Spectroscopic and photometric observations of unidentified ultraviolet variable objects in the G

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.。

Spectroscopy and Spectral AnalysisSpectroscopy is a branch of science that deals with the study of the interaction between matter and electromagnetic radiation. Spectral analysis is a technique that is used to identify and measure the properties of substances based on the electromagnetic radiation that they emit, absorb, or scatter. The study of spectroscopy and spectral analysis is essential to many fields, including chemistry, physics, environmental science, and biomedical research.Types of SpectroscopyThere are several types of spectroscopy, each based on the type of electromagnetic radiation used. The most common types of spectroscopy include:1. Absorption SpectroscopyAbsorption spectroscopy is a technique that measures the amount of radiation absorbed by a sample. This type of spectroscopy is used to identify the chemical composition and concentration of a substance. Absorption spectroscopy can be used in the ultraviolet, visible, and infrared regions of the electromagnetic spectrum.2. Emission SpectroscopyEmission spectroscopy measures the amount of radiation emitted by a substance. This type of spectroscopy is used to identify the chemical composition of a substance and the temperature and pressure of the environment. Emission spectroscopy can be used in the ultraviolet, visible, and infrared regions of the electromagnetic spectrum.3. Fluorescence SpectroscopyFluorescence spectroscopy is a technique that measures the amount of radiation emitted by a substance when it is excited by light of a particular wavelength. This type of spectroscopy is used to identify the presence of certain substances in a sample, such as proteins and DNA molecules. Fluorescence spectroscopy can be used in the ultraviolet and visible regions of the electromagnetic spectrum.4. Raman SpectroscopyRaman spectroscopy is a technique that measures the scattered radiation produced when a sample is irradiated with a laser beam. This type of spectroscopy is used to identify the chemical composition and structure of a substance. Raman spectroscopy can be used in the visible and near-infrared regions of the electromagnetic spectrum.Applications of Spectroscopy and spectral analysis have a wide range of applications in various fields, including:1. ChemistrySpectroscopy is used extensively in chemistry to identify the chemical composition and properties of substances. Spectroscopy is used to determine the purity of a substance, study chemical reactions, and analyze the structure of molecules.2. PhysicsIn physics, spectroscopy is used to study the properties of materials, such as their electronic and magnetic properties. Spectroscopy is used to study the interactions between atoms and molecules and to investigate the behavior of quantum systems.3. Environmental ScienceSpectroscopy is used in environmental science to study the properties of soil, water, and air. Spectroscopy can be used to identify pollutants in the environment and to monitor the quality of drinking water and industrial wastewater.4. Biomedical ResearchIn biomedical research, spectroscopy is used to study the properties of biological molecules, such as proteins and DNA. Spectroscopy is used to image and diagnose diseases, such as cancer, and to monitor the effectiveness of treatments.ConclusionSpectroscopy and spectral analysis are powerful tools for studying the properties of matter and electromagnetic radiation. There are several types of spectroscopy, each with its own strengths and applications. Spectroscopy and spectral analysis are used in many fields, including chemistry, physics, environmental science, and biomedical research, and have a wide range of applications.。

测量薄膜光谱的仪器
测量薄膜光谱的仪器通常称为光谱仪或分光光度计。

以下是一些常用的测量薄膜光谱的仪器：
1. 分光光度计：分光光度计使用光栅或光柱将入射光分散成不同波长的光，并测量每个波长处的光强度。

分光光度计通常具有高分辨率和较大的波长范围，适用于测量宽波长范围内的薄膜光谱。

2. 椭圆偏振仪：椭圆偏振仪用于测量薄膜在不同波长处的偏振特性。

它可以测量通过薄膜的偏振光的椭圆度和振幅比，从而获得薄膜材料的光学特性。

3. 紫外-可见-近红外光谱仪：这种光谱仪可测量从紫外到可见
到近红外范围的波长。

它通常具有高分辨率和较大的波长范围，能够对薄膜的吸收、透射和反射等光学性质进行全面测量。

4. 激光扫描共聚焦显微镜：这种显微镜使用激光束扫描薄膜表面，并测量薄膜在不同位置处的荧光或散射信号。

它可以提供有关薄膜表面形貌、结构和光学特性的详细信息。

5. 拉曼光谱仪：拉曼光谱仪使用激光照射薄膜样品，并测量薄膜表面散射光的频移。

通过测量散射光的频移，可以获得薄膜样品的分子结构信息和化学成分。

这些仪器通常结合不同的探测器、样品夹持装置和分析软件来实现对薄膜光谱的准确测量和分析。

相干反斯托克斯拉曼散射光谱技术的发展下载温馨提示:该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。

文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by theeditor.I hope that after you download them,they can help yousolve practical problems. The document can be customized andmodified after downloading,please adjust and use it according toactual needs, thank you!In addition, our shop provides you with various types ofpractical materials,such as educational essays, diaryappreciation,sentence excerpts,ancient poems,classic articles,topic composition,work summary,word parsing,copy excerpts,other materials and so on,want to know different data formats andwriting methods,please pay attention!相干反斯托克斯拉曼散射光谱技术的发展与前沿自20世纪60年代首次被提出以来，相干反斯托克斯拉曼散射（CARS）光谱技术已经在化学、物理、生物医学和材料科学等领域展现了其独特的价值。

太赫兹波谱图的分析和用途太赫兹波是指电磁波频率在100GHz至10THz之间的无线电波，被称为物质世界的最后一片未知天地。

太赫兹波的频率和波长介于微波和红外线之间，具有穿透性强、分辨率高等优点，因此在材料科学、药物制剂、食品安全等领域具有重要应用价值。

太赫兹波谱图是暴露物体所反射、透射和散射太赫兹波的强度和相位的可视化结果，也是太赫兹波技术应用中最主要、最基础的手段之一。

太赫兹波谱图可以提供材料的电磁特性信息，如电导率、介电常数、多晶核心双折射参数等，也可以检验物质的结构、组分的一致性和纯度等。

太赫兹波谱图的分析方法相对较为简单常见，主要分为时间域和频率域两类。

时间域方法侧重于太赫兹波与检测样品的时差、相位差等参数的测量，适用于复杂介质的分析。

频率域方法则可通过离散傅里叶变换（FFT）将太赫兹光谱数据转换到频率域，进而获取物质的功率谱、复折射率等信息，常用于高精度检测。

太赫兹波谱图的应用十分广泛。

在材料科学领域，太赫兹波谱图可用于分析纳米材料的电子特性、检测半导体的缺陷信息等；在药物制剂领域，可用于表征药剂的晶型和结晶类型等信息，判断药物中掺杂物的性质；在食品安全上，可用于检测食品中的添加物、除草剂等信息，以及探测食品中的水分含量。

此外，太赫兹波谱图在安检、生化诊断、人体成像等方面也具有潜在应用。

尽管太赫兹波技术具有巨大的潜力，但其发展仍受到一定的限制。

太赫兹波的穿透性强，但其波长又较短，因此其传输距离受到一定的限制，只能作用于相对较短的距离内。

此外，太赫兹发射源的制造和检测技术也存在较大的挑战性，目前尚不能形成成熟的工业化生产模式。

总体来说，在科技的持续发展和应用的推广下，太赫兹技术必将有更广泛的应用前景和发展空间。

而太赫兹波谱图的分析方法和应用研究也将得到不断的完善和优化，为材料科学、医药、食品安全等领域进一步提供技术支持和解决方案，为人类创造更加美好的未来。

近红外光谱法英文Near-Infrared SpectroscopyNear-infrared spectroscopy (NIRS) is a powerful analytical technique that has gained widespread recognition in various scientific and industrial fields. This non-invasive method utilizes the near-infrared region of the electromagnetic spectrum, typically ranging from 700 to 2500 nanometers (nm), to obtain valuable information about the chemical and physical properties of materials. The versatility of NIRS has led to its application in a diverse array of industries, including agriculture, pharmaceuticals, food processing, and environmental monitoring.One of the primary advantages of NIRS is its ability to provide rapid and accurate analysis without the need for extensive sample preparation. Unlike traditional analytical methods, which often require complex sample extraction and processing, NIRS can analyze samples in their natural state, allowing for real-time monitoring and decision-making. This efficiency and non-destructive nature make NIRS an attractive choice for applications where speed and preservation of sample integrity are crucial.In the field of agriculture, NIRS has become an invaluable tool for the assessment of crop quality and the optimization of farming practices. By analyzing the near-infrared spectra of plant materials, researchers can determine the content of various nutrients, such as protein, carbohydrates, and moisture, as well as the presence of contaminants or adulterants. This information can be used to guide precision farming techniques, optimize fertilizer application, and ensure the quality and safety of agricultural products.The pharmaceutical industry has also embraced the use of NIRS for a wide range of applications. In drug development, NIRS can be used to monitor the manufacturing process, ensuring the consistent quality and purity of active pharmaceutical ingredients (APIs) and finished products. Additionally, NIRS can be employed in the analysis of tablet coatings, the detection of counterfeit drugs, and the evaluation of drug stability during storage.The food processing industry has been another significant beneficiary of NIRS technology. By analyzing the near-infrared spectra of food samples, manufacturers can assess parameters such as fat, protein, and moisture content, as well as the presence of adulterants or contaminants. This information is crucial for ensuring product quality, optimizing production processes, and meeting regulatory standards. NIRS has been particularly useful in the analysis of dairy products, grains, and meat, where rapid and non-destructive testing is highly desirable.In the field of environmental monitoring, NIRS has found applications in the analysis of soil and water samples. By examining the near-infrared spectra of these materials, researchers can obtain information about the presence and concentration of various organic and inorganic compounds, including pollutants, nutrients, and heavy metals. This knowledge can be used to inform decision-making in areas such as soil management, water treatment, and environmental remediation.The success of NIRS in these diverse applications can be attributed to several key factors. Firstly, the near-infrared region of the electromagnetic spectrum is sensitive to a wide range of molecular vibrations, allowing for the detection and quantification of a variety of chemical compounds. Additionally, the ability of NIRS to analyze samples non-destructively and with minimal sample preparation has made it an attractive choice for in-situ and real-time monitoring applications.Furthermore, the development of advanced data analysis techniques, such as multivariate analysis and chemometrics, has significantly enhanced the capabilities of NIRS. These methods enable the extraction of meaningful information from the complex near-infrared spectra, allowing for the accurate prediction of sample propertiesand the identification of subtle chemical and physical changes.As technology continues to evolve, the future of NIRS looks increasingly promising. Advancements in sensor design, data processing algorithms, and portable instrumentation are expected to expand the reach of this analytical technique, making it more accessible and applicable across a wider range of industries and research fields.In conclusion, near-infrared spectroscopy is a versatile and powerful analytical tool that has transformed the way we approach various scientific and industrial challenges. Its ability to provide rapid, non-invasive, and accurate analysis has made it an indispensable technology in fields ranging from agriculture and pharmaceuticals to food processing and environmental monitoring. As the field of NIRS continues to evolve, it is poised to play an increasingly crucial role in driving innovation and advancing our understanding of the world around us.。

荧光光谱法英文Fluorescence SpectroscopyFluorescence spectroscopy is a powerful analytical technique that has found widespread applications in various fields, including chemistry, biology, materials science, and environmental studies. This analytical method is based on the measurement of the emission of light by a substance that has been excited by the absorption of light or other forms of energy. The process of fluorescence involves the absorption of energy by molecules or atoms, followed by the subsequent emission of light at a longer wavelength than the absorbed light.The fundamental principle of fluorescence spectroscopy is that when a molecule or atom is exposed to light, it can absorb the energy of the incoming photons, causing electrons within the molecule or atom to be excited to higher energy levels. This excitation is a temporary state, and the electrons will eventually return to their ground state, releasing the excess energy in the form of a photon. The energy of the emitted photon is typically lower than the energy of the absorbed photon, resulting in a shift in the wavelength of the emitted light compared to the absorbed light. This wavelength shift is known as the Stokes shift, and it is a key characteristic offluorescence.The intensity and wavelength of the emitted light are influenced by various factors, such as the chemical structure of the fluorescent molecule, the solvent environment, temperature, and the presence of other compounds that can interact with the excited molecules. By analyzing the characteristics of the emitted light, researchers can gain valuable insights into the properties and behavior of the sample under investigation.Fluorescence spectroscopy has a wide range of applications in various fields. In chemistry, it is used for the identification and quantification of organic and inorganic compounds, as well as the study of reaction kinetics and molecular interactions. In biology, fluorescence spectroscopy is employed for the investigation of protein structure and dynamics, the detection and quantification of biomolecules, and the study of cellular processes. In materials science, this technique is used to characterize the properties of polymers, semiconductors, and nanomaterials, among others.One of the key advantages of fluorescence spectroscopy is its high sensitivity, which allows for the detection and quantification of analytes at very low concentrations. Additionally, the technique is non-invasive and can be performed in real-time, making it a valuable tool for in-situ and online monitoring applications. Furthermore, thedevelopment of advanced fluorescent probes and labeling techniques has expanded the versatility of fluorescence spectroscopy, enabling the visualization and tracking of specific molecules or cellular components in complex biological systems.Despite its many benefits, fluorescence spectroscopy also faces some limitations. The presence of interfering compounds, quenching effects, and the potential for photobleaching of the fluorescent molecules can challenge the reliability and accuracy of the measurements. Researchers are constantly working to address these challenges through the development of new instrumentation, data analysis methods, and sample preparation techniques.In conclusion, fluorescence spectroscopy is a powerful and versatile analytical tool that has made significant contributions to various scientific disciplines. As technology continues to advance, the applications of this technique are expected to expand further, providing researchers with new opportunities to gain a deeper understanding of the world around us.。