rich precipitates of different morphologies (plate-like,spherical and rhombohedron-shaped)could also be found in the dendrites.The results are discussed in terms of segregation processes governed by the enthalpies of mixing of the binary systems.Ó2010Acta Materialia Inc.Published by Elsevier Ltd.All rights reserved.Keywords:High-entropy alloys;TEM;EDX microanalysis;Three-dimensional atom probe measurements1.IntroductionThe recently developed high-entropy (HE)alloys belong to a class of metallic materials introduced in Ref.[1,2].Such an alloy has been defined by Yeh as an alloy with at least five principal elements and equiatomic or near-equiatomic composition [3,4].Small atomic size differences and near-zero values of the absolute enthalpy of mixing facilitate the formation of solid solutions for equiatomic alloys [5].HE alloys can exhibit high hardness [3]and excellent resistance to softening,even at 800°C [6,7],which makes them candidate materials for many potential appli-cations.Although knowledge of the microstructure and phase composition of a material is essential for the under-standing and prediction of its macroscopic mechanical properties,only a few studies have been carried out on the microstructure of HE alloys [4,8,9].It was reported that,after casting,HE alloys tend to form solid solutionphases,mainly of face-centred cubic (fcc)or body-centred cubic (bcc)structure [4,8].The number of the phases formed and their structure depend on the alloying elements in HE alloys synthesized under identical processing conditions [8].For quinary alloys such as CoCrCuFeNi,the formation of only one solid solution,namely a fcc phase was observed [8].In con-trast,the six-element alloy AlCoCrCuFeNi forms both fcc and bcc phases with a dendritic microstructure [8].Scan-ning electron microscopy (SEM)and transmission electron microscopy (TEM)investigations of the dendrites in AlCo-CrCuFeNi alloy reveal a modulated plate structure which was assumed to be formed as a result of spinodal decompo-sition [3].The modulated plates are oriented along the h 100i directions of the matrix and are composed of ordered bcc plates (B2structure)and disordered bcc inter-plates with the same lattice parameter of 2.89A˚,but of dif-ferent chemical compositions [3,8].Within the plates,small nanometre-sized (7–50nm)Cu-rich precipitates of fcc structure were also identified [8].Recently,two phases enriched in Cr–Fe and Ni–Al have been observed in the1359-6454/$36.00Ó2010Acta Materialia Inc.Published by Elsevier Ltd.All rights reserved.doi:10.1016/j.actamat.2010.09.023⇑Corresponding author.Tel.:+4913080622079/2730/2774.E-mail address:wanderka@helmholtz-berlin.de (N.Wanderka)./locate/actamatActa Materialia 59(2011)182–190dendrites[9]upon decomposition.However,thesition of multi-component AlCoCrCuFeNi HEnot been studied so far and needs furtherThe present investigation is focused on the decomposition behaviour of aCrCuFeNi HE alloy synthesized by the twocessing conditions splat quenching andThe splat-quenched and as-cast AlCoCrCuFeNiwas investigated using X-ray diffractionand three-dimensional atom probe(3D-AP).Inthe3D-AP measurements were used to map the distribution of alloying elements in as-cast HE2.Experimental detailsAluminium,cobalt,chromium,copper,iron99.999%purity were used as raw materials foralloy.This alloy was manufactured in thetory of the Helmholtz-Zentrum Berlin,Germany. component AlCoCrCuFeNi alloy was producedthe constituent elements in an equiatomic ratio intion levitation furnace.The furnace was10.8kW,with a frequency of200kHz.Meltingwere performed in a pure argon atmosphere in acible.Repeated melting was carried out at leastimprove the chemical homogeneity of the alloy.were solidified inflowing argon,which providedrate of10–20K sÀ1.The solidified ingots were diameter and20mm thick,and were cut into2mm thick for characterization.Splats ofalloy were produced in vacuum(10À6mbar)in an electro-magnetic levitation chamber.The splat quenching device allowed us to reach cooling rates of the order of106–107K sÀ1.The splats produced were40l m thick.The splat-quenched and as-cast materials were charac-terized by different techniques such as XRD,SEM,TEM and3D-AP.Thin pieces of both the splats and the as-cast ingots were mechanically polished at the end with1l m dia-mond paper for XRD.XRD spectra were measured with Cu K a radiation in the h–2h configuration using a Bruker AXS,D8diffractometer.Thin-foil specimens suitable for TEM observations were prepared by mechanical thinning down to a thickness of20l m followed by Ar-ion milling. The microstructure of the samples was characterized by TEM in a Philips CM30operated at300kV equipped with an energy dispersive X-ray(EDX)spectrometer.The beam size in the EDX microanalysis measurements was usually 10nm.Thin0.2Â0.2Â10mm3long rods were cut from the ingots for3D-AP measurements.Sharp tips were pre-pared from these rods by polishing in two steps:first by mechanical polishing down to2l m in diameter using 1l m diamond paper,then by a focused ion beam(FIB) provided by a Zeiss1540EsB cross beam microscope with both SEM and a FIB.Tips with a radius<50nm were milled by gallium ions with30keV energy and a beam cur-rent starting with2000pA andfinishing with50pA.The finally prepared sharp tips were checked using TEM,as shown in Fig.1.3D-AP analysis was performed in a vac-uum of10À7Pa at a temperature of$70K.A pulse frac-tion of20%with a pulse penetration frequency of 1000Hz was applied in the CAMECA atom probe.Many tips were investigated,from which three important results representative of the as-cast HE alloy are presented here.Nanohardness and indentation elastic modulus of the splat-quenched and as-cast HE alloys were measured using a Fischer Picodentor HM500.The measurements were car-ried out at a load of300mN for20s.For comparison,the microhardness of as-cast HE alloy was also measured using a Reichert-Jung MHT-10micro hardness tester,applying a load of890N for10s.3.ResultsFig.2shows XRD patterns of both splat-quenched and as-cast equiatomic AlCoCrCuFeNi HE alloy.In the splat-quenched material,XRD peaks corresponding to a bcc phase are observed.The lattice parameter of the bcc phase is2.87A˚.In contrast,in the as-cast alloy,Bragg peaks cor-responding to both fcc and bcc structures are observed. The intensity of the diffraction peaks of the bcc phase is much stronger than that of the fcc phase.The intensities of[011]for bcc and[111]for fcc reflections were used to estimate the volume fraction of the respective phases Bright-field TEM image of a sharp tip prepared fromsteps:mechanical polishing and subsequent sharpeningS.Singh etin the as-cast alloy.Taking into account that the scattering intensity of fcc lattice is four times that of the bcc lattice,it is found that the volume fraction of the bcc phase is higher than that of the fcc phase.The diffraction pattern at =43–44°(the magnified image in the inset in Fig. shows two peaks which correspond to two fcc phases named fcc1and fcc2,with different lattice parameters, A˚and3.62A˚,respectively.The bright-field TEM image of splat-quenched HE alloy Fig.3a shows the typical microstructure of a polycrystal-material with distinct grain boundaries and grain sizes 1.5l m.The dark-field TEM micrograph in Fig. imaged using the[001]superlattice reflection,reveals imperfectly ordered domain-like structure(bright contrast) some nanometres in diameter.The corresponding[1zone axis diffraction pattern(inset in Fig.3b)verifies the presence of an ordered bcc structure.Chemical analysis by EDX/TEM shows a near-uniform distribution of ele-ments close to an equiatomic composition(see Table1). Only the concentration of Al is less than that of other ele-ments,which is most probably due to its high absorption.Fig.4shows bright-field TEM micrographs of the as-cast HE alloy.This alloy solidifies dendritically(see Fig.4a).Several typical precipitate morphologies within the dendrites are shown in the TEM bright-field image in Fig.4b.The[001]zone axis diffraction pattern(inset in Fig.4b)displays the superlattice reflections generated by the plate-like precipitates of B2structure.Plate-like precip-itates with a dark contrast$50nm thick and200–500nm long are aligned along the h110i matrix direction.No additional spots in the selected area electron diffraction (SAED)image could be observed,and hence it was con-cluded that the plate-like precipitates are coherent with the bcc matrix.Faint streaks in the corresponding SAED pattern are visible,arising from the plate-like morphology of the precipitates.The lattice parameter of the bcc phase as measured by SAED is2.88A˚.Small and almost spheri-cal precipitates with size5–15nm are also present.Beside the plate-like and the spherical precipitates in the dendrites, some precipitates with a rhombohedron-shaped morphol-2.XRD patterns of both splat-quenched and as-cast equiatomicAlCoCrCuFeNi HE alloy.The splat-quenched HE alloy showspresence of a bcc phase(a bcc=2.87A˚),whereas the as-cast HEindicates the presence of one bcc(a bcc=2.87A˚)and two fcc phases.phases are clearly visible from two overlapping peaks shown incorresponding to the two{111}planes with slightly different latticeparameters(a fcc1=3.59A˚and a fcc2=3.62A˚).3.TEM images of the splat-quenched equiatomic AlCoCrCuFeNi HE alloy:(a)bright-field image showing polycrystalline structure; micrograph of microstructure imaged by[001]superlattice reflection shows regions with different contrast on the scale of a few corresponding diffraction pattern in the[100]zone axis indicates ordering in bcc phase(a bcc=2.87A˚).59(2011)182–190The[112]zone axis of the diffraction image clearly reveals that the interdendritic region belongs to the phase with fcc structure.Weak superlattice reflections corresponding to a L12structure are also visible.The lattice parameter of the interdendritic phase measured by SAED is$3.58A˚and is close to that of the fcc1phase measured by XRD(see Fig.2).The chemical compositions of phases,measured by EDX microanalysis in the TEM,are also given in Table1.Plates shown in Fig.4b are enriched in Al–Ni(A)or Ni (C),while interplates are enriched in Cr–Fe(B).Both the plate-like precipitates(D)and the spherical precipitates contain more than84at.%Cu.The plate-like precipitate (D),the rhombohedron-shaped precipitate(E)(fcc2)and the interdendritic region(F)(fcc1)are all Cu-rich,but on different compositions levels.The presence of fcc2(from XRD measurements)which corresponds to the lattice parameter of the Cu-rich rhombohedron-shaped precipi-tate(as determined by TEM)has not been reported previ-ously for AlCoCrCuFeNi alloy[4,8].The microchemical information on the precipitates and matrix of the dendrites of as-cast HE alloy was obtained by3D-AP tomography with nearly atomic resolution(Figs. 5–10).Fig.5displays a3D reconstruction of Al,Cr,Ni, Co,Fe and Cu atom positions in an analysed volume of 9.5Â9.5Â93nm3.All the alloying elements in the HE alloy are non-uniformly distributed and form three discrete regions.The compositions of these regions were deter-mined from concentration–depth profiles using a cylinder 1nm in radius oriented along the central line through the analysed volume.The concentration depth profiles in Fig.6demonstrate that region1is mainly enriched in Al and Ni.Region2is enriched in Cr and Fe,and region3 is largely enriched in Cu.Cobalt is homogeneously distrib-uted in regions1and2,but depleted in region3.The inter-face of the Cu-rich precipitate is very sharp and is$0.5nm wide.The transition interface between the Al–Ni-rich and Cr–Fe-rich region is comparably broad($7nm),as observed in the depth profile in Fig.6.In region1,a small Cu-rich precipitate can also be found.The concentration depth profiles of small($2nm in size)Cu-rich precipitate was reconstructed by taking a cylinder1nm in radius per-pendicular to one side of the precipitate/matrix interface (see Fig.7).The small precipitate was observed to contain >88at.%Cu(see also1a in Table1).The compositions of regions1(Al–Ni),2(Fe–Cr)and3(Cu-rich)can be identi-fied as regions marked in Fig.4as A,B and D,respectively.The compositionalfluctuations of elements in between two Cu-rich precipitates can be observed by concentration depth profiles taken along the axis of the cylinder1nm in radius in another investigated volume and are displayed in Fig.8.The cylinder cuts out a region with alternating enriched and depleted Al–Ni and Cr–Fe areas.The width of the Al–Ni enriched areas is$5and10nm,while the Cr–Fe-rich regions are20and35nm thick.The composi-tion of Co in both areas is almost the same.However, Cr,Fe and Co are not uniformly distributed in Cr–Fe-rich areas.Moreover,compositionalfluctuations of Cr are observed that are anti-correlated to both Fe and Co.The average chemical compositions of Al–Ni-rich and Cr–Fe-rich areas are similar to the corresponding areas observed in Fig.6and,therefore,they will not be additionally num-bered and included in Table1.Fig.9displays another example of3D-AP measure-ments.Three-dimensional reconstructions of Al,Cr,Ni, Co,Fe and Cu atom positions are shown in an analysed volume of12.6Â12.6Â134.2nm3.A general observation of thisfigure is that two discrete regions enriched and depleted in Cr are obtained.Fe is approximately uniformlyTable1Crystal structure and chemical composition(in at.%)of splat-quenched and as-cast HE alloys:the compositions of polycrystalline grains in the splat-quenched alloy and different phases marked in the as-cast alloy(regions A–F as marked in Fig.4)are measured by EDX/TEM;the lattice parameter of phases/precipitates was measured by SAED/TEM;the local concentrations in regions1–7(defined in Figs.6,7and10)are measured by3D-AP;the error in concentration is represented by the standard deviation2r.Symbols Regions enriched Crystal structureand lattice parameterAl Co Cr Cu Fe NiSplat-quenched Grains B2,2.87±0.02A˚13.1±317.7±316.2±318.7±317.6±316.7±3 As-cast DendriteA Al–Ni B2,2.88±0.02A˚25.8±317.9±3 3.0±310.4±312.1±330.8±3B Cr–Fe bcc,2.88±0.02A˚ 2.2±320±343.2±30.3±329±3 5.3±3C Ni B2,2.88±0.02A˚19.4±319.5±3 6.3±310±314.4±330.4±3D Plate precipitate B2,2.88±0.02A˚ 4.9±3 1.8±3 1.1±385±3 2.0±3 5.2±3E Rhombohedron-shaped phases L12,3.64±0.02A˚8.0±38.9±3 2.7±358.8±3 6.6±315±31Al–Ni29.7±1.518.4±1.3 2.5±0.58.6±0.912.7±1.128.1±1.5 1a Spherical precipitate 4.3±1.40.6±0.90.01±0.388.5±1.6 2.5±0.9 4.1±1.5 2Cr–Fe 2.8±0.618.8±1.239.6±1.4 1.3±0.431.3±1.4 6.2±0.5 3Plate precipitate7.8±0.7 1.9±0.40.5±0.183.5±1.0 1.1±0.2 5.2±0.6 4Al–Ni–Fe31.2±1.6 6.5±0.8 3.3±0.67.9±1.025.6±1.525.5±1.5 5Ni–Cr–Fe 1.5±1.0 4.9±1.828.4±3.74±1.429.3±3.931.9±3.7 6CrFe(NiCo) 6.3±0.910.5±1.121.9±1.5 4.9±0.823.4±1.533±1.7 7Ni–Cr–Co–Fe7.4±0.721.1±1.119±1.0 5.5±0.622.8+1.124.2±1.2 Interdendritic regionF Cu L12,3.58±0.02A˚7±3 2.9±3 1.3±377.1±3 2.5±39.1±3S.Singh et al./Acta Materialia59(2011)182–190185distributed.All other elements show a heterogeneous distri-bution throughout the analysed volume.The depth of134nm in Fig.10is subdivided mainly into four regions.In order to keep the numbers of the regions (Table 1)con-sistent with the previously numbered regions,the numbers 4–7are given to the regions in Fig.10.Region 4is enriched in Al,Ni,Fe and depleted in Cr,Co and Cu.However,within this region,after a depth of $20nm,a small fluctu-ation in Cu is also observed which corresponds to the Cu-rich precipitate.Region 5is enriched in Ni,Cr and Fe.The core of this area contains equal amounts of Ni,Cr and Fe,while the boundary is enriched in Ni.Region 6exhibits a large gradient in Ni and Co concentrations,whereas Al,Fe,Co and Cu are homogeneously distributed.Accord-ingly,in region seven some fluctuation of Cr can be obtained,whereas the Ni,Fe,Al,Co and Cu are uniformly distributed.The local concentrations of regions 4–7are listed in Table 1.All regions whose chemical compositions deviate from the global composition of elements (16.67at.%)are listed in Table 1.The error bars for the chemical composition of the elements are represented by the standard deviation 2r ,determined by standard count-ing statistics.The phases formed under the processing con-ditions investigated in the present study namely splat-quenching (cooling rate 106–107K s À1)and normal casting (cooling rate 10–20K s À1)are summarized and presented schematically in Fig.11.The hardness and indentation elastic moduli of both the splat-quenched and as-cast HE alloys are given in Table 2.From this table,it is obvious that the hardness of splat-quenched (539HV)and as-cast (534HV)alloys is almost equal.However,the indentation elastic modulus of the as-cast material (182GPa)is markedly higher than that of the splat-quenched alloy (105GPa).Bright-field TEM images of as-cast equiatomic AlCoCrCuFeNi HE alloy:(a)dendrite and interdendritic regions;(b)microstructure plate-like precipitates oriented along the h 110i directions.The corresponding electron diffraction pattern of the [001]zone reflections of ordered bcc,B2structure (a bcc =2.88A˚).The characters A–D are positions of the EDX measurements;(c)rhombohedron-shaped precipitates (a fcc2=3.64A ˚).The corresponding electron diffraction pattern of the h 110i zone axis in the inset reflections corresponding to L12structure;(d)microstructure of the interdendritic region and corresponding electron diffraction showing weak superlattice reflections corresponding to L12structure (a fcc1=3.58A˚).Fig.5.Three-dimensional reconstruction of Al,Cr,Ni,Co,Fe and Cuatom positions in an analysed volume9.5Â9.5Â93nm3of as-castAlCoCrCuFeNi HE alloy.The symbols for different atoms are:Al,Co,Cr,Cu,Fe,and Ni.4.DiscussionThe successful preparation of a cubic bcc phase AlCo-CrCuFeNi HE alloy by rapid cooling(106–107K sÀ1)isreported in the present work for thefirst time.The highcooling rates during splat quenching prevent the growthof equilibrium phases,and hence metastable bcc phasecan be obtained as predicted in Ref.[10].However,theprofiles of all alloying elements in an as-cast AlCoCrCuFeNi HE alloy in an analysed volumeseparate regions1–3enriched in Al–Ni,Cr–Fe and Cu,respectively.The microstructural studies show that Cu is the element separates first and forms Cu-rich phases.The behav-of Cu can be explained by its strong repulsive interac-with the other elements and is not surprising,since the highest positive enthalpy of mixing with Fe,Cr,Ni [13].The composition of the Cu-rich plate-like cipitates as given in Table 1indicates that they contain mainly Cu (83–85at.%)and small amounts of Al (4–or depleted in Ni–Al or Cr–Fe (see Table 1,regions 1and 2).The wavelengths of these fluctuations (information from several 3D-AP measurements)range from 5to 45nm Figs.6and 8).Areas with similar compositions were also observed using TEM/EDX microanalysis (Table 1,regions A and B).However,the wavelengths of Ni–Al-and Cr–Fe-rich regions reported in the EDX line scan analysis [9]are much larger than the fluctuations obtained in the present work.This is because of the non-uniformity in size and dis-tribution of phases in as-cast material.The formation of the Ni–Al-rich phase can also be 7.Concentration depth profiles through a small Cu precipitate observed in the Al–Ni-rich area (Fig.6,region 1),namely region 1a.profiles of all alloying elements in an as-cast AlCoCrCuFeNi HE alloy in an analysed 9.5Âare visible.Cu-rich precipitates are located both at the right and left side of the measurement.Fig.9.Three-dimensional reconstruction of Al,Cr,Ni,Co,Fe and Cu atom positions in an analysed 12.6Â12.6Â134.2nm 3volume of as-cast AlCoCrCuFeNi HE alloy.The symbols used for different atoms are:Al ,Co ,Cr ,Cu ,Fe ,and Ni .isÀ22kJ molÀ1(see Table3)[13].However,this phase consists of large amounts of Co,Fe,Cu and Cr.Based on the local concentration analysis of the atom probe and on the data reported in the literature[15],it can be assumed that Fe prefers the Al sites,whereas Cr and Co prefer the Ni sites.Thus,the stoichiometry of the Ni–Al-rich phase could be(Ni28Co19Cr3)–(Al30Fe12Cu8).profiles of all alloying elements in an as-cast AlCoCrCuFeNi HE alloy(data set from Fig.9 Al–Ni–Fe,Ni–Cr–Fe,CrFe(NiCo)and Ni–Cr–Co–Fe,respectively.representation of phase segregation observed during solidification of AlCoCrCuFeNi HE alloy by two different rate106–107K sÀ1)and casting(cooling rate10–20K sÀ1).Table2Hardness and indentation elastic modulus values of the splat-quenchedand as-cast HE alloys.Materials Microhardness(HV)Nanohardness(HV)Elastic modulus(GPa)Splat-quenched539105 As-cast500534182Table3Enthalpy of mixing(kJ molÀ1)of binary systems containing the elements in AlCoCrCuFeNi as estimated in Ref.[13].D H mix!Elements#Ni Cr Al Co FeCu412À1613 NiÀ7À220À2 CrÀ10À4À1 AlÀ19À11 CoÀ1As shown in Table3,the enthalpy of mixing of a Cr–Fe binary system is justÀ1kJ molÀ1[13].Nevertheless,Fe and Cr jointly segregate into Cr–Fe-rich regions.The in-depth analysis of the atom probe data shows that Cr and Fefluctuations are anti-correlated(Figs.6and8).The amplitude of Crfluctuations in the Cr–Fe-rich regions ranges from32to47at.%,while its wavelength is$6nm (Fig.8).Thefluctuations of Cr and Fe in a Fe–Cr–Co-based alloy have been previously investigated in detail [16].Fe–Cr–Co-based alloys belong to the permanent mag-nets,and the magnetic hardening of this alloy was explained to be associated with the modulated structure formed by spinodal decomposition.The amplitude of ele-mentfluctuations in the present investigations is much lower than that reported in Ref.[16],but the same tendency of element correlation is obtained,pointing to a decompo-sition of the Cr–Fe phase in the as-cast alloy.Regions enriched in Al–Ni–Fe(Fig.10,region4)can also be explained by the negative enthalpy of mixing of Al–Ni and Al–Fe(À11kJ molÀ1)[13]binary systems, which is large enough for clustering.The composition of the Al–Ni–Fe phase(Table1)is similar to the composition of the ordered bcc(B2)phase recently reported in maraging steels[17].The Al–Ni–Fefluctuations obtained in this study could act as nuclei for the crystallization of interme-tallic phases during heat treatment.From the results obtained in the present study,it cannot be confirmed that all elements are expected to be randomly distributed in the crystal lattice in HE alloys,according to the statistically expected occupancy[18].However,it is shown that only high mixing entropy leads to the forma-tion of phases with cubic crystal structure in both splat-quenched and as-cast HE alloys.The mechanical properties of alloys sensitively depend on their microstructure.The elastic modulus measured for the as-cast HE alloy is much higher than that of its splat-quenched counterpart.This result is not unexpected, as the splat-quenched alloy exhibits an almost uniform dis-tribution of elements,whereas the as-cast alloy shows phase decomposition with a coarse microstructure.5.SummaryThe equiatomic AlCoCrCuFeNi HE alloy produced by splat quenching(cooling rate106–107K sÀ1)and normal casting into a crucible(cooling rate10–20K sÀ1)was inves-tigated by XRD,TEM and3D-AP.XRD indicates forma-tion of only one bcc phase in the splat-quenched alloy, whereas one bcc and two fcc(fcc1,fcc2)phases were detected in the cast alloy.However,the more detailed investigations by TEM showed that the splat-quenched alloy contained an imperfectly ordered bcc phase with domain-like structure a few nanometres in size.The as-cast alloy comprised several bcc and a fcc phases within the dendrites,namely a plate-like Cu-rich precipitate of B2 structure,rhombohedron-shaped Cu-rich precipitates of L12structure(fcc2),Al–Ni-rich plates of B2structure and Cr–Fe-rich interplates of bcc structure.The interden-drite was found to be Cu-rich phase of L12structure (fcc1).Further analysis of the local composition by atom probe indicated the presence of compositionfluctuations and segregation within the dendrite.HE alloy synthesized from elements with a large differences in their enthalpy of mixing such as Al–Ni,Cu–Ni and Fe–Cr are prone to seg-regation during solidification.AcknowledgmentThe authors are grateful to the German Research Foun-dation(DFG)for thefinancial support by Grants WA 1378/14-1and WA1378/15-1.References[1]Ranganathan S.Curr Sci2003;85:1404.[2]Yeh JW.Knowl Bridge2003;40:1.[3]Yeh JW,Chen SK,Lin SJ,Gan JY,Chin TS,Schun TT,et al.AdvEng Mater2004;6:299.[4]Tung CC,Yeh JW,Shun TT,Chen SK,Huang YS,Chen HC.MaterLett2007;61:1.[5]Zhang Y,Zhou YJ,Lin JP,Chen GL,Liaw PK.Adv Eng Mater2008;10:534.[6]Tong CJ,Chen MR,Chen SK,Yeh JW,Shun TT,Lin SJ,et al.Metall Mater Trans2005;36A:1263.[7]Yeh JW,Chen YL,Lin SJ,Chen SK.Mater Sci Forum2007;560:1.[8]Tong CJ,Chen YL,Chen SK,Yeh JW,Shun TT,Tsau CH,et al.Metall Mater Trans2005;36A:881.[9]Wang YP,Li BS,Fu HZ.Adv Eng Mater2009;11:641.[10]Duwez P,Willens RH,Klement W.J Appl Phys1960;31:1136.[11]Galenko P,Jou D.Physica A2009;388:3113.[12]Li A,Xi Zhang.Acta Metall Sinica2009;22:219.[13]Takeuchi A,Inoue A.Mater Trans2005;46:2817.[14]Raghavan V.J Phase Equilib Diff2006;27:389.[15]Wanderka N,Glatzel U.Mater Sci Eng A1995;203:69.[16]Zhu F,Wendt H,Haasen P.Scripta Metall1982;18:1175.[17]Ho¨ring S,Wanderka N,Banhart J.Ultramicroscopy2009;109:574.[18]Yeh JW,Chen SK,Gan JY,Lin SJ,Chin TS,Shun TT,et al.MetallMater Trans2004;35A:2533.190S.Singh et al./Acta Materialia59(2011)182–190。

This article was downloaded by: [112.83.53.115]On: 08 May 2014, At: 17:21Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UKMaterials Research LettersPublication details, including instructions for authors and subscription information:/loi/tmrl20High-Entropy Alloys: A Critical ReviewMing-Hung Tsai a & Jien-Wei Yeh ba Department of Materials Science and Engineering, National Chung Hsing University,T aichung 40227, T aiwanb Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu30013, T aiwanPublished online: 30 Apr 2014.PLEASE SCROLL DOWN FOR ARTICLEMater.Res.Lett.,2014 /10.1080/21663831.2014.912690/toc/tmrl20/currentHigh-Entropy Alloys:A Critical Review Ming-Hung Tsai a ∗and Jien-Wei Yeh b ∗aDepartment of Materials Science and Engineering,National Chung Hsing University,Taichung 40227,Taiwan;bDepartment of Materials Science and Engineering,National Tsing Hua University,Hsinchu 30013,Taiwan(Received 10February 2014;final form 2April 2014)Supplementary Material Available OnlineHigh-entropy alloys (HEAs)are alloys with five or more principal elements.Due to the distinct design concept,these alloys often exhibit unusual properties.Thus,there has been significant interest in these materials,leading to an emerging yet exciting new field.This paper briefly reviews some critical aspects of HEAs,including core effects,phases and crystal structures,mechanical properties,high-temperature properties,structural stabilities,and corrosion behaviors.Current challenges and important future directions are also pointed out.Keywords :High-Entropy Alloys,High-Entropy Materials,Alloy Design,Mechanical Properties,Corrosion Resistance1.Introduction Most conventional alloys are based on one principal element.Different kinds of alloying ele-ments are added to the principal element to improve its properties,forming an alloy family based on the principal element.For example,steel is based on Fe,and aluminum alloys are based on Al.However,the number of elements in the periodic table is limited,thus the alloy families we can develop are also limited.If we think outside the conventional box and design alloys not from one or two ‘base’elements,but from multiple elements altogether,what will we get?This new concept,first proposed in 1995,[1]has been named a high-entropy alloy (HEA).[2]HEAs are defined as alloys with five or more princi-pal elements.Each principal element should have a con-centration between 5and 35at.%.[2,3]Besides principal elements,HEAs can contain minor elements,each below 5at.%.These alloys are named ‘HEAs’because their liquid or random solid solution states have significantly higher mixing entropies than those in conventional alloys.Thus,the effect of entropy is much more pronounced in HEAs.Existing physical metallurgy knowledge and binary /ternary phase diagrams suggest that such multi-element alloys may develop several dozen kinds of phases and intermetallic compounds,resulting in complex and brittle microstructures that are difficult to analyze and engineer,and probably have very limited practical value.∗Correspondingauthor.Emails:mhtsai@.tw ;jwyeh@.twOpposite to these expectations,however,experimental results show that the higher mixing entropy in these alloys facilitates the formation of solid solution phases with simple structures and thus reduces the number of phases.Such characters,made available by the higher entropy,are of paramount importance to the development and appli-cation of these alloys.Therefore,these alloys were named as ‘high-entropy’alloys.Because of the unique multiprincipal element com-position,HEAs can possess special properties.These include high strength /hardness,outstanding wear resis-tance,exceptional high-temperature strength,good struc-tural stability,good corrosion and oxidation resistance.Some of these properties are not seen in conventional alloys,making HEAs attractive in many fields.The fact that it can be used at high temperatures broadens its spectrum of applications even further.Moreover,the fab-rication of HEAs does not require special processing techniques or equipment,which indicates that the mass production of HEAs can be easily implemented with existing equipment and technologies.More than 30ele-ments have been used to prepare more than 300reported HEAs,forming an exciting new field of metallic mate-rials.This paper reviews some crucial aspects of the field,including core effects,phase formation,mechani-cal properties,high-temperature properties,and corrosion©2014The Author(s).Published by Taylor &Francis.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (/licenses/by/3.0),which permits unrestricted use,distribution,and reproduction in any medium,provided the original work is properly cited.The moral rights of the named author(s)have been asserted.D o w n l o a d e d b y [112.83.53.115] a t 17:21 08 M a y 2014behaviors.It also points out current challenges and future directions.The physical (magnetic,electrical,and ther-mal)properties of HEAs have been reviewed elsewhere [4]and are not discussed in this paper.2.Core Effects [3,5]The multiprincipal-element character of HEAs leads to some important effects that are much less pronounced in conventional alloys.These can be considered as four ‘core effects’.This section briefly introduces and discusses the core effects.2.1.High-Entropy Effect.The high-entropy effect states that the higher mixing entropy (mainly configura-tional)in HEAs lowers the free energy of solid solution phases and facilitates their formation,particularly at higher temperatures.Due to this enhanced mutual solu-bility among constituent elements,the number of phases present in HEAs can be evidently reduced.According to G =H −TS (where G is the Gibbs free energy,H is enthalpy,T is temperature,and S is entropy)entropy can stabilize a phase with higher entropy,provided that temperature is sufficiently high.For example,the melting phenomenon of a pure metal is due to the higher entropy of the liquid state relative to the solid state (according to Richard’s rule,[6]at melting point the entropy difference between the two states roughly equals the gas constant R ).Similarly,among the various types of phases in an alloy,those with higher entropy may also be stabilized at high temperature.In conventional alloys,solid solu-tion phases (including terminal and intermediate solid solutions)have higher mixing entropy than intermetal-lic compounds do.For HEAs,the difference in entropy between solid solutions and compounds is particularly large owing to the multiprincipal-element design.For example,the configurational entropy of an equimolar quinary random solid solution is 1.61R .This means that the entropy difference between an equimolar quinary solution and a completely ordered phase (whose entropy is negligible)is about 60%larger than the entropy dif-ference of the melting case mentioned above (i.e.60%larger than the entropy difference between liquid and solid states of pure metals).Thus,it is highly probable that solid solution phases become the stable phase in HEAs at high temperature.It is expected that with the increase of tem-perature,the overall degree of order in HEAs decreases.Thus,even those alloys that contain ordered phases in their cast state can transform to random solid solutions at high temperature.However,if the formation enthalpy of an intermetallic compound is high enough to overcome the effect of entropy,that intermetallic compound will still be stable at high temperature.There is much evidence for the high-entropy effect.[7–13]Here we demonstrate this effect with Figure 1,which shows the XRD patterns of a series of binary to septenary alloys.It is seen that thephasesFigure 1.The XRD patterns of a series alloy designed by the sequential addition of one extra element to the previous one.All the alloys have one or two major phases that have simple structures.in quinary,senary,and septenary alloys remain rather simple:there are only two major phases,and these phases have simple structures such as BCC and FCC (note that the septenary alloy actually contain minor intermetallic phases,although not seen clearly in the XRD pattern).Such simple structures contradict conventional expectation:formation of various kinds of binary /ternary compounds.Two myths regarding the high-entropy effect are discussed here.The first myth is that the high-entropy effect guarantees the formation of a simple solid solution phase (order /disordered BCC /FCC)at high tempera-tures.This is not true.Actually,in the very first paper of HEA,[2]the possibility of compound formation was already mentioned.In the same year,the existence of (Cr,Fe)-rich borides in the AlB x CoCrCuFeNi alloys was observed.[14]As mentioned previously,it is the compe-tition between entropy and enthalpy that determines the phase formation.The large negative formation enthalpy of the borides thus justifies their stability at high tem-peratures.Similar phenomena have also been observed in other high-temperature annealing experiments.[15–17]The second myth is that only simple solid solution phases (BCC /FCC)are the desired phases for practical applica-tions of HEAs.This is also not true.It is true that these multiprincipal-element simple solid solution phases are unique to HEAs,and they can have outstanding proper-ties.However,as shown later,many HEAs containing intermediate phases also have great potential.Thus,the exploration of HEAs should not be limited to simple solid solution phases.2.2.Sluggish Diffusion Effect.It was proposed that the diffusion and phase transformation kinetics in HEAs are slower than those in their conventional counterparts.[18]This can be understood from two2D o w n l o a d e d b y [112.83.53.115] a t 17:21 08 M a y 2014Figure 2.Schematic diagram of the potential energy change during the migration of a Ni atom.The mean difference (MD)in potential energy after each migration for pure metals is zero,whereas that for HEA is the largest.[19]aspects.Firstly,in HEA the neighboring atoms of each lattice site are somewhat different.Thus,the neighbors before and after an atom jump into a vacancy are differ-ent.The difference in local atomic configuration leads to different bonding and therefore different local energies for each site.When an atom jumps into a low-energy site,it becomes ‘trapped’and the chance to jump out of that site will be lower.In contrast,if the site is a high-energy site,then the atom has a higher chance to hop back to its origi-nal site.Either of these scenarios slows down the diffusion process.Note that in conventional alloys with a low solute concentration,the local atomic configuration before and after jumping into a vacancy is,most of the time,iden-tical.Tsai et ed a seven-bond model to calculate the effect of local energy fluctuation on diffusion.[19]They showed that for Ni atoms diffusing in Co–Cr–Fe–Mn–Ni alloys (which has a single-phase,FCC structure),the mean potential energy difference between lattice sites is 60.3meV,which is 50%higher than that in Fe–Cr–Ni alloys (see Figure 2).This energy difference leadsto a significantly longer occupation time at low-energy sites (1.73times longer than that at high-energy sites).Indeed,diffusion couple experiments show that the acti-vation energy of diffusion in the Co–Cr–Fe–Mn–Ni alloys is higher than those in other FCC ternary alloys and pure elements (Table 1).[19]The diffusivity of Ni at the respective melting point of each metal is also calculated (Table 1).The slowest diffusion takes place again in the Co–Cr–Fe–Mn–Ni alloys.Secondly,the diffusion rate of each element in a HEA is different.Some elements are less active (for example,elements with high melting points)than others so these elements have lower success rates for jumping into vacancies when in competition with other elements.However,phase transformations typically require the coordinated diffusion of many kinds of elements.For example,the nucleation and growth of a new phase require the redistribution of all elements to reach the desired composition.Grain growth also requires the coop-eration of all elements so that grain boundaries can successfully migrate.In these scenarios,the slow-moving elements become the rate-limiting factor that impedes the transformation.The slow kinetics in HEAs allows readily attainable supersaturated state and nano-sized precipitates,even in the cast state.[2,20,21]It also contributes to the excellent performance of HEA coatings as diffusion barriers.[22,23]Moreover,it allows better high-temperature strength and structural stability,which are discussed in Section 5.For the same reason,HEAs are also expected to have outstanding creep resistance.2.3.Severe-Lattice-Distortion Effect.The lattice in HEAs is composed of many kinds of elements,each with different size.These size differences inevitably lead to distortion of the rger atoms push away their neighbors and small ones have extra space around.The strain energy associated with lattice distortion raises the overall free energy of the HEA lattice.It also affects the properties of HEAs.For example,lattice distortion impedes dislocation movement and leads to pronouncedTable 1.Diffusion parameters for Ni in different FCC matrices.The compositions of Fe-Cr-Ni(-Si)alloys are in wt.%.[19]D 0Q T m (T s )D T m Solute System (10−4m 2/s)(kJ /mol)(K)Q /T m (10−13m 2/s)NiCoCrFeMnNi 19.7317.516070.19750.95FCC Fe 331418120.1733 2.66Co 0.43282.217680.1596 1.98Ni1.77285.317280.1651 4.21Fe-15Cr-20Ni 1.530017310.1733 1.33Fe-15Cr-45Ni 1.829316970.1727 1.73Fe-22Cr-45Ni 1.129116880.1724 1.09Fe-15Cr-20Ni-Si4.831017050.18181.533D o w n l o a d e d b y [112.83.53.115] a t 17:21 08 M a y 2014Figure 3.Hardness of the Al x CoCrCuFeNi alloys as a function of Al content.solid solution strengthening (see Section 4.4).It also leads to increased scattering of propagating electrons and phonons,which translates to lower electrical and thermal conductivity.[4]2.4.Cocktail Effect.The properties of HEAs are cer-tainly related to the properties of its composing elements.For example,addition of light elements decreases the den-sity of the alloy.However,besides the properties of the individual composing elements,the interaction among the component elements should also be considered.For example,Al is a soft and low-melting-point element.Addition of Al,however,can actually harden HEAs.Figure 3plots the hardness of the Al x CoCrCuFeNi alloy as a function of Al content.It is clearly seen that the alloy hardens significantly with the addition of Al.This is partly because of the formation of a hard BCC phase,and partly due to the stronger cohesive bonding between Al and other elements,and its larger atomic size.Thus,the macroscopic properties of HEA not only come from the averaged properties of the component elements,but also include the effects from the excess quantities produced by inter-elemental reactions and lattice distortion.3.Phase and Crystal Structure In conventional alloys,phases are typically classified into three types:ter-minal solutions,intermediate solutions,and intermetallic compounds.We can use similar,but expanded concepts to classify phases in HEAs.Terminal phases are phases based on one dominating element.The definition of intermetallic compounds is not changed:they are stoi-chiometric and have fixed composition ratio.However,because phases in HEAs typically have a composition range rather than fixed composition ratio,intermetallic compounds are rarely seen in HEAs.Solution phases are phases not belonging to the above two categories.This category includes the solid solutions based on both simple(e.g.BCC and FCC)and complex structures (ves phase).In the literature,phases in HEAs are usually clas-sified differently.They are often classified as:random solid solution (e.g.FCC,BCC),ordered solid solution (e.g.B2and L12),and intermetallic phases (ves phases).This classification may lead to some confusion because by definition,intermetallic phases can also be classified as ordered solid solutions—they have compo-sition ranges and are typically ordered.In view of this,we suggest classifying the phases according to their structure (simple /complex)and ordering (ordered /disordered).A phase is said to be simple if its structure is identical to or derived from FCC,BCC or HCP ly,cI2-W,cF4-Cu,and hP2-Mg structures and their ordered versions (superlattices)such as cP2-CsCl (B2)and cP4-AuCu3(L12)structures are simple.[24]If a phase is not simple (ves phases),it is said to be complex .Therefore,the above three types now becomes:simple disordered phase (SDP),simple ordered phase (SOP),and complex ordered phase (COP).3.1.Simple Solid Solution or Intermetallics?.One fundamental and important question regarding HEAs is:what kind of phase and crystal structure will form when we mix so many different elements together?To the surprise of most people,simple structures (SDPs and SOPs)are the most frequently seen in as-cast HEAs (see Figure 1).These simple phases originate from the high-entropy effect mentioned previously.Besides sim-ple phases,different kinds of COPs,such as σ,μ,Laves,etc.,are also observed in HEAs.Because simple-solution phases with more than five elements are unique to HEAs,researchers have worked intensely to understand the conditions for their formation.From the classic Hume-Rothery rules,factors that affect the formation of binary solid solutions include atomic size difference,electron concentration,and dif-ference in electronegativity.[25]Besides these factors,enthalpy and entropy of mixing are the most important phase formation parameters for HEAs.Zhang et al.[26]and Guo et al.[27]studied the effect of these parameters on the phase formation of HEAs and obtained similar conclusions:the formation of simple or complex phases depends mainly on the enthalpy of mixing ( H mix ),entropy of mixing ( S mix ),and atomic size differences (δ).In order to form sole simple phases (i.e.FCC,BCC,and their mixtures,including both ordered /disordered cases),the following conditions have to be met simulta-neously:−22≤ H mix ≤7kJ /mol,δ≤8.5,and 11≤ S mix ≤19.5J /(K mol).[27]This is shown in Figure 4,where the boundary of simple phases is shown.These conditions are quite logical: H mix cannot be too large in value because large positive H mix leads to phase separation and large negative H mix typically leads to 4D o w n l o a d e d b y [112.83.53.115] a t 17:21 08 M a y 2014Figure 4.Superimposed effect of H mix,δ,and S mix on phase stability in equiatomic multicomponent alloys and BMGs.Notes:Blue symbols indicate the relationship between H mix andδ,while red ones represents that between S mix and δ.Symbol◦represents equiatomic amorphous phase-forming alloys;•represents nonequiatomic amorphous phase-forming alloys; represents simple phases and represents inter-metallic phases.The region delineated by the dash-dotted lines indicates the requirements for simple phases to form.[27]intermetallic phases.δhas to be small enough since large δleads to excess strain energy and destabilizes simple structures. S mix has to be large enough because it is the main stabilizing factor for simple phases.If the tar-get of discussion is limited to SDPs only,the conditions are more strict:−15≤ H mix≤5kJ/mol,δ≤4.3,and 12≤ S mix≤17.5J/(K mol).[26]The other criterion to judge whether or not sole simple phases will form in an HEA is the parameter ε=|T S mix/ H mix|.[28,29]Based on the concept of entropy–enthalpy competition(see Section2.1),εrep-resents the effect of entropy relative to that of enthalpy. Thus,largerεsuggests a higher possibility of forming SDP,which agrees well with the analysis.[28,29]A draw-back of the two criterions shown above is that even if an alloy is designed following these criterions,it can still contain intermetallic phases.This can be seen in Figure4. The‘solid solution’region delineated by the dash-dotted lines still contains triangles,indicating the existence of intermetallic phases.A new thermodynamic parameter has been proposed to solve this problem,and the results will be published soon.[30]Some points of notice should be mentioned here: Firstly,the above analyses are based on the as-cast state.This is natural because most reported HEAs are in their as-cast state.Secondly,the phases observed macro-scopically in as-cast HEAs can contain atomic-scale decompositions/inhomogeneities.This is evidenced by detailed transmission electron microscopy(TEM)and atomic probe analysis.[21,31,32]Thirdly,in HEAs sim-ilar disordered and ordered versions of the same base structure often co-exist.[8,33–35]For example,coex-istence of BCC and ordered BCC(B2)phases was frequently reported.[8,21,34]Coexistence of FCC and ordered FCC(L12)phases was also observed.[35]When these phases have nearly identical lattice parameter, their XRD peaks overlap(except the superlattice peaks). Therefore,when the amount of the SOP is small,XRD may reflect only the peaks belonging to SDP,even if SOP exists.[34,35]This indicates that TEM analysis is needed to really confirm the existence of SOP.[34,35]3.2.Crystal Structure of Simple Solid Solution Phases.If an HEA does crystallize into simple phases, what will the structure of the phase be?Virtually all sim-ple solid solution phase observed in HEAs have either BCC or FCC structures.[8,10,36,37]The most critical factor that decides whether an alloy crystallizes into BCC or FCC structure appears to be its VEC(valence electron concentration,the VEC of an alloy is calculated fromthe Figure5.Relationship between VEC and the FCC,BCC phase stability for various HEA systems.Notes:Fully closed symbolsfor sole FCC phases;fully open symbols for sole BCC phase;top-half closed symbols for mixed FCC and BCC phases.[38]5Downloadedby[112.83.53.115]at17:218May214weighted average VEC of the constituent components).Guo et al.summarized the relationship between VEC and structure of many HEAs (Figure 5).[38]They found that when the VEC of the alloy is larger than 8,the FCC structure is stabilized.When the VEC of the alloy is smaller than 6.87,the BCC structure is stabilized.Co-existence of FCC and BCC phase is observed at VEC values between 6.87and 8.[38]However,the mechanism behind the effect of VEC on phase formation has not been fully understood so far.3.3.High-Entropy Bulk Metallic Glass.Although HEAs and bulk metallic glasses (BMGs)are both multi-component and their compositions look similar at first glance,these two classes of materials are based on completely different concepts.For HEAs,more than five principal elements are required.In contrast,most BMGs are based on one or two principal elements.More importantly,BMGs are characterized by their amor-phous structure,but there is no structural requirement for HEAs.The concept of HEAs,however,triggered an idea:would there be good glass formers in HEAs?If so,how do we design high-entropy BMGs (HEB-MGs)with good glass-forming ability (GFA)and better properties?Inoue’s empirical rules [39]state that (1)more than three composing element,(2)large atomic size differ-ences,and (3)large negative mixing enthalpies favor the formation of BMGs.The first rule is in line with the design concept of HEAs.Indeed,high configurational entropy in HEAs was found to be beneficial for glass formation.This was evidenced by lower critical cooling rates in BMGs with higher S mix .[27]Another earlier study also reported similar findings.[40]The second and third rules for BMG formation are exactly opposite to the requirements to form simple phases in HEAs (see Section 3.1).This suggests that one should choose HEAs that are predicted to form intermetallic phases rather than simple phases as candidates for HEBMGs.Indeed,very recently Guo et al.analyzed the H mix and δof some HEAs and BMGs and found that simple-solution-type HEAs and BMGs fall in opposite corners of the δ- H mix plot (shown in Figure 6[41]).They further pointed out that as long as the competition from intermetallic phases can be ruled out by composition adjustment,HEAs that locate in the amorphous phase region of the δ- H mix plot indeed solidify into metallic glasses.[41]This strat-egy has been successfully demonstrated in a series of Zr-containing alloys prepared via melt spinning.[41]Not many HEBMGs have been fabricated till now.Most of these HEBMGs have diameters smaller than 3mm.[42–46]Takeuchi et al.successfully prepared Pd 20Pt 20Cu 20Ni 20P 20HEBMG with a maximum diam-eter of 10mm.[47]But they suggested that conventional GFA assessment parameters such as T rg (reducedglassFigure 6.The δ- H mix plot delineating the phase selection in HEAs.Note:The dash-dotted regions highlight the individual region to form simple solid solutions,intermetallic phases and amorphous phases.[41]transition temperature normalized with liquidus temper-ature)cannot evaluate the GFA of HEBMG effectively.Other factors,e.g.Gibbs free energy assessments,need to be taken into account.[47]Apparently more study is needed to understand these issues.The distinct composition design in HEAs brings with their amorphous structure other special prop-erties.For example,an extremely high crystalliza-tion temperature (probably the highest among reported BMG)of over 800◦C and good performance as dif-fusion barrier between Cu and Si was observed in 20-nm-thick NbSiTaTiZr alloy film.[23]Additionally,Zn 20Ca 20Sr 20Yb 20(Li 0.55Mg 0.45)20shows room temper-ature homogeneous flow and a plasticity of 25%,[44]which is in sharp contrast to the typical shear banding and brittle behavior of most BMGs.Very recently CaMgZn-SrYb,a high-entropy modification of Ca 65Mg 15Zn 20BMG,has shown improved properties in orthope-dic applications.[48]CaMgZnSrYb not only has better mechanical properties,but also promotes osteogenesis and new bone formation after two weeks of implanta-tion.These examples indicate that high-entropy BMGs and amorphous films do have great potential and deserve further exploration—which is good news for both com-munities.3.4.Phases at Elevated Temperatures.As men-tioned in Section 3.1,our understanding of the phases in HEAs focuses mainly on their as-cast state.However,the cast state is typically not in thermodynamic equilibrium.For alloys that locate in the BCC or FCC phase region suggested by Guo et al.(see Figure 5[38]),phase trans-formation may be less significant.For example,no phase formation was found in the Co–Cr–Fe–Mn–Ni FCC alloys during their high-temperature annealing.[14,17]For alloys that locate in the BCC +FCC phase region,6D o w n l o a d e d b y [112.83.53.115] a t 17:21 08 M a y 2014Figure 7.Relationship between the VEC and the presence of σphase after aging for a number of HEAs.Green and red icons indicate the absence and presence of σphase after aging,respectively.[52]however,phase transformation will take place at higher temperatures.In the Al-Co-Cr-Cu-Fe-Ni system,anneal-ing at ∼800◦C or below increases the fraction of the BCC phase.[49]Annealing at temperatures higher than 800◦C increases the fraction of the FCC phase.Because the FCC phase is ductile and the BCC phase is relatively brittle,annealing can be used to tune the mechanical properties.Some alloys have phases that are stable at interme-diate temperatures only.These alloys may contain only simple phases in their as-cast state due to the higher cool-ing rate of Cu mold casting.Upon annealing,however,the intermediate-temperature phases will appear.For exam-ple,ηphase [17,50]and σphase [12,34,51]are known to form in some HEAs.One should pay great attention to the formation of these intermediate-temperature phases,because their formation can lead to significant changes in mechanical properties.Tsai et al.studied the stabil-ity of the σphase.[52]They showed that the formation of the σphase is predictable:it is directly related to the VEC of the alloy.As shown in Figure 7,[52]there is a σ-phase-forming VEC range for HEAs based on Al,Co,Cr,Cu,Fe,Mn,Ni,Ti,and V.Alloys whose VEC fall in this range develop σphase upon annealing at 700◦C.puter-Aided Phase Prediction.The multi-principal-element nature of HEA translates to an immense amount of possible compositions.It takes huge amounts of time and cost to study all the composi-tions experimentally.Hence,it is critically important to implement highly efficient,low-cost methods to assist experimental study.For example,phase diagram is one of the most important tools to understand an alloy sys-tem.However,developing the phase diagram of just one senary HEA requires tremendous efforts—there are six composition axes!If the phase diagram could be reliably predicted with computational techniques,then experi-ments are only needed to verify some critical points onthe predicted phase diagram,and the work needed is significantly reduced.The main problem in applying existing phase-prediction techniques (e.g.CALculation of PHAse Dia-grams,CALPHAD)on HEAs is probably the lack of a suitable database for multicomponent systems.[53]Thus,Zhang et al.developed a thermodynamic database for the Al–Co–Cr–Fe–Ni alloy system by extrapolat-ing binary and ternary systems to wider composition ranges.[53]This database does not consider quaternary or quinary interaction ing their database,they obtained phase diagrams of the Al–Co–Cr–Fe–Ni system that agree with available experimental results.In some cases,it seems that phase diagrams with reasonable accuracy can still be obtained even with-out the development of new databases.An Ni-based superalloy database was used to predict the equilib-rium phases and phase fractions of Al 0.5CoCrCuFeNi,Al 23Co 15Cr 23Cu 8Fe 15Ni 16and Al 8Co 17Cr 17Cu 8Fe 17Ni 33alloys.[12,31]Except for some minor discrepancies,[12]experiments and predictions agree with each other.These examples demonstrate the effectiveness of computational methods in predicting the phase of HEAs.Although the accuracy of computational phase predictions for other HEA systems (other than Al–Co–Cr–Cu–Fe–Ni)is still not clear (the only result appears to be not as pleas-ing [54]),it is greatly expected that these techniques will become important tools for the future design and development of HEAs.3.6.Phase Evolutions in Representative Alloy Sys-tems.The phase evolutions in some important alloy systems are discussed,along with their mechanical prop-erties,in Sections4.2and 4.3.This is because the mechanical properties of HEAs are directly related to the phases contained in the alloys (see Section 4.1).There-fore,the best way to understand the mechanical properties is to discuss phase evolution and mechanical properties together.4.Mechanical Properties4.1.Basic Concepts and Deformation Behaviors.Because of the wide composition range and the enor-mous number of alloy systems in HEAs,the mechanical properties of HEA can vary significantly.In terms of hardness /strength,the most critical factors are:(1)Hardness /strength of each composing phase inthe alloy;(2)Relative volume ratio of each composing phase;(3)Morphology /distribution of the composingphases.7D o w n l o a d e d b y [112.83.53.115] a t 17:21 08 M a y 2014。

Briefing of principal component analysisTime series analysis provides practical means to extract both linear nad non-linear variation patterns in single station. However, when scientists attempt to extract more delicated signals from the time series, this approach is not always satisfactory. For example, in a time series the transient signal is very hard to be distinguished from noises. Also some systemetic error caused apparent variations are also very hard to be identified in a single time series. To extract such subtle information, there is one important character should be noticed and utilized. For a common source (either from space or under ground) the surface station's responses could be different, but their temporal variations should be the same, i. e. the common source time function. That means if we are able to extract the network variation pattern with the same temporal function, such a pattern is usually related to a common source and is not a random error.Principal component analysis (PCA) provides just such a tool. It can decompose the network time series into a series of principal component mode. Each mode consists of a common temporal function and related different spatial responses. Such a decomposition can help scientists to find dedicated signal with intrinsic spatial and temporal correlation. Because the network time series analysis greatly enhance the signal to noise ratio due to the intrinsic correlation of the signals. The PCA has wide applications. For example, it is applied to GPS network time series analysis to perform spatial regional filtering to remove so called common mode error (CME) (see Dong et al. 2006). The pca package of the QOCA software is aimed to help the QOCA users to perform such PCA analysis.Since PCA is used for network time series analysis (not single station time series), the users should prepare the network time series data in advance. The network time series could consist of network analysis output (single epoch many stations) or individual station time series (single station many epochs) or combination of the two. The PCA package analyzes the network east, north and vertical component time series separate;y or jointly. In most cases, the epoch number is larger than the station number. In this case, the principal component will be the common time function and the eigenvector will be the spatial responses. Current PCA utility uses equal weighting. It is still an open question of which weighting is the optimal one.In PCA analysis, the network time series form a data matrix. In this data matrix, each row contains one daily network solutions, with each element of the row represents one station solution at this day. Each column of the data matrix stands for a station time series, with each element of the column represent this station solution at each day. Assuming the network time series consist of m days and n stations, the data matrix is a (m x n) matrix, usually m > n. The SVD of this matrix can get n orthogonal vectors, each vector is called as an "eigenvector".If we want to use the PCA package to perform regional filtering to remove CME effects, the network time series must be the residual time series. That means all tectonic and known geophysical signals (such as mass loading signals) must be extracted in advance. Otherwise, a part of the tectonic and geophysical signals will be treated as CME and will be removed.On the other hand, if we want to analyze tectonic signals from PCA analysis, we still should remove unrelated or well-known signals from time series analysis. For example, if we want to extract post-seismic signals, the velocity, co-seismic jump, and seasonal terms should be removed. If we want to investigate the secular motion, the seasonal, co-seismic jump and possible post-seismic terms should be removed.To run the pca, just typing:pca pca.drvAll the commands are stored in the pca.drv file. The pca.drv file has the same structure and convention asother QOCA driver file. If you are not familiar with the driver file, please look at the basic class web page first. The command lines of the pca.drv file are as followings:job-type: (the job type for this pca run)"regional filtering" means that all tectonic and seasonal terms are removed.Our goal is to get the common mode error (mostly high-frequency)."tectonic signal" means that seasonal terms are removed. The time seriescontain tectonic signals (either velocity or jump or post-seismic).decomposition method: (choose the decomposition algorithm)PCA: Principal Component AnalysisKLE: Karhunen-Loeve ExpansionSVD: Singular Value Decompositionapriori value file: (site position and velocity apriori value file)the same format as other QOCA package used.site_list: (site list file name)In the site_list file,the first line is the site number.The rest lines are site names.Sometimes the user might set redundant site names by accident. The pcaprogram will check the site name list and remove redundant site names.in_list: (input file list name)In the in_list file, the first line is data file number.The rest lines are the file name (including path) and file type.type = 11: QOCA map file format (analyze_tseri output residual file format)type = 12: SOPAC neu data file format.type = 13: single component (n, e, u) file for one station (Owen's file)type = 14: GIPSY stacov formattype = 15: REASoN web service xyz tar file formattype = 16: high-rate coordinate file (map format)The default data type is 11.output file: (output file name)This file records the i/o information, adjusted bias, trend, and seasonal terms foreach station, eigen values and the percentage powers for east, north and upcomponents. If "adj_6par" option is not activated, the eigen values and the percentagepowers are still listed in the output file.component file: (output principal component file)The first entry is the file name, the secont entry is the output category.if the category is 'component': output scaled principal component values.if the category is 'vector': output eigenvector values.if the category is 'time series': output time series for east, north and up respectively.The default category is 'component'. The other categories are still under construction.Currently, the PCA code only outputs the first 12 principal components.time series file: (output filtered result file)The first entry is the file name, the secont entry is the output category.if the category is 'residual': output unfiltered, filtered values and their residuals.if the category is 'coordinates': output recovered coordinates using all PC modes.(this category is trivial, only used to check if the PC are correct)if the category is 'map': output filtered time series in QOCA map format.The default category is 'residual'.eigenvector file: (spatial eigenvector output file)This file records the normalized spatial responses of each station for the first10 principal components.sit_range: (geographical ranges of the network)The four entries are minimum and maximum longitude, minimum and maximum latitude.The unit is degree. The defaults are 0.0, 360.0, -90.0 and 90.0.soln_span : (option to specify the start and end epoches of the solutions)input two real valuesfill_gap option: (yes or no)The default is "yes".If the answer is "no", the program will disable the fill gap function.In most cases, fill the time series gap is necessary. Otherwise, the gap willbe assigned zero. It will generate artificial pattern and cause misleading.It is ideal if every time series have no gap. In proctice, however, many timeseries have big or small gaps. If we discard stations with gaps in pca analysis,we will lost many stations, sometimes most stations.How to fill the gap is not easy and is still an open question. If the taskis "regional filtering", that means the time series is dominated by highfrequency common mode errors, and the station response to the cme is similar.In this case, pca program uses "stacking" values to fill the gap. If thetask is "tectonic signal", that means the patterns in the time series aredominated by tectonic signals, which might differ by one to two orders fromstation to station. In this case, pca program first assigns smoothed averagedvalues to the gaps. Then perform pca analysis. Then re-fill the gap using thefirst principal component time series and station responses. After thisprocedure, re-do the pca analysis to get the updated principal componentsand station responses. Using the updated first principal component and itscorresponding station responses to re-fill the gap.horizontal jointly: (yes or no)In "regional filtering" task, we usually analyze east and north time seriesseparately. In this case, this option should be "no". In "tectonic signal"task, however, many users want to analyze east and north components jointlyto get horizontal principal components. In this case, this option should be"yes".adj_6par option: (yes or no)If the answer is "yes", the program will adjust 6 parameters (bias, tread, annualand semi-annual terms) for each time series before performing PCA analysis.In general, the time series have other signals, such as jumps. We recommendthe users to use analyze_tseri to remove the 6 parameters and other un-relatedsignal (such as jumps). Then use the residual time series (output of theanalyze_tseri program) as the input file for pca program. In this case, thisadj_6par option should be turned off.outlier_sigma criterion: (specify the uncertainty criteria to kick out outliers)The following three entries are sigmas for east, north and up components.If the data sigma is larger than the criteria (weak solutions), thisdata will be eliminated. The unit is mm.outlier_value criterion: (specify the residual criteria to kick out outliers)The following three entries are absolute residual criteria for east, northand up components. The unit is mm.If the absolute residuals at one epoch of one station is larger than thecriteria (abnormal solutions), this data will be eliminated.cut_p: (minimum data percentage for each station)The entry is the percentage number.If one station has the solution epoch number less than the total solutionepoch number times the percentage, this station will be eliminated.For the task of "regional filtering", the fill gap is relatively easy. Inthis case, the cut_p can be set as small as 35%. For the task of"tectonic signal", the fill gap is very difficult. In this case, the cut_pshould be at least 65%.cut_t: (minimum station percentage for each epoch)The entry is the percentage number.If one day has the station number less than the total station numbertimes thise percentage, this day will be eliminated.reference frame: (terrestrial reference frame name)always WGS84.reference coordinate, rtime:first term is always "geodetic", second term is reference time forcalculating apriori coordinatesThe format and structure of the pca output files are:output fileThe first part is the pca driver file command information.If the driver file selects the adj_6par option, the next part will be theadjusted 6 parameters for each station. The format of this part is:* Atjusted terms for each site (unit:meter)* site bias trend annu(s) annu(c) semi(s) semi(c) AGMT_GPS east 0.000483 -0.000139 -0.000587 0.000291 0.000543 -0.001222 AGMT_GPS north 0.004310 -0.000989 0.000785 0.000120 -0.000656 -0.000174 AGMT_GPS verti 0.006870 -0.001467 -0.000014 -0.000348 -0.001415 -0.000936 AOA1_GPS east -0.000649 0.000089 -0.000279 0.000465 0.000668 -0.001094 AOA1_GPS north 0.000521 -0.000102 0.001112 0.000100 -0.000420 -0.000133 AOA1_GPS verti -0.002280 0.001013 -0.000036 0.001317 -0.001080 -0.000027Where (s) and (c) represent sine and cosine components respectively.Then the next part is the eigenvalues for east, north and vertical componentsrespectively. The format is:* East eigenvalues and % of correlation matrix1 0.00142040 89.00230941 89.002312 0.00003027 1.89642852 90.898743 0.00002282 1.42978558 92.328524 0.00000886 0.55546524 92.883995 0.00000725 0.45409034 93.33808The structure is:eigenvalue index, eigenvalue, % contribution of this eigenvalue, cumulative % to this eigenvalue.principal component fileThe pca program only outputs the first 12 scaled principal components. If any QOCA user requiresmore principal component output, please let me know. It is not difficult to output all principalcomponent values. But the size of the file will be very large.The output records have such format:* scaled east components:* time cpt1 cpt2 cpt3 cpt4 cpt5 cpt6 cpt7 cpt8 cpt9 cpt10 cpt11 cpt122000.0014 -0.001976 0.014927 0.000296 -0.002457 0.002051 0.000381 -0.001594 0.000008 0.001212 0.000122 0.000100 0.0005492000.0041 -0.004825 0.011802 0.005657 -0.000232 0.000883 0.000546 -0.000659 -0.001126 0.000435 0.000327 0.001250 0.0002702000.0068 -0.002982 0.013643 0.000794 -0.000796 0.001570 0.000526 -0.001143 -0.000050 -0.000443 0.000135 0.000111 0.0008072000.0096 -0.001354 0.014377 -0.000281 -0.002076 0.002044 0.000419 -0.001664 0.000370 0.000370 0.000288 0.000146 0.0008752000.0123 -0.003078 0.011687 -0.000870 -0.001672 0.002274 0.000095 -0.001365 0.000408 0.000301 0.000353 0.000108 0.0007932000.0150 -0.004222 0.002929 -0.006689 -0.002020 0.002913 0.000959 -0.001715 0.000395 -0.000029 0.000748 0.000032 0.001222The unit of epoch is year, the unit of principal component is meter.Each column (from the second column) represents the time series of the principal component.spatial eigenvector fileThe pca program only outputs the first 10 normalized eigenvectors for each station. If any QOCAuser wants complete eigenvector output, please let me know. It is not difficult to output alleigenvector values. But the size of the file will be very large.The output records have such format:* east eigenvector: 227* long lati site cpt1 cpt2 cpt3 cpt4 cpt5 cpt6 cpt7 cpt8 cpt9 cpt10* normalizing factor -0.09528 0.77039 -0.61805 0.59285 0.46782 -0.48422 -0.46368 -0.34874 0.46373 0.40250* average response -0.86172 0.05211 -0.08256 0.07733 0.10342 -0.11071 -0.11280 -0.17440 0.12686 0.13102243.5706 34.5943 AGMT_GPS 0.786173 0.030840 -0.043089 0.038027 -0.020653 0.092111 -0.130852 -0.313286 0.032294 -0.088940241.1697 34.1574 AOA1_GPS 0.854234 0.018386 0.002262 -0.059263 -0.020137 0.035081 -0.117655 0.181050 -0.163100 0.030760242.8460 34.4683 A VRY_GPS 1.000000 -0.201297 0.263746 -0.160093 1.000000 0.586014 0.825860 1.000000 1.000000 -0.510985243.3703 33.5401 AZRY_GPS 0.889059 -0.042620 0.048725 -0.024711 0.122527 0.012637 -0.064372 -0.198350 0.070798 0.005159240.0185 34.5822 BBDM_GPS 0.870611 0.050043 -0.116844 0.069494 0.108233 0.003930 0.215983 -0.128866 -0.295977 -0.117076243.1158 34.2643 BBRY_GPS 0.855492 0.017201 -0.046896 -0.069091 -0.035756 0.049116 0.074084 -0.199824 0.088559 -0.055232Since this is the normalized eigenvector, it is unitless. the maximum value is 1.000.Each column (from the fourth column) represents the spatial distribution of the principal component.If user chooses "horizontal jointly" option, the eigen vector will be horizontal and vertical. In thiscase, the horizontal output will be as this:* horizontal eigenvector: 454* long lati site cpt1 cpt2 cpt3 cpt4 cpt5 cpt6 cpt7 cpt8 cpt9 cpt10* normalizing factor 0.30347 0.32534 -0.29331 -0.34670 -0.18258 0.30022 0.38840 -0.26361 0.38303 0.33569* average response 0.09114 0.07266 -0.09649 -0.08137 -0.17093 0.10126 0.07657 -0.12041 0.07424 0.09000243.5706 34.5943 AGMT_GPE -0.040610 0.034052 -0.417406 0.196077 0.155420 0.011836 0.197804 0.357700 0.047622 -0.120002240.6365 34.0150 ANA1_GPE 0.009949 0.032434 -0.022279 -0.006904 0.058120 -0.009183 0.020171 -0.024710 -0.040098 -0.056674241.1697 34.1574 AOA1_GPE -0.047484 0.116542 0.034051 -0.044705 -0.170371 0.052921 0.063861 0.108879 0.032371 0.134470..............243.5706 34.5943 AGMT_GPN 0.014060 -0.010375 -0.019847 -0.014936 0.037490 -0.035642 -0.024061 -0.024847 0.014083 -0.043634240.6365 34.0150 ANA1_GPN 0.190329 0.623364 -0.109740 -0.013221 -0.046476 -0.091553 -0.027226 0.067135 0.045108 0.124644241.1697 34.1574 AOA1_GPN 0.336354 -0.819326 -0.011507 0.252991 0.289680 0.0297590.068345 -0.219933 -0.025195 0.195039As we can see, for the east response the last character of the station name is assigned as "E".For the north response the calst character of the station name is assigned as "N".The pca output principal component file and spatial eigenvector file can be directly used in utility analyze_tseri to perform regional filtering correction. Interested users can look at the advanced class "Time series analysis" web page for details.。

材料科学基础常用英语词汇材料的类型Types of materials, metals, ceramics, polymers, composites, elastomer部分材料性质复习Review of selected properties of materials,电导率和电阻率conductivity and resistivity,热导率thermal conductivity,应力和应变stress and strain,弹性应变elastic strain,塑性应变plastic strain,屈服强度yield strength,最大抗拉强度ultimate tensile strength,最大强度ultimate strength,延展性ductility,伸长率elongation,断面收缩率reduction of area,颈缩necking,断裂强度breaking strength,韧性toughness,硬度hardness,疲劳强度fatigue strength,蜂窝honeycomb,热脆性heat shortness,晶胞中的原子数atoms per cell,点阵lattice, 阵点lattice point,点阵参数lattice parameter,密排六方hexagonal close-packed,六方晶胞hexagonal unit cell,体心立方body-centered cubic,面心立方face-centered cubic,弥勒指数Miller indices,晶面crystal plane,晶系crystal system,晶向crystal direction,相变机理Phase transformation mechanism:成核生长相变nucleation–growth transition,斯宾那多分解spinodal decomposition,有序无序转变disordered-order transition,马氏体相变martensite phase transformation，成核nucleation,成核机理nucleation mechanism,成核势垒nucleation barrier,晶核，结晶中心nucleus of crystal,（金属组织的）基体quay,基体，基块，基质，结合剂matrix,子晶，雏晶matted crystal,耔晶，晶种seed crystal,耔晶取向seed orientation,籽晶生长seeded growth,均质核化homogeneous nucleation,异质核化heterogeneous nucleation,均匀化热处理homogenization heat treatment,熟料grog,自恰场self-consistent field固溶体Solid solution:有序固溶体ordered solid solution,无序固溶体disordered solid solution,有序合金ordered alloy,无序合金disordered alloy.无序点阵disordered lattice,分散，扩散，弥散dispersal,分散剂dispersant,分散剂，添加剂dispersant additive,分散剂，弥散剂dispersant agent缺陷defect, imperfection,点缺陷point defect,线缺陷line defect, dislocation,面缺陷interface defect, surface defect,体缺陷volume defect,位错排列dislocation arrangement,位错阵列dislocation array,位错气团dislocation atmosphere,位错轴dislocation axis,位错胞dislocation cell,位错爬移dislocation climb,位错滑移dislocation slip, dislocation movement by slip, 位错聚结dislocation coalescence,位错核心能量dislocation core energy,位错裂纹dislocation crack,位错阻尼dislocation damping,位错密度dislocation density,体积膨胀volume dilation,体积收缩volume shrinkage,回火tempering,退火annealing,退火的，软化的softened,软化退火，软化（处理）softening,淬火quenching,淬火硬化quenching hardening,正火normalizing, normalization,退火织构annealing texture,人工时效artificial aging,细长比aspect ratio,形变热处理ausforming,等温退火austempering,奥氏体austenite,奥氏体化austenitizing,贝氏体bainite,马氏体martensite,马氏体淬火marquench,马氏体退火martemper,马氏体时效钢maraging steel,渗碳体cementite,固溶强化solid solution strengthening,钢屑混凝土steel chips concrete,水玻璃，硅酸钠sodium silicate,水玻璃粘结剂sodium silicate binder,硅酸钠类防水剂sodium silicate waterproofing agent, 扩散diffusion,扩散系数diffusivity,相变phase transition,烧结sintering,固相反应solid-phase reaction,相图与相结构phase diagrams and phase structures , 相phase,组分component,自由度freedom,相平衡phase equilibrium,吉布斯相律Gibbs phase rule,吉布斯自由能Gibbs free energy,吉布斯混合能Gibbs energy of mixing,吉布斯熵Gibbs entropy,吉布斯函数Gibbs function,相平衡phase balance,相界phase boundary,相界线phase boundary line,相界交联phase boundary crosslinking,相界有限交联phase boundary crosslinking,相界反应phase boundary reaction,相变phase change,相组成phase composition,共格相phase-coherent,金相相组织phase constentuent,相衬phase contrast,相衬显微镜phase contrast microscope,相衬显微术phase contrast microscopy,相分布phase distribution,相平衡常数phase equilibrium constant,相平衡图phase equilibrium diagram,相变滞后phase transition lag, Al-Si-O-N系统相关系phase relationships in the Al-Si-O-N system, 相分离phase segregation, phase separation,玻璃分相phase separation in glasses,相序phase order, phase sequence,相稳定性phase stability,相态phase state,相稳定区phase stabile range,相变温度phase transition temperature,相变压力phase transition pressure,同质多晶转变polymorphic transformation,相平衡条件phase equilibrium conditions,显微结构microstructures,不混溶固溶体immiscible solid solution,转熔型固溶体peritectic solid solution,低共熔体eutectoid，crystallization,不混溶性immiscibility,固态反应solid state reaction,烧结sintering,相变机理Phase transformation mechanism:成核生长相变nucleation–growth transition,斯宾那多分解spinodal decomposition,有序无序转变disordered-order transition,马氏体相变martensite phase transformation，成核nucleation,成核机理nucleation mechanism,成核势垒nucleation barrier,晶核，结晶中心nucleus of crystal,（金属组织的）基体quay,基体，基块，基质，结合剂matrix,子晶，雏晶matted crystal,耔晶，晶种seed crystal,耔晶取向seed orientation,籽晶生长seeded growth,均质核化homogeneous nucleation,异质核化heterogeneous nucleation,均匀化热处理homogenization heat treatment,熟料grog,。