DOI 10.1007s11276-007-0023-z Performance of a burst-frame-based CSMACA protocol Analysis an

网络出版时间：２０２３－０６－１５１４：１５：３３　网络出版地址：ｈｔｔｐｓ：／／ｋｎｓ．ｃｎｋｉ．ｎｅｔ／ｋｃｍｓ２／ｄｅｔａｉｌ／３４．１０８６．Ｒ．２０２３０６１４．１５４４．０１３．ｈｔｍｌ◇心血管药理学◇机械敏感离子通道Ｐｉｅｚｏ１通过激活β ｃａｔｅｎｉｎ诱导心房成纤维细胞衰老叶兴东，罗雪珊，李巧巧，何锦涛，徐玉雯，杨　慧，邓春玉，邝素娟，张梦珍，吴书林，饶　芳，薛玉梅（广东省心血管病研究所，广东省人民医院，广东省医学科学院，广东广州　５１００８０）收稿日期：２０２３－０３－１０，修回日期：２０２３－０５－１３基金项目：国家自然科学基金资助项目（Ｎｏ８１８７０２５４，８２２７０３２１）；广东省科技计划项目协同创新与平台环境建设（Ｎｏ２０２０Ｂ１１１１１７００１１）作者简介：叶兴东（１９９６－），男，硕士生，研究方向：心律失常临床实验，Ｅ ｍａｉｌ：ｇｄｃｉ２０ａｍｄｓ＠１６３．ｃｏｍ；薛玉梅（１９７０－），女，博士，主任医师，博士生导师，研究方向：心律失常与药物治疗，通信作者，Ｅ ｍａｉｌ：ｘｕｅｙｕｍｅｉ＠ｇｄｐｈ．ｏｒｇ．ｃｎｄｏｉ：１０．１２３６０／ＣＰＢ２０２２１１０６９文献标志码：Ａ文章编号：１００１－１９７８（２０２３）０７－１２３４－０８中国图书分类号：Ｒ ３３２；Ｒ３２２ １１；Ｒ３３９ ３８；Ｒ５４１ ７５；Ｒ９７７ ６摘要：目的　衰老心房成纤维细胞中新型机械敏感离子通道Ｐｉｅｚｏ１的基因表达明显升高，观察其是否通过激活β ｃａｔｅｎｉｎ参与心房成纤维细胞的衰老进程。

方法　通过酶消化法分离培养３～４周龄雄性Ｃ５７ＢＬ／６小鼠原代心房成纤维细胞，给予叔丁基过氧化氢（ＴＢＨＰ）刺激建立衰老模型，检测衰老相关β 半乳糖苷酶（ＳＡ βＧａｌ）活性。

Ｗｅｓｔｅｒｎｂｌｏｔ检测ＴＢ ＨＰ（１００μｍｏｌ·Ｌ－１）处理的细胞中Ｐｉｅｚｏ１、β ｃａｔｅｎｉｎ／ｐ βｃａｔｅｎｉｎ及衰老相关蛋白ｐ５３和ｐ２１的表达水平。

铁死亡抑制剂预培养的大鼠心肌细胞抵抗素诱导后细胞肥大程度及铁死亡相关蛋白表达观察朱贲贲1，2，海日汗3，王巍嵩4，杨鹏杰5，61 内蒙古自治区肿瘤医院药剂科，呼和浩特010020；2 内蒙古医科大学附属人民医院药剂科；3 内蒙古医科大学麻醉系；4 内蒙古医科大学附属医院药学部；5 内蒙古自治区肿瘤医院胸外科；6 内蒙古医科大学附属人民医院胸外科摘要：目的　观察铁死亡抑制剂Fer -1预培养的大鼠心肌细胞系H9c2抵抗素诱导后细胞肥大程度及铁死亡相关蛋白的表达情况。

方法　将H9c2细胞饥饿培养16 h ，随机分为1、2、3、4组。

1组用含1 μmmol /L 的Fer -1的培养基培养16 h ，后换为含50 ng /mL 抵抗素的培养基培养32 h ；2组用含1 μmmol /L 的Fer -1培养基培养16 h ，后换为0.1% FBS 培养基培养32 h ；3组用含50 ng /mL 抵抗素的培养基培养48 h ；4组（正常对照组）用0.1% FBS 培养基培养48 h 。

取各组细胞，采用Image J 软件测量细胞表面积，采用BCA 法测算单细胞蛋白含量，采用RT -qPCR 法检测心肌细胞肥大相关胚胎基因ANP 、BNP 和β-MHC ；采用WESTERN Blotting 法检测各组铁死亡相关蛋白谷胱甘肽过氧化物酶 4 （Glutathione Peroxidase 4，GPX4）、酰基辅酶 A 合成酶长链家族成员 4（Acyl -CoA synthetase long -chain fami⁃ly member ，ACSL4） 及环氧合酶2（COX2）。

结果　与4组比较，3组H9c2细胞表面积增大，单细胞蛋白含量高，ANF 、BNF 及β-MHC 基因相对表达量高，GPX4蛋白相对表达量低、ACSL4及COX2蛋白相对表达量高（P 均 <0.05）。

与3组相比，1组细胞表面积小，单细胞蛋白含量低，ANF 、BNF 及β-MHC 基因相对表达量低（P 均 <0.05）；与3组相比，1组细胞GPX4蛋白相对表达量高、ACSL4及COX2蛋白相对表达量低（P 均 <0.05）。

山东医药2023 年第 63 卷第 29 期天然产物抑制小胶质细胞活化的作用机制研究进展王迎紫，文家斌，李龑大连医科大学附属第二医院国际医疗部，辽宁大连 116023摘要：小胶质细胞是大脑中巨噬细胞样的固有免疫细胞，其活化介导的神经炎症是一系列神经退行性疾病发生发展的重要病理机制，而抑制小胶质细胞活化成为防治神经退行性疾病的重要策略。

天然产物是指动、植物提取物或昆虫、海洋生物、微生物体内的组成成分或代谢产物，具有多效、多靶点、毒性小的特点。

部分天然产物中的有效成分具有抑制小胶质细胞活化，减轻神经炎症的作用。

对天然产物抑制小胶质细胞活化的作用机制进行深入研究，或可为神经退行性疾病的药物治疗提供新思路。

关键词：小胶质细胞激活；天然药物；髓细胞上表达的触发受体2；丝裂原活化蛋白激酶；肠—脑轴doi：10.3969/j.issn.1002-266X.2023.29.021中图分类号：R741.02 文献标志码：A 文章编号：1002-266X（2023）29-0087-03随着人口老龄化的加剧，以脑衰老为代表的神经退行性疾病患病率逐年增高，神经退行性疾病的相关机制及干预策略成为当前研究的热点［1］。

小胶质细胞是中枢神经系统中最小的一种神经胶质细胞，小胶质细胞活化介导的神经炎症是一系列神经退行性疾病发生发展的重要病理机制，而抑制小胶质细胞活化成为了防治神经退行性疾病的重要策略［2］。

天然产物是指动、植物提取物或昆虫、海洋生物、微生物体内的组成成分或代谢产物，主要包括蛋白质、多肽、氨基酸、核酸、各种酶类等天然存在的化学成分。

多项研究显示，部分天然产物中的有效成分具有抑制小胶质细胞活化，改善神经炎症的作用，且具有多效、多靶点、毒性小的特点。

天然产物可通过增加触发受体2（TREM2）表达、调控丝裂原活化蛋白激酶（MAPK）信号通路、改善肠道菌群失调等机制作用于小胶质细胞，发挥抑制小胶质细胞活化，减轻神经炎症的作用。

第58卷第3期2018年5月大连理工大学学报J o u r n a l o fD a l i a nU n i v e r s i t y o f T e c h n o l o g yV o l .58,N o .3M a y 2018文章编号:1000-8608(2018)03-0221-08大温差条件下多孔材料干燥过程数值模拟陈 磊1, 王树刚*2, 张腾飞2, 贾子光3, 房天宇4(1.大连大学建筑工程学院,辽宁大连 116622;2.大连理工大学建设工程学部,辽宁大连 116024;3.大连理工大学海洋科学与技术学院,辽宁盘锦 124221;4.中国建筑东北设计研究院有限公司,辽宁沈阳 110016)摘要:基于热力学平衡假设并结合动态边界条件,对大温差下多孔保温材料中的水分迁移及三态相变过程进行数值模拟,并以海绵干燥过程为例给出了实验验证.在数学模型中考虑了由于温差引起的自然对流效应,分析了不同时刻海绵内部的温度㊁水蒸气浓度㊁液态水及冰体积分数的分布规律,讨论了材料主要物性参数对水分传递过程的影响.结果表明:数学模型能够实现对大温差下多孔介质中水分传递过程的准确预测;由物性参数分析的结果可知,相比于影响多孔保温材料水分传递的有效扩散系数和孔隙率两个关键物性参数而言,有效导热系数影响相对微弱.关键词:多孔介质;自然对流;孔隙率;有效扩散系数中图分类号:T K 124文献标识码:Ad o i :10.7511/d l l gx b 201803001收稿日期:2017-06-26; 修回日期:2018-03-20.基金项目:国家自然科学基金青年基金资助项目(51608094).作者简介:陈磊(1983-),男,博士,讲师,E -m a i l :l _l e i _l @163.c o m ;王树刚*(1963-),男,博士,教授,博士生导师,E -m a i l :s g w a n g@d l u t .e d u .c n .0 引 言由于多孔材料具有良好的保温隔音效果,被广泛应用于食品加工㊁建筑材料㊁燃料电池等诸多领域[1-3].多孔材料长期置于温㊁湿度间歇性变化的环境中,水分极易积聚于孔隙,若不能及时去除,将会引起保温隔音性能下降㊁细菌滋生㊁腐蚀等诸多负面效应.因此,多孔材料中水分传递过程一直备受关注.目前,国内外关于水分在多孔材料中的传递及相变的研究主要集中于水分传递过程㊁能量传递过程及相间转换3个方面.多孔材料孔隙中的水分会以水蒸气㊁液态水和冰3种不同的相态存在,而孔隙间水分的迁移通常是以水蒸气和液态水两种形式.液态水的传递均是采用D a r c y 定律进行求解[4],当液态水的体积分数较小,自身重力无法克服毛细阻力时,其流动可被忽略[5].与液态水的传递过程相比,水蒸气的传递方式呈现出复杂多样性.当多孔保温材料及其所处环境之间温㊁湿度差异较小时,如多孔材料表面无高速湿空气冲刷,可认为水蒸气的传递只取决于浓度差引起的自由扩散而忽略流动效应[6].当多孔保温材料与其所处环境间存在强制对流时,水蒸气的流动效应凸显,通常采用显式的D a r c y 速度对水蒸气的传递过程进行描述[7-8].而考虑到大温差引起的多孔材料内部的自然对流时,由于自然对流的非线性流动特性,D a r c y 定律便不能准确描述多孔材料孔隙间水蒸气的流动现象[9],需采用B r i n k m a n -F o r c h h e i m e r -e x t e n d e d D a r c y 模型(D a r c y 扩展模型)对流场进行求解[10-11].当多孔材料中水蒸气㊁液态水和冰的3种水分相态的任意两种同时存在时,相变的发生将不可避免.而当多孔材料内的温度变化范围跨越冰点时,其内部的水分就会出现复杂的三相间的相互转换,水分相态间的转换必然伴随着热量的吸收与释放.基于热力学平衡原理,在计算多孔材料中的水分传递过程时,须考虑任意时刻同时满足能量及质量的守恒关系[12-13].此外,为确保热湿耦合模型对水分传递和相变过程预测的准确性,需对多孔材料的骨架结构进行合理简化,并正确选取材料的物性参数[14-16].本文对多孔保温材料的干燥过程进行数值模拟,并通过实验对模拟结果进行验证,进一步分析大温差下水分传递和相变过程的速度㊁温度㊁水蒸气浓度㊁液态水及冰含量随时间的变化规律,讨论孔隙率㊁有效导热系数和有效扩散系数对水分传递过程的影响.1数学模型和实验过程1.1数学模型为使得模型封闭并且能够准确地预测水分在多孔材料中的传递及相变过程,在方程求解前做出如下假设:(1)气相各组分具有相同的速度;(2)水蒸气饱和时相间发生转化;(3)多孔材料孔隙均匀.根据假设条件(1),干空气与水蒸气共享湿空气的速度场,因本文采用的数学模型将应用于大温差条件下的自然对流,势必诱发多孔材料中气相速度的非线性分布,宜结合D a r c y扩展模型与浮升力模型对宏观流动进行求解,其实质是对N a v i e r-S t o k e s方程组进行扩展.在源项引入黏性和惯性阻力项,表现形式如下[8]:v∂(ϕρv)∂t+d i v(ϕρv v)=-ϕÑp+Ñ(ϕτ--)-ϕρgβΔT-(μα+ρC22v)v(1)式中:ϕ为孔隙率;ρ为气体密度,k g/m3;v为气相速度,m/s;t为时间,s;p为气相压力,P a;τ为剪切应力,P a;g为重力加速度,m/s2;β为热扩散系数,K-1;ΔT为特定参考温度下的温差,K;μ为动力黏度,P a㊃s;1/α为黏性阻力系数,m-2;C2为惯性阻力系数,m-1.根据假设条件(2),水蒸气在多孔介质中的传递过程分为两种情况:当水蒸气未达饱和时,无相变发生,水蒸气遵循质量守恒原理在其分压力的作用下传递于多孔材料的孔隙间;而当水蒸气达到当前温度下的饱和状态时,孔隙中的水蒸气密度将不发生改变,而水蒸气的流入流出必将伴随着相变的发生.水蒸气质量传递方程表达形式如下[17]:v∂ρv∂t+d i v(vρv)=d i v(D e f f g r a dρv)+S v(2)式中:ρv为水蒸气密度,k g/m3;D e f f为有效扩散系数,m2/s;S v为相变量,k g/m2∙s.多孔材料中水蒸气的相变,将会引起能量的吸收与释放.能量方程表达式为[17]ρm c p,m∂T∂t+ρg c p,g d i v(v T)=d i v(k e f f g r a d T)+S T(3)式中:ρm为各组分构成的混合密度,k g/m3;c p,m为混合比定压热容,k J/(k g㊃K);ρg为气相密度, k g/m3;c p,g为气相比定压热容;T为温度,K;k e f f 为有效导热系数,W/(m㊃K);S T为由于相变产生的相变热源.在能量方程中相关参数的定义式如下[17-18]:ρm c p,m=a gρg c p,g+a sρs c p,s+a lρl c p,l+a iρi c p,i(4) k e f f=a g k g+a s k s+a l k l+a i k i(5)D e f f=a g D v a/J(6)式中:a表示体积分数,下标s㊁g㊁l㊁i分别表示固体骨架㊁气相㊁液相及固相;D v a表示水蒸气在干空气中的扩散系数,m2/s;J表示曲折因子.1.2实验过程本文以非吸湿性多孔材料(海绵)作为实验对象,其尺寸为210m mˑ150m mˑ44m m.实验准备阶段将海绵浸入纯净水中,待海绵孔隙充满液态水后,将其取出并挤压拍打,以保证孔隙中含水量尽可能均匀.将含水海绵放入低压箱中,并设置箱内的温度为-15ħ,待海绵与环境间达到热平衡时,海绵温度稳定在-14.1ħ.经精密天平称量得到海绵的增重量为3.97g.完成上述实验准备,将海绵取出并迅速挂入恒温箱中进行自然干燥.恒温箱顶部放置一部精密天平,带有挂钩的非吸水性细线穿过恒温箱顶部微小孔隙与精密天平相连,以便通过精密天平实时记录海绵中的水分散失量.在恒温箱内靠近测试对象区域分别布有温度与湿度传感器,用以记录海绵周围环境温㊁湿度随时间变化情况.为保证实验过程中,海绵所处的温㊁湿度环境尽可能不受外界干扰,除对恒温箱整体保温处理外,恒温箱222大连理工大学学报第58卷的底部增设了加热器与加湿用的水盘,实验示意图如图1所示.图1 实验示意图F i g .1 E x p e r i m e n t a l s c h e m a t i c d i a gr a m 图2为所监测的海绵附近环境温度和相对湿度随时间变化情况.从环境温㊁湿度随时间的变化曲线可知,箱体内的温度在实验开始阶段呈下降趋势,20m i n 左右又出现微小的回升,并在140m i n 以后随着时间的增加逐渐下降.主要原因在于,恒温箱顶部的微小孔隙必将引起箱内环境与外界环境间的温㊁湿度交换,从而降低了恒温箱的保温性能.但从温度变化的幅度来看,实验全程仅有1K 左右的波动,足以说明海绵所处的温度环境相对恒定.而从相对湿度曲线的变化趋势可知,实验开始阶段,相对湿度呈上升趋势,原因在于海绵内的水分以水蒸气的形式融入周围环境,从而导致箱内空气的含湿量增高,实验过程中,恒温箱内相对湿度一度升至65%.由于恒温箱与大气环境间(顶部开孔)的热湿交换,箱内水蒸气在分压力的驱动下向恒温箱外扩散,导致实验后半段箱内相对湿度逐渐下降.温㊁湿度传感器所记录的海绵周围环境的温㊁湿度变化曲线将以第一类边界条件应用于模型中.图2 恒温箱内温度及相对湿度随时间变化曲线F i g .2 T e m p e r a t u r e a n d r e l a t i v eh u m i d i t y c h a n gew i t h t i m e i n t h e t h e r m o s t a t2 模型验证及过程分析根据数学模型结合C F D 软件,对大温差下多孔材料(海绵)的自然干燥过程进行数值模拟.如图1所示,因海绵所处的环境温湿度相对均匀,选取半块海绵采用二维数值模拟足以满足计算要求.运用网格生成软件对尺寸为210m mˑ22m m 的矩形海绵进行网格划分,由于研究对象形状规则,可生成单位尺寸为1m m 的均匀结构化网格(详见图3).求解以5s 为单位时间步长,每个时间步长内迭代3000次,保证动量和连续性方程残差低于10-3,标量方程残差在10-6以下,以此判定各求解方程的收敛性.为保证求解过程不受网格尺寸和网格数量影响,分别选取单位尺寸为0.5m m 和1.5m m 的网格进行独立解的测试,不同网格尺寸的计算误差小于1.3%.模型中采用的物性参数详见表1.图3 网格划分F i g .3 M e s h i n g sc h e m e 海绵内部水分总量随时间变化的模拟结果与实验测量值的对比如图4所示.结果表明:在干燥过程的前20m i n,海绵中的水分含量急剧下降,主要原因在于,冰冻后的海绵在实验开始时被迅速放入恒温箱中,海绵与恒温箱的环境之间存在较大的温差,当低温海绵接触到相对高温的环境时,热量会迅速传入引起温升并为相变提供热量.干燥过程初始阶段,含冰孔隙的温度低于冰点,因此,海绵内的水分(冰)将以水蒸气升华的形式渗出.随着时间的增加,海绵温度逐渐升高,当温度高于冰点后,海绵中的冰全部融化成液态水,水分将以水蒸气蒸发的形式继续从海绵孔隙脱离.随322 第3期陈磊等:大温差条件下多孔材料干燥过程数值模拟表1数学模型中物性参数[19-20]T a b .1 P h y s i c a l pa r a m e t e r s i n t h em o d e l [19-20]参数数值固体骨架体积分数a s0.03密度ρs/(k g ㊃m -3)1000导热系数k s /(W ㊃m -1㊃K -1)0.05比定压热容c p ,s /(k J ㊃k g -1㊃K -1)1.4曲折因子J1.02液态水密度ρl /(k g ㊃m -3)1000气相体积分数a g 0.97有效孔隙率ϕg_e f f 0.8水蒸气扩散系数D v a/(m 2㊃s -1)2.55ˑ10-5导热系数k g/(W ㊃m -1㊃K -1)0.0242水蒸气汽化热h gl /(k J ㊃k g -1)2537着海绵温度继续升高,海绵内外温差逐渐缩小,其与环境间温度梯度也随之减小,外界提供用于相变的热量亦随之下降,从而降低了脱水率.从50m i n 开始,海绵中的脱水率近似线性变化,主要原因在于,随着海绵内的温度和水蒸气浓度与环境间逐渐平衡,海绵和环境间的水蒸气分压力差趋于稳定.值得注意的是,模拟结果的脱水率比实验测量值普遍偏低,此误差主要来自测量仪器响应和实验读数的延迟性.当达到480m i n 时,水分基本蒸干.图4 海绵内水分含量随时间变化F i g .4 T h e c h a n g e o fm o i s t u r e i n s p o n gew i t h t i m e 3 分析与讨论3.1 各主要参数的分布规律预测图5展示了干燥过程进行到10m i n 时,保温材料内部速度㊁温度㊁水蒸气浓度㊁液态水和冰体积分数的分布规律.初始时海绵温度较低(-14.1ħ),其与环境间存在较大的温差,致使海绵孔隙间的水蒸气(在空气的携带下)形成自然对流,但由于多孔骨架所引起的黏性和惯性阻力,湿空气流动缓慢,数量级仅为10-3m /s .从流场分布来看,由于海绵中心区域与环境间存在较大的温差,该区域水蒸气流速要高于与环境相接触的海绵孔隙的.从温度分布的情况可知,由于海绵较低的导热性能且水分蒸发需要吸收外界环境传入部分热量,因而靠近海绵中心区域温升缓慢.此时,海绵中心区域温度仍在冰点以下,仍有冰未完全融化.相比之下,近环境侧海绵孔隙由于直接与温度相对较高的低浓度水蒸气相接触,孔隙中冰迅速融化成水,并以水蒸气蒸发的形式从海绵中脱离.根据文中假设条件(2),含水孔隙的水蒸气始终为饱和态.从水蒸气浓度分布规律可知,此时,环境侧海绵孔隙中水分已经蒸干,此区域孔隙中水蒸气浓度已低于饱和浓度,传入热量完全用于热传导和温升,温度已与环境相近,因此,其与环境间温度梯度很小.干燥过程至3h 时,海绵中水蒸气流速㊁温度㊁水蒸气的浓度及液态水体积分数分布如图6所示.经过3h 的干燥,海绵逐渐升温,且温度已与周围环境相近,仅有1K 左右的温差.此时,由温差引起的自然对流相比10m i n 时已明显减弱,数量级已降至10-4m /s,从此时的边界条件的测量值可知,干燥过程中环境温度变化幅度只有1K 左右,而相对湿度也只相差10%(详见图2).基于假设条件(2),含水孔隙中的水蒸气饱和,水蒸气将在其分压力的驱动下(流速影响可忽略)从海绵中渗出,而水蒸气的浓度梯度主要存在于含水孔隙(水蒸气饱和)与恒温箱内的环境之间.因此,此过程海绵内部的水蒸气的脱水率相对恒定.而温度㊁水蒸气浓度和液态水体积分数非线性分布现象明显减弱.图7为干燥过程达到7h 时,海绵中主要参数的分布规律.温度分布与3h 时相接近,此时,海绵内部的温度与环境之间已达平衡,温差所引起自然对流进一步减弱,海绵内部因自然对流引起的水分迁移几乎可以忽略.近海绵中心处温度略低于边界区域的孔隙,主要由于液态水的蒸发吸热延缓了温升.因中心区域的孔隙中仍有少量的液态水残存,基于假设条件(2),孔隙中水蒸气亦为饱和,因此,从水蒸气的浓度分布中仍可看出明显的浓度梯度.3.2 物性参数影响如图8所示,当模拟过程采用的边界条件及物性参数均不发生变化时,将有效导热系数增大422大连理工大学学报第58卷(a)速度场(m/s)(b)温度场(ħ)(c)水蒸气浓度场(k g/m3)(d)水体积分数(e)冰体积分数图5干燥过程进行到10m i n主要参数分布F i g.5 D i s t r i b u t i o no fm a j o r p a r a m e t e r s d u r i n g d r y i n gp r o c e s s a t t=10m in(a)速度场(m/s)(b)温度场(ħ)(c)水蒸气浓度场(k g/m3)(d)水体积分数图6干燥过程进行到3h主要参数分布F i g.6 D i s t r i b u t i o no fm a j o r p a r a m e t e r s d u r i n g d r y i n gp r o c e s s a t t=3h(a)速度场(m/s)(b)温度场(ħ)(c)水蒸气浓度场(k g/m3)(d)水体积分数图7干燥过程进行到7h主要参数分布F i g.7 D i s t r i b u t i o no fm a j o r p a r a m e t e r s d u r i n g d r y i n gp r o c e s s a t t=7h1倍至0.05W/(m㊃K),对上述干燥过程再次进行数值模拟.虽然从数值上来看,有效导热系数的改变量并不大,但多孔材料的一个重要特性就是其良好的保温隔热性能即导热系数较小,因此,有效导热系数增加1倍已经远超模拟过程参数选取的极限误差值.基于模拟结果与实验测量值的对522第3期陈磊等:大温差条件下多孔材料干燥过程数值模拟比可知,海绵的单位时间脱水率与实验测量值(每5m i n 进行一次称重读数)的偏差小于5%,干燥时间的累计误差小于7%.由此模拟结果可知,有效导热系数对多孔材料中的水分传递影响很小,选取此参数进行数值模拟时可允许误差范围较大.图8 海绵有效导热系数为0.05W /(m ㊃K )时水分含量变化对比F i g .8 C o m p a r i s o n o f m o i s t u r e c h a n g e i n s p o n ge w h e n t h e ef f e c t i v e t h e r m a l c o n d u c t i v i t y i s 0.05W /(m ㊃K )如图9所示,海绵曲折因子增大1倍,其他物性参数不变的情况下,对干燥过程进行数值模拟.由模拟结果与实验测量值的对比可知,单位时间脱水率最大偏差将近40%,干燥时间的累积误差将近25%.从水分含量的变化规律来看,初始阶段,由于自然对流流速较高(相比于扩散作用),20m i n 内,实验与模拟结果具有较好的一致性,而随着自然对流的减弱(海绵与环境间温差缩小),扩散作用对水分传递的影响更加显著.由式(6)可知,海绵的曲折因子的增大等同于有效扩散系图9 海绵有效扩散系数缩小至1.32ˑ10-5m 2/s 时水分含量变化对比F i g .9 C o m p a r i s o no fm o i s t u r e c h a n g e i n s p o n gew h e n t h e e f f e c t i v e d i f f u s i o n c o e f f i c i e n t i s 1.32ˑ10-5m 2/s数的减小.模拟结果足以说明,有效扩散系数是影响多孔介质中水分传递的关键性参数.如图10所示,在其他物性参数不变,仅忽略有效孔隙率的情况下,对干燥过程进行再次数值模拟(结果详见图10中曲线P 97,I C ),同时,为进一步展示大温差下自然对流效应对海绵内水分迁移的影响,增加了一组不考虑流动的纯扩散的模拟结果(图10中曲线P 97,D O ),并分别与实验测试值进行对比.由式(1)和式(6)可知,孔隙率的提高必然加强多孔材料中的流动强度及扩散效果.从水分传递规律(与图4的模拟过程相比)和纯扩散的干燥过程可知,在孔隙率提高了20%的情况下,扩散引起的偏差主要来自长时间的模拟过程中脱水率的累积效应,即单位时间内对脱水率的影响并不明显.因此,在多孔材料内外温差较小(自然对流效应忽略)的短时间水分传递过程中,孔隙率的选取偏差并不会对水分传递造成较大影响.但大温差条件(本文为15~35ħ),孔隙率增大,自然对流的作用大幅增强,致使干燥过程加快了近3h .从而说明大温差条件下孔隙率在建模时的正确选取将直接影响到模型的准确性.图10 不考虑有效孔隙率(孔隙率97%)时对流(P 97,I C )及纯扩散(P 97,D O )模型水分含量变化对比F i g .10 C o m p a r i s o n o f m o i s t u r e c h a n ge w h e n i g n o r i n g ef f e c t i v e p o r o s i t y (97%o fp o r o s i t y )u n d e r c o n s i d e r i n g c o n v e c t i o n (P 97,I C )a n d p u r e d i f f u s i o n (P 97,D O )m o d e l r e s p e c t i v e l y4 结 论(1)从实验数据及模拟结果可以看出,所建立的数学模型与实验结果具有较好的一致性,能够准确预测出大温差条件下多孔介质中水分的相变及传递过程.622大连理工大学学报第58卷(2)分析不同时刻模拟结果中的温度㊁水蒸气浓度㊁水分(冰或液态水)体积分数可知,当低温多孔材料放入相对恒定高温环境时,虽然近35ħ保温材料内外温差所诱发自然对流的流速十分微弱,数量级只有10-3~10-4m/s,但其流动却导致了多孔材料中较为明显的温度㊁水蒸气浓度和液态水的非线性分布(参见图5~7).(3)由海绵这种多孔材料中水分传递过程的分析可知,有效导热系数增大1倍(从0.025 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t i o n s,a m a t h e m a t i c a lm o d e l i sd e v e l o p e dt os i m u l a t e m o i s t u r e m i g r a t i o na n dt h r e e s t a t e p h a s e t r a n s i t i o n p r o c e s s i n p o r o u s i n s u l a t i o nm a t e r i a l u n d e r l a r g e t e m p e r a t u r ed i f f e r e n c e,a n d i t i s v e r i f i e db y t h e s p o n g ed r y i n g e x p e r i m e n t.I n t h em o d e l i n g,t h e e f f e c t o f n a t u r a l c o n v e c t i o nc a u s e d b y t e m p e r a t u r ed i f f e r e n c e i s c o n s i d e r e d.T h e a n a l y s i s i s c o n d u c t e do n t h ed i s t r i b u t i o nc h a r a c t e r i s t i c s o f t e m p e r a t u r e,w a t e r v a p o r c o n c e n t r a t i o n,t h ev o l u m e f r a c t i o no f l i q u i dw a t e r a n d i c e i n t h e s p o n g e w i t he v o l u t i o no ft i m e.T h ei n f l u e n c eo f p h y s i c a l p a r a m e t e r so nt h e m o i s t u r et r a n s f e r p r o c e s si s d i s c u s s e d.F i n a l l y,i t i sc o n c l u d e dt h a t t h e m o d e l c o u l da c c u r a t e l yp r e d i c tt h e p r o c e s so fm o i s t u r e t r a n s f e r i n p o r o u s m e d i a u n d e rl a r g et e m p e r a t u r e d i f f e r e n c e,a n df r o m t h ea n a l y s i s o f p h y s i c a l p a r a m e t e r s,t h e p o r o s i t y a n de f f e c t i v ed i f f u s i o nc o e f f i c i e n t a r e t h ek e y f a c t o r s t oa f f e c t t h em o i s t u r e t r a n s f e r i n p o r o u s i n s u l a t i o nm a t e r i a l,a n d t h e e f f e c t i v e t h e r m a l c o n d u c t i v i t y h a s a l e s s e r i m p a c t. K e y w o r d s:p o r o u sm e d i a;n a t u r e c o n v e c t i o n;p o r o s i t y;e f f e c t i v e d i f f u s i o n c o e f f i c i e n t822大连理工大学学报第58卷。

2017年第36卷第8期 CHEMICAL INDUSTRY AND ENGINEERING PROGRESS·2787·化 工 进展1,4-丁炔二醇加氢过程研究进展刘响，廖启江，张敏卿（天津大学化工学院，天津 300072）摘要：1,4-丁炔二醇（BYD ）加氢产物1,4-丁二醇（BDO ）是重要的有机化工原料，工业上该加氢过程存在反应压力高、设备投资大、催化剂易失活等问题。

本文综述了1,4-丁炔二醇加氢反应过程的特点，对比了不同BYD 加氢工艺并分析了各工艺特点和面临的问题，回顾了Ni 基、Pt 基、Pd 基加氢催化剂的开发进展，简述了新型加氢反应器的特点与研究进展。

基于催化剂表面氢浓度对BYD 加氢反应过程影响显著的特点，提出通过强化反应过程气液、液固传质效率对BYD 加氢过程进行改进，以期降低反应压力。

通过加入金属助剂和改变载体性质开发加氢催化剂，提高催化剂活性和稳定性。

最后总结展望了开发BYD 低压加氢剂催化剂和低压加氢反应器的发展趋势，结合BYD 加氢Ni 基催化剂具有磁性的特点，提出磁场辅助流化床对BYD 加氢过程的潜在应用前景。

关键词：1,4-丁炔二醇；1,4-丁二醇；加氢；过程强化；催化剂；反应器中图分类号：TQ032.4 文献标志码：A 文章编号：1000–6613（2017）08–2787–11DOI ：10.16085/j.issn.1000-6613.2017-0320Research progress of 1,4-butynediol hydrogenation processLIU Xiang ，LIAO Qijiang ，ZHANG Minqing（School of Chemical Engineering and Technology ，Tianjin University ，Tianjin 300072，China ）Abstract: 1,4-butanediol （BDO ），hydrogenation product of 1,4-butynediol （BYD ），is an importantorganic chemical raw material. The main problems of industrial hydrogenation of BYD are high pressure ，huge capital investment and catalyst deactivation. Characteristics of the hydrogenation process of BYD ，characteristics and confronting problems of hydrogenation process ，development of Ni-based ，Pt-based and Pd-based catalysts for hydrogenation process and new types of reactors were reviewed. Based on the characteristics of great impact of hydrogen concentration of catalyst surface on the BYD hydrogenation reaction process ，it was proposed to improve the hydrogenation process of BYD by enhancing the gas-liquid ，liquid-solid mass transfer efficiency and to reduce the hydrogenation pressure. The activity and stability of the catalyst could be improved by adding supporting metals and change of the carriers. The development trends of low pressure 1,4-butynediol hydrogenation catalyst and reactor were discussed. Combining the magnetic properties of the Ni-based catalyst used in the hydrogenation process ，the potential application of magnetically assisted fluidized bed reactor in BYD hydrogenation process was proposed. Key words ：1,4-butynediol ；1,4-butanediol ；hydrogenation ；process intensification ；catalyst ；reactors1,4-丁二醇（BDO ）是一种重要的有机和精细化工原料，用于生产聚氨酯树脂、聚对苯二甲酸丁二醇酯、四氢呋喃、γ-丁内酯、聚四亚甲基乙二醇醚等产品，广泛应用于医药、化工、纺织、造纸、汽车等领域[1]。

中国组织化学与细胞化学杂志CHINESE JOURNAL OF HISTOCHEMISTRY AND CYTOCHEMISTRY第29卷第4期2020年8月V ol.29.No.4August.2020〔收稿日期〕2020-03-09 〔修回日期〕2020-08-10〔基金项目〕重庆市中医药科技项目（ZY201702039）〔作者简介〕张容，女（1983年），汉族，本科，药师*通讯作者(To whom correspondence should be addressed)：380663347@黄芪多糖减轻放射引起的小鼠肺部炎症和纤维化张容1，周儒兵2，周双容3*（重庆大学附属肿瘤医院1肿瘤转移与个体化诊治转化研究重庆市重点实验室门诊药房，2药学部，3肿瘤转移与个体化诊治转化研究重庆市重点实验室中药房，重庆 400030）〔摘要〕目的 探究黄芪多糖（astragalus polysaccharides, APS ）对放射诱导肺损伤（radiation-induced lung injury, RILI ）小鼠的保护作用。

方法 通过单剂量22.5 Gy 胸部暴露构建RILI 小鼠模型，并给予APS 治疗；在放射后第4周，用MGG 染色和BCA 法分别检测支气管肺泡灌洗液(bronchoalveolar laragefluid, BALF)中炎性细胞和总蛋白含量；用HE 染色和ELISA 法分别评估肺组织病理学改变和炎性介质水平；在放射后第12周，用Masson 染色评估肺组织纤维化程度。

构建 Lewis 肺癌荷瘤小鼠，并评估APS 对放疗效果的影响。

结果 APS 治疗可降低RILI 小鼠的BALF 中炎性细胞数和总蛋白浓度并减轻肺组织损伤、炎性反应和纤维化。

并且，APS 不影响荷瘤鼠的放疗效果。

结论APS 减轻了小鼠中放射诱导的肺部炎症、损伤及肺纤维化，且APS 不影响放疗效果。

〔关键词〕黄芪多糖；放射；放射诱导肺损伤；保护作用〔中图分类号〕R734.2 〔文献标识码〕A DOI ：10.16705/ j. cnki. 1004-1850. 2020. 04. 004Astragalus polysaccharides attenuates radiation-induced inflammation and fibrosis ofthe lung in miceZhang Rong 1, Zhou Rubing 2, Zhou Shuangrong 3*(1Department of outpatient pharmacy, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment; 2School of Pharmacy; 3Department of traditional Chinese pharmacy, Chongqing Key Laboratory of Translational Researchfor Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030)〔Abstract 〕Objective To investigate the protective effect of astragalus polysaccharides (APS) on radiation-induced lung injury (RILI) in mice. Methods RILI mouse model was established by a single dose of 22.5Gy chest exposure, and then APS treatment was given. At the 4th week after radiation, the contents of inflammatory cells and total protein in bronchoalveolar laragefluid (BALF) were detected by MGG staining and BCA assay; the changes of histopathology and levels of inflammatory mediators in lung tissues were evaluated by HE staining and ELISA assay, respectively. At 12th week after radiation, the degree of pulmonary fibrosis was evaluated by Masson staining. Lewis lung cancer tumor-bearing mice were constructed and the effect of APS on radiotherapy was evaluated. Results APS treatment decreased the number of inflammatory cells and total protein concentration in BALF of RILI mice and reduced lung tissue damage, inflammatory response and fibrosis. In addition, APS did not affect the radiotherapy effect of tumor-bearing mice. Conclusions APS reduces radiation-induced pulmonary inflammation, injury and pulmonary fibrosis in mice without influence on the effect of radiotherapy.〔Keywords 〕Astragalus polysaccharide; radiation; radiation-induced lung injury; protection放疗是多种局部晚期和不能手术的胸部恶性肿瘤的基本治疗方式之一[1]，例如，超过一半的非小细胞肺癌患者目前接受放射治疗[2]。

CHEMICAL INDUSTRY AND ENGINEERING PROGRESS 2016年第35卷第11期·3576·化工进展氧化石墨烯-羧基碳纳米管-多乙烯多胺三维蜂窝状材料吸附CO2胡航标，张涛，崔征，唐盛伟（四川大学化学工程学院，四川成都 610065）摘要：以氧化石墨烯-羧基碳纳米管水溶液为原料，葡萄糖酸β内酯为交联促进剂，通过冷冻干燥法制得负载有多乙烯多胺的氧化石墨烯-羧基碳纳米管三维多孔气凝胶。

通过在制备过程中改变多乙烯多胺的加入量可以调节其负载量。

FTIR、XRD、TG、SEM、XPS、Raman、N2吸脱附等测试表征发现：多乙烯多胺通过酰胺键与基体连接，所得材料呈蜂窝状，且比表面积、孔容和平均孔径随胺类负载量的增加而逐渐降低，引入多乙烯多胺后的气凝胶材料通过化学作用实现CO2吸附。

在200kPa、328K下，多乙烯多胺含量为55.8%的改性吸附剂的CO2吸附量可达3.9mmol/g，为相同条件下未改性吸附剂的9.8倍。

结果表明，将多乙烯多胺、氧化石墨烯和羧基碳纳米管制备成三维蜂窝状多孔材料能有效提高CO2吸附性能。

关键词：纳米材料；二氧化碳；凝胶；吸附中图分类号：TQ 424.29 文献标志码：A 文章编号：1000–6613（2016）11–3576–09DOI：10.16085/j.issn.1000-6613.2016.11.029Preparation of three-dimensional honeycomb-like material of graphene oxide -carboxylated carbon nanotube-polyethylenepolyamine toadsorb CO2HU Hangbiao，ZHANG Tao，CUI Zheng，TANG Shengwei（College of Chemical Engineering，Sichuan University，Chengdu 610065，Sichuan，China）Abstract：Using graphene oxide and carboxylated carbon nanotube as base material and gluconic acid β lactone as crosslinking promoter，we prepared a three-dimensional（3D）porous aerogel material functionalized by polyethylenepolyamine（PEPA）with a freeze-drying method. The PEPA loading was adjusted by changing the PEPA dosage. The as-synthesized 3D porous aerogel material were characterized by FTIR，XRD，TG，SEM，XPS，Raman and N2 adsorption-desorption. The results indicated that PEPA was grafted by an amide bond between PEPA and graphene oxide. The 3D porous material had a honeycomb like-appearance. The specific surface area，pore volume and average pore size were decreased with increasing PEPA loading. The adsorption of CO2 on the 3D honeycomb-like material was based on a mechanism of chemisorption. At 200kPa and 328K，the CO2 adsorption capacity on the 3D porous material with a PEPA content of 55.8% reached up to 3.9mmol/g，which was9.8 times to that on the aerogel without PEPA loading. The results showed that crosslinkingpolyethylenepolyamine with graphene oxide sheet and carboxylated carbon nanotube to prepare 3D porous aerogel material effectively improved the CO2 adsorption capacity.Key words：nanomaterials；carbon dioxide；gels；adsorption温室效应造成的全球气候问题已成为人类进入21世纪以来面临的最大生存挑战。

13Nonlinear DynamicsAn International Journal of Nonlinear Dynamics and Chaos in Engineering SystemsISSN 0924-090X Volume 74Combined 1-2Nonlinear Dyn (2013) 74:133-142DOI 10.1007/s11071-013-0953-1Explicit ultimate bound sets of a newhyperchaotic system and its application in estimating the Hausdorff dimension Pei Wang, Yuhuan Zhang, Shaolin Tan & Li WanYour article is protected by copyright and all rights are held exclusively by Springer Science +Business Media Dordrecht. This e-offprintis for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further depositthe accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publicationor later and provided acknowledgement is given to the original source of publicationand a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at ”.13Nonlinear Dyn(2013)74:133–142DOI10.1007/s11071-013-0953-1O R I G I NA L PA P E RExplicit ultimate bound sets of a new hyperchaotic system and its application in estimating the Hausdorff dimension Pei Wang·Yuhuan Zhang·Shaolin Tan·Li WanReceived:30March2013/Accepted:7May2013/Published online:21May2013©Springer Science+Business Media Dordrecht2013Abstract Ultimate bound estimation of chaotic sys-tems is a difficult yet interesting mathematical ques-tion.At present,explicit ultimate bound sets can be analytically obtained only for some special chaotic systems,and few results are known for hyper-chaotic ones.In this paper,through the Lagrange multiplier method and set operations,one derives two kinds of explicit ultimate bound sets for a novel hyperchaotic system.Based on the estimated result and optimiza-tion method,one further estimates the Hausdorff di-mension of the hyperchaotic attractor.Numerical sim-ulations show the effectiveness and correctness of the conclusions.The investigations enrich the related re-sults for the hyperchaotic systems,and provide sup-port for the assertion:hyperchaotic systems are ulti-mately bounded.P.Wang( )·Y.ZhangSchool of Mathematics and Information Sciences,Henan University,Kaifeng475004,Chinae-mail:wp0307@S.TanLSC,Institute of Systems Science,Academyof Mathematics and Systems Science,Chinese Academyof Sciences,Beijing100190,P.R.ChinaL.WanSchool of Mathematics and Computer Science,Wuhan Textile University,Wuhan430073,P.R.China Keywords Hyperchaotic system·Ultimate bound·Lagrange multiplier method·Optimization·Hausdorff dimension1IntroductionChaotic systems are ultimately bounded[1–13].In that,the phase portraits of the systems will be ulti-mately trapped in some compact sets[4,9,10].Ul-timate bound estimation of chaotic systems is a fun-damental mathematical problem.However,it is diffi-cult to obtain explicit ultimate bound sets for chaotic systems,especially for high dimensional ones.There are two main difficulties.On the one hand,it is fun-damentally difficult tofind an appropriate Lyapunov function[10].On the other hand,if one couldfind an appropriate Lyapunov function for a certain chaotic system,it is still difficult to derive the explicit ultimate bound sets for the chaotic system,especially for high dimensional ones.Following,one briefly overviews some recent pro-gresses on the ultimate bound estimation of the hy-perchaotic systems.In[8],the authors prove that the hyper-chaotic Lorenz–Haken system is ultimately bounded,but no explicit bound sets were derived due to the complexity of the optimization problem. And in[9],we investigate the ultimate boundness of the hyperchaotic Lorenz–Stenflo system,through the method of dimension reduction,we have obtained some explicit bound sets for the hyperchaotic system.134P.Wang et al.Recently,based on the optimization method and ma-trix theory,we have investigated the ultimate bound-ness of the general quadratic autonomous dynamical systems,and derive a sufficient condition for the gen-eral system.In addition,a class of high dimensional quadratic autonomous chaotic systems are proved to be ultimately bounded[10].Though much works have been done,and one couldfind some appropriate Lya-punov functions for some systems by following[10], it is still technically difficult to obtain explicit bound sets for the high dimensional chaotic systems[10].For example,in[10],if one wants to derive explicit ulti-mate bound sets for the high dimensional dynamical systems,one has to solve some high dimensional op-timization problems.However,the high dimensional optimization problems are always the main obsta-cle.Ultimate bound sets have important applications in chaos control and its synchronization.It can also be applied in estimating the fractal dimensions of chaotic attractors,such as the Hausdorff dimension and the Lyapunov dimension of chaotic attractors[12,14–18]. The Hausdorff dimension of some3D chaotic attrac-tors have been estimated[12,14–18].It is intriguing to estimate the Hausdorff dimension of the hyperchaotic attractors by using their ultimate bound sets.Motivated by the above two issues,based on the optimization method and the Lagrange multiplier method,we show a possible way to explore the ul-timate bound sets for hyperchaotic systems[13].And discuss the application of the ultimate bound sets in the estimation the Hausdorff dimension of the hyper-chaotic attractors.The rest of the paper is organized as follows.Section2investigates the ultimate bound sets for the new hyperchaotic system.The applications of the ultimate bound sets in the estimation of the Haus-dorff dimension are discussed in Sect.3.Concluding remarks are presented in Sect.4.2Ultimate bound estimation of the new hyperchaotic systemFirst of all,we introduce the existing general method for bound estimation.A general method of ultimate bound estimation has been presented in our previous paper[10],the main conclusion is as follows.The general quadratic autonomous dynamical sys-tem(GQADS)[9,10,19]is described by ˙X=AX+ni=1x i B i X+C,(1)where X=(x1,x2,...,x n)T∈R n is the state vec-tor,A=(a ij)n×n∈R n×n,B i=(b ijk)n×n∈R n×n, and C=(c1,c2,...,c n)T∈R n.Also,all elements of B1,B2,...,B n satisfy b kij=b jik for i,j,k= 1,2,...,n.System(1)contains all quadratic au-tonomous chaotic systems as its special cases,such as the well-known Lorenz system,the Chen system, the unified system,the Lüsystem,the Rössler system [19,21–23].Following,P>0means that matrix P is positive definite.Similarly,P<0is negative definite.A suf-ficient condition obtained in[10]for system(1)is as follows.Lemma1[10]Suppose that there exists a real sym-metric matrix P>0and a vectorμ∈R n such thatQ=A T P+P A+2B T1PμT,B T2PμT,...,B T n PμTT<0(2)andni=1x i X TB T i P+P B iX=0(3)for any X=(x1,x2,...,x n)T∈R n,then system(1)is bounded and has the following ultimate bound set:Ω=X∈R n|(X+μ)T P(X+μ)≤R max,(4) where R max is a real number to be determined by max(X+μ)T P(X+μ)s.t.X T QX+2μT P A+C T PX+2C T Pμ=0.(5)Lemma1only gives a sufficient condition.In[10], boundness of some4D,6D,or even9D chaotic sys-tems have been discussed,but no explicit ultimate bound sets have been obtained for these high dimen-sional chaotic system.In real world,the optimization problem(5)is very difficult to be analytically solved. In the following,taken a newly proposed hyperchaotic system as an example,we introduce a possible way to explore the explicit ultimate sets of the high dimen-sional chaotic systems.Explicit ultimate bound sets of a new hyperchaotic system and its application in estimating135 Recently,a new hyperchaotic system is proposedin[13],which is described as⎧⎪⎪⎪⎪⎨⎪⎪⎪⎪⎩˙x1=a(x2−x1),˙x2=bx1−x1x3−cx2+x4,˙x3=x1x2−dx3,˙x4=−kx2−rx4.(6)When a=12,b=23,c=1,d=2.1,k=6,r=0.2, system(6)is hyperchaotic.Rewrite system(6)in the form of system(1),one hasA=⎡⎢⎢⎣−a a00b−c0100−d00−k0−r⎤⎥⎥⎦,B1=⎡⎢⎢⎣0000 00−1/20 01/200 0000⎤⎥⎥⎦,B2=⎡⎢⎢⎣000000001/20000000⎤⎥⎥⎦,B3=⎡⎢⎢⎣0000−1/200000000000⎤⎥⎥⎦,B4=04×4,C=04×1.Suppose p11>0,p22>0,and takeP=⎡⎢⎢⎣p110000p220000p2200001kp22⎤⎥⎥⎦,μ=0,0,−ap11+bp22p22,0T.Then P,μsatisfy Eqs.(2)and(3)in Lemma1.And one hasQ=⎡⎢⎢⎣ap110000cp220000dp220000rkp22⎤⎥⎥⎦,M=0,0,d(ap11+bp22),0T.By Lemma1and the following techniques to solvethe high dimensional optimization problem,one canderive the following proposition for system(6).Proposition1Suppose that a>0,c>0,d>0,r>0,p ii>0,denoteΩ=X∈R4|p11x21+p22x22+p22x3−ap11+bp22p222+p22kx24≤R max.(7)ThenΩis an ultimate bound set of system(6),whereR max=⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩d2(ap11+bp22)24p22a(d−a),0<a<min{c,r},d≥max{a+c,a+r};d2(ap11+bp22)24p22c(d−c),0<c<min{a,r},d≥max{a+c,c+r};d2(ap11+bp22)24p22r(d−r),0<r<min{a,c},d≥max{a+r,c+r};(ap11+bp22)2p22,otherwise.Proof From the Lemma1,R max can be determined bysolving the following optimization problem:max V=p11x21+p22x22+p22x3−ap11+bp22p222+p22kx24s.t.ap11x21+cp22x22+dp22x3−ap11+bp222p222+rp22kx24=(ap11+bp22)2d4p22.(8)Letθ=(ap11+bp22)/(2√p22),√p11x1=y1,√p22x2=y2,√p22x3−2θ=y3,√p22/kx4=y4,then the optimization problem(8)is equivalent toproblem:max H=y21+y22+y23+y24s.t.ay21+cy22+d(y3+θ)2+ry24=dθ2.(9)136P.Wang et al.By the Lagrange multiplier method,define:L=y21+y22+y23+y24−ωay21+cy22+d(y3+θ)2+ry24−dθ2,(10)and let:⎧⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎩∂L∂y1=2y1(1−ωa)=0,∂L∂y2=2y2(1−ωc)=0,∂L∂y3=2y3(1−ωd)−2ωdθ=0,∂L∂y4=2y4(1−ωr)=0,∂L∂ω=−(ay21+cy22+d(y3+θ)2+ry24−dθ2)=0.(11)(i)Whenω=1/a,and d≥2a,a>0,one derivestwo stationary points:y∗1,y∗2,y∗3,y∗4=±dθ√d−2a(d−a)√a,0,dθa−d,0,andH1=Hy∗1,y∗2,y∗3,y∗4=d2(ap11+bp22)24p22a(d−a).(ii)Whenω=1/c,and d≥2c,c>0,one hasy∗1,y∗2,y∗3,y∗4=0,±dθ√d−2c(d−c)√c,dθc−d,0,andH2=Hy∗1,y∗2,y∗3,y∗4=d2(ap11+bp22)24p22c(d−c).(iii)Whenω=1/r,and d≥2r,r>0,one obtainsy∗1,y∗2,y∗3,y∗4=0,0,dθr−d,±dθ√d−2r(d−r)√r,andH3=Hy∗1,y∗2,y∗3,y∗4=d2(ap11+bp22)24p22r(d−r).(iv)Whenω=1/a,ω=1/c,ω=1/r,one hasy∗1,y∗2,y∗3,y∗4=(0,0,−2θ,0)ory∗1,y∗2,y∗3,y∗4=(0,0,0,0),and H4=4θ2or H5=0.Summarizing(i)–(iv),since the constrained con-dition in Eq.(9)is an ellipsoid,the objective func-tion H can achieve its minimum and maximumvalues.Obviously,H5=0is the minimum value of H.Denote the maximum values of H as H max,then,for0<a<min{c,r},d≥max{a+c,a+r},H max=H1=max{H1,H2,...,H5}.For0<c< min{a,r},d≥max{a+c,c+r},H max=H2= max{H1,H2,...,H5}.For0<r<min{a,c},d≥max{a+r,c+r},H max=H3=max{H1,H2, ...,H5}.Otherwise,H max=H4=max{H1,H2, ...,H5}.For simplicity,one denotes S1={(a,c, d,r)∈R4+|0<a<min{c,r},d≥max{a+c, a+r}},S2={(a,c,d,r)∈R4+|0<c<min{a,r}, d≥max{a+c,c+r}},S3={(a,c,d,r)∈R4+|0<r<min{a,c},d≥max{a+r,c+r}},S4=R4+− 3i=1S i.Thus,one hasH max=⎧⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎩d2θ2a(d−a),(a,c,d,r)∈S1;d2θ2c(d−c),(a,c,d,r)∈S2;d2θ2r(d−r),(a,c,d,r)∈S3;4θ2,(a,c,d,r)∈S4.(12) Substitutedθfor(ap11+bp22)/(2√p22)in H max, and noted that V max=H max=R max,the proof is thus completed. Remark1The above discussions for the hyperchaotic system can be extended to other systems.It is noted that,in work[13],the authors also discussed the boundness of this hyperchaotic system.However,the estimated results only hold for a>1,c>1,d>1, and the estimated results are very rough and also too large.For example,when a=12,b=23,c=1, d=2.1,k=5,r=0.2,a bound estimated in[13]isx21+x22+(x3−35)2+0.2x24≤6138.9,whereas ourresult shows that x21+x22+(x3−35)2+0.2x24≤1225, which is obviously more accurate and conservative than the result in[13].Moreover,our results hold for any a>0,c>0,d>0,r>0.Remark2In work[10],we only demonstrate that some high dimensional chaotic systems are ultimately bounded and have ellipsoidal bound sets;no explicit bound sets have been analytically calculated for4D and higher dimensional dynamical systems.Here,for the new hyperchaotic system,one derives an explicit ultimate bound set through the Lagrange multiplierExplicit ultimate bound sets of a new hyperchaotic system and its application in estimating137 Fig.1The phase portraits and ultimate bound of system(6)in different3D projection planes,where a=12,b=23,c=1,d=2.1,k=5,r=0.2,p11=p22=1method.As far as we know,as to high dimensionalchaotic systems,there are only few reports with ex-plicit bound estimation in existing references[9].To verify the correctness of the conclusion inProposition1,one sets a=12,b=23,c=1,d=2.1,k=5,r=0.2,p11=p22=1,then R max=(ap11+bp22)2/p22,the phase portraits and ultimatebound of the system(6)in different3D projectionplanes are shown in Fig.1,where all phase por-traits are ultimately trapped by the estimated ellip-soid.Figure2shows the case for a=12,b=23,c=1,d=15,k=5,r=0.2,p11=p22=1,whereR max=d2(ap11+bp22)2/[4p22r(d−r)].Here,thesolution of system(6)is a periodic orbit,and the peri-odic orbit is also encircled by the estimated result.The estimated result in Proposition1is a set withfree parameters p11,p22,based on the set operations,one can further derive a more conservative boundaryfor each state variable in the system(6).Corollary1Suppose that a>0,c>0,d>0,r>0,then based on Proposition1,the smallest range foreach state variable in the system(6)is given as fol-lows:|x1|≤⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩√bd√d−a,(a,c,d,r)∈S1;√abd√c(d−c),(a,c,d,r)∈S2;√abd√r(d−r),(a,c,d,r)∈S3;2√(a,c,d,r)∈S4;(13)|x2|≤⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩bd2√a(d−a),(a,c,d,r)∈S1;bd2√c(d−c),(a,c,d,r)∈S2;bd2√r(d−r),(a,c,d,r)∈S3;b,(a,c,d,r)∈S4;(14)|x3−b|≤⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩bd2√a(d−a),(a,c,d,r)∈S1;bd2√c(d−c),(a,c,d,r)∈S2;bd2√r(d−r),(a,c,d,r)∈S3;b,(a,c,d,r)∈S4;(15)138P.Wang etal. Fig.2The phase portraits and ultimate bound of system(6)in different3D projection planes,where a=12,b=23,c=1,d=15,k=5,r=0.2,p11=p22=1|x4|≤⎧⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎩bd√k2√a(d−a),(a,c,d,r)∈S1;bd√k2√c(d−c),(a,c,d,r)∈S2;bd√k2√r(d−r),(a,c,d,r)∈S3;b√k,(a,c,d,r)∈S4.(16)Proof From Proposition1,we also denoteθ=(ap11+bp22)/(2√p22),then one has|x1|≤R max/p11,|x2|≤R max/p22,|x3−2θ/√p22|≤R max/p22,|x4|≤max22whereR max=⎧⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎩d2θ2a(d−a),(a,c,d,r)∈S1;d2θ2c(d−c),(a,c,d,r)∈S2;d2θ2r(d−r),(a,c,d,r)∈S3;4θ2,(a,c,d,r)∈S4.(17)Therefore,tofind the upper bound for each state vari-able,it is sufficient tofind the lower bound ofθ/√p11andθ/√p22.Since p11,p22are arbitrary positive numbers,andθ√p11=a2√p11√p22+b2√p22√p11≥√ab,therefore,one can obtain the bound for x1as shown inEq.(13).For the bound of x2,x3,x4,there is a same term:θ√p22=12(εa+b),whereε=p11/p22,which can also be arbitrary.Tak-ingε=1/N(N=1,2,...),and noted that the inter-sections of ultimate bound sets are also an ultimatebound sets[2].DenoteR maxp22=121Na+bc0,Explicit ultimate bound sets of a new hyperchaotic system and its application in estimating 139Table 1Comparison between the differentestimated results and actual bound for the system (6),where a =12,b =23,c =1,d =2.1,k =5,r =0.2,p 11=p 22=1Variables Proposition 1Corollary 1Actual bound x 1(−35.00,35.00)(−33.23,33.23)(−15.85,16.57)x 2(−35.00,35.00)(−23.00,23.00)(−19.81,20.89)x 3(0.00,70.00)(0.00,46.00)(1.91,38.87)x 4(−78.26,78.26)(−51.43,51.43)(−39.20,38.26)where c 0is a constant,which can be easily derived from the expression of R max asc 0=⎧⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎩d √a(d −a),(a,c,d,r)∈S 1;d √c(d −c),(a,c,d,r)∈S 2;d √r(d −r),(a,c,d,r)∈S 3;2,(a,c,d,r)∈S 4.(18)Since∞ N =1x 2∈R ||x 2|≤12 1Na +bc 0= x 2∈R ||x 2|≤12bc 0,one can derive a smaller bound set for x 2as shown in Eq.(14).Similarly,one obtains more conservative bounds for x 3and x 4as shown in Eqs.(15)–(16).The proof is thus completed. For a =12,b =23,c =1,d =2.1,k =5,r =0.2,p 11=p 22=1,Table 1shows the estimated bound-ary for each state variable in Proposition 1and Corol-lary 1,as well as the actual bound from numerical so-lution of the system (6).Obviously,the estimated re-sult in Corollary 1is more conservative than Proposi-tion 1.And the estimated ranges all contain the actual bound of each state variable,which demonstrate the correctness and advantage of Corollary 1.It is noted that the bound for each state variable in Corollary 1is only determined by the system param-eters.Therefore,it is very convenient to be applied in chaos control and synchronization.3Application in the estimation of the Hausdorff dimensionIn this section,one discusses the application of ulti-mate bound estimation in estimating the Hausdorff di-mension of the hyperchaotic attractor.The Hausdorffdimension is a very important characteristic quantity for chaotic systems,which plays the role as period and amplitude to an oscillator.Denote J as the Jacobian of the hyperchaotic sys-tem (6).For some positive definite matrix Θ,suppose λ1≥λ2≥λ3≥λ4to be solutions of the following characteristic equation:det ΘJ +J T Θ−λΘ=0,(19)where λi are functions of x 1,x 2,x 3,x 4,and Θis inde-pendent of the state vector.From the works of Leonov [14–17]and Pogromsky et al.[12,18],one has the following lemma.Lemma 2[12,14,17,18]Suppose that for some posi-tive definite matrix Θ,there exists a number σ∈[0,1),such thatλ1(x 1,x 2,x 3,x 4)+λ2(x 1,x 2,x 3,x 4)+λ3(x 1,x 2,x 3,x 4)+σλ4(x 1,x 2,x 3,x 4)<0(20)for all x 1,x 2,x 3,x 4in some trapping region ,then the Hausdorff dimension dim H K of the hyperchaotic at-tractor is upper bounded by 3+σ.Remark 3In existing works [12,14,17,18],re-searchers mainly considers the Hausdorff dimension estimation for 3D chaotic systems.To the best of our knowledge,Hausdorff dimension estimation for hy-perchaotic systems are rarely investigated,which is mainly because explicit ultimate bound sets for hyper-chaotic systems could not be easily obtained.Following,by using Lemma 2,for a >0,c >0,d >0,r >0,one estimates the upper bound of the Hausdorff dimension of the hyperchaotic system (6).The Jacobian of the hyperchaotic system isJ =⎡⎢⎢⎣−a a 00b −x 3−c −x 11x 2x 1−d 00−k 0−r⎤⎥⎥⎦.140P.Wang et al.Taking Θ=diag {1,1,1,1/k },and from (19),λis the eigenvalue of matrix ΘJ Θ−1+J T=⎡⎢⎢⎣−2a a +b −x 3x 20a +b −x 3−2c 00x 20−2d 0000−2r⎤⎥⎥⎦.Therefore,λ1(X)+λ2(X)+λ3(X)+λ4(X)=−2(a +c +d +r)<0.(21)It follows that λ4(X)<0for any X =(x 1,x 2,x 3,x 4)T .From (20)and (21),one has σ>1−2(a +c +d +r)|λ4(X)|.(22)Thus,to find the upper estimation of dim H K ,it is sufficient to find the lower bound λmin 4of λ4(X).Then the upper bound of the Hausdorff dimension is dim H K ≤4−2(a +c +d +r)|λmin 4|.(23)Following,one estimates λmin 4.The characteristicequation from (19)is(λ+2r)(λ+2a)(λ+2c)(λ+2d)−(λ+2c)x 22−(λ+2d)(a +b −x 3)2 =0.(24)It follows that λ=−2r or (λ+2a)(λ+2c)(λ+2d)−(λ+2c)x 22−(λ+2d)(a +b −x 3)2=0.(25)Letη1=2(a +c +d),η2=4(ac +ad +cd)− x 22+(a +b −x 3)2 ,η3=8acd −2 cx 22+d(a +b −x 3)2 .Then,Eq.(25)degenerates into λ3+η1λ2+η2λ+η3=0.(26)Denote R 1=⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩d 2(a +b)24a(d −a),(a,c,d,r)∈S 1;d 2(a +b)24c(d −c),(a,c,d,r)∈S 2;d 2(a +b)24r(d −r),(a,c,d,r)∈S 3;(a +b)2,(a,c,d,r)∈S 4.Then,from Proposition 1,set p 11=1,p 22=1,onederives 0≤x 22+(a +b −x 3)2≤R 1.Therefore,4(ac +ad +cd)−R 1≤η2≤4(ac +ad +cd).Further denote β=max {c,d },one obtains 8acd −2βR 1≤η3≤8acd .Thus,one can derive the minimum value of λin Eq.(26)from the following optimization problem:min λs.t.λ3+η1λ2+η2λ+η3=0;η1=2(a +c +d);4(ac +ad +cd)−R 1≤η2≤4(ac +ad +cd);8acd −2βR 1≤η3≤8acd.(27)For example,for a =12,b =23,c =1,d =2.10,k =6,r =0.20,one gets η1=30.20,−1067.80≤η2≤157.20,−4943.40≤η3≤201.60.One obtains λ=−51.15from optimization problem (27).There-fore,λmin 4=min {−51.15,−2r }=−51.15,and dim H K ≤4−2(a +c +d +r)|λ4|≈3.40.That is,for a =12,b =23,c =1,d =2.10,k =6,r =0.20,the upper bound of the Hausdorff dimen-sion of the hyperchaotic attractor is 3.40.For a =12,b =23,c =1,d =2.10,k =6,r =0.20,through the time series of the system,one can also compute the upper bound of the Hausdorff di-mension of the hyperchaotic attractor.For example,from time series of x 2(t)and x 3(t),one can derive −166.83≤η2≤156.99,−363.96≤η3≤200.88.And from the optimization problem,which is similar to (27),one can derive λmin 4=−35.11.Thus,one has dim H K ≤pared with the result by using the estimated ultimate bound set,the estimated result dim H K ≤3.40is very close to dim H K ≤3.13.This demonstrates the correctness of our result.It is noted that one has to numerically solve the differential equa-tions by the time series method,which is time consum-Explicit ultimate bound sets of a new hyperchaotic system and its application in estimating141ing and highly parameter-dependent,therefore,the ad-vantage of ultimate bound estimation in the estimation of the Hausdorff dimension is remarkable.4ConclusionsThis paper has investigated the ultimate bound es-timation of a newly proposed hyper-chaotic system. We introduce a possible way to derive explicit ulti-mate bound sets for high dimensional chaotic systems. One obtains an explicit ellipsoidal bound for such sys-tem.And based on the ellipsoidal bound set and set operations,one further obtains a more conservative boundary for each variable in the hyperchaotic system, which only relies on the system parameters.Numeri-cal simulations show the correctness of our results.The investigations provide support for the assertion that:chaotic systems are ultimately bounded[1–13]. Ultimate bounds of chaotic systems can be applied in chaos control and synchronization,which has been ex-tensively investigated[12,20].It can also be used to estimate the Hausdorff dimension and Lyapunov di-mension of the strange attractor.Based on the esti-mated bound set,one concludes that the Hausdorff dimension of the hyperchaotic attractor is not higher than3.40.At present,ultimate bound estimations of many chaotic systems are still a difficult mathematical prob-lem,such as the well-known Chen system[21],Lüsystem[11,22,23]and multi-scroll chaotic attractors [24,25].How tofind explicit ultimate bound sets for these systems is still an unsolved question.It is also intriguing to explore other possible applications of ul-timate bound estimations,and estimating the ultimate bound for other dynamical systems,such as stochastic neural networks[26,27].Acknowledgements This work was supported by the Science Foundation of Henan University under Grants2012YBZR007, and also partly supported by the National Natural Science Foun-dation of China under Grants60804039,60974081,11271295, and60821091.This work was also partly supported by the Sci-ence and Technology Research Projects of Hubei Provincial De-partment of Education under Grants:D2*******. 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第37卷 第11期 高 师 理 科 学 刊 Vol. 37 No.11 2017年 11月 Journal of Science of Teachers′College and University Nov. 2017文章编号：1007-9831（2017）11-0038-03N-十二烷基-3-苯胺甲酰甲基咪唑氯盐的合成向韦佳，王丽艳，杨超（齐齐哈尔大学 化学与化学工程学院，黑龙江 齐齐哈尔 161006）摘要：以苯胺、氯乙酰氯为原料合成氯乙酰苯胺（中间体A）．以咪唑、1-溴代十二烷为原料合成N-十二烷基咪唑（中间体B）．中间体A与中间体B反应合成目标产物N-十二烷基-3-苯胺甲酰甲基咪唑氯盐．通过单因素实验，考察了中间体A与中间体B投料比、反应温度及反应时间对目标产物收率的影响，利用1HNMR和IR对目标产物结构进行表征．咪唑盐；单因素实验；合成关键词：鎓中图分类号：O621.3 文献标识码：A doi：10.3969/j.issn.1007-9831.2017.11.010Synthesis of N-dodecyl-3-aniline formyl methyl imidazole chlorideXIANG Wei-jia，WANG Li-yan，YANG Chao（School of Chemistry and Chemical Engineering，Qiqihar University，Qiqihar 161006，China）Abstract：Chloroacetanilide（intermediate A）was synthesized with aniline and chloroacetyl chloride as materials．N-dodecyl midazole（intermediate B）was synthesized with imidazole and 1-bromodecane as materials．N-dodecyl -3-aniline formyl methyl imidazole chloride was synthesized from intermediate A and intermediate B．The influences of molar ratio of intermediate A and intermediate B，reaction temperature and reaction time on the yield of target product were investigated via single factor experiment．The structure of the target products was confirmed by 1HNMR and IR．Key words：imidazolium；single factor experiment；synthesisN-十二烷基-3-苯胺甲酰甲基咪唑氯盐是杂环类表面活性剂，杂环类表面活性剂种类多[1-2]、性能广、市场利用价值高，因此应用广泛．该类表面活性剂可用作杀菌剂[3-5]，特别是在药物制剂、食品防腐剂、医疗灭菌和生物复合材料等方面有着广泛的应用[6]．此外，在洗涤剂工业[7]、石油工业[8]和农业[9]等方面也具有一定的应用价值．1 实验部分1.1 主要仪器与试剂750 Magna-IR 型傅里叶红外光谱仪（美国Nicolet 公司）；Avance-600 型超导核磁共振仪（瑞士Bruker 公司）等．咪唑、氯乙酰氯、1-溴代十二烷、二氯甲烷、N，N-二甲基甲酰胺（DMF）等（天津市科密欧试剂有限公司）．收稿日期：2017-06-19基金项目：2017年省级重点大学生创新创业训练计划资助项目（201710221001）作者简介：向韦佳（1995-），女，重庆丰都人，在读本科生．E-mail：1843751066@通信作者：王丽艳（1971-），女，黑龙江讷河人，教授，博士，主要从事表面活性剂方面的研究．E-mail：wlydlm@第11期 向韦佳，等：N-十二烷基-3-苯胺甲酰甲基咪唑氯盐的合成 391.2 N-十二烷基-3-苯胺甲酰甲基咪唑氯盐的合成中间体A 的合成参见文献[10]，中间体B 的合成参见文献[11]，N-十二烷基-3-苯胺甲酰甲基咪唑氯盐的合成反应方程式为 HN +Cl O N N 10HN N O N 10Cl 120℃中间体A 中间体B 目标产物 向带有搅拌装置的25 mL 三颈瓶中依次加入中间体A （0.26 g，1.5 mmol），中间体B （0.42 g，1.8 mmol），DMF 3 mL，加热搅拌，在120 ℃下反应5 h，TLC 监测反应进程．利用旋转蒸发仪旋蒸除DMF，得到米白色固体粗产物，用乙酸乙酯与乙醇混合溶液对粗产物进行重结晶，晶体过滤后在烘箱中35 ℃烘干至恒重，得白色固体粉末N-烷基-3-苯胺甲酰甲基咪唑氯盐0.49 g，收率为80.3%，熔点为147.3~149.8 ℃． 2 结果与讨论2.1 合成工艺条件优化通过单因素实验，考查了中间体A 与中间体B 的投料比、反应温度及反应时间对目标产物收率的影响．2.1.1 反应物投料比对收率的影响 反应物投料比是影响产物收率的主要因素．反应物投料比过高或过低都不利于获得高收率的产物．当反应温度为120 ℃，反应时间为5 h 时，考察中间体A 与中间体B 投料比对收率的影响（见表1）．由表1 可见，随着中间体B 投料量的增加，产物的收率先增加后减少．由于开始中间体B 的投料量小，反应不充分，但随着其投料量的增加，副产物也增加，后处理困难，这都导致收率不高．因此，选择较佳的投料比为1∶1.2．2.1.2 反应温度对收率的影响 反应温度是影响反应速率的主要因素．反应温度过低，反应速率缓慢；反应温度过高，副反应增多，影响收率．当n 中间体A ∶n 中间体B =1∶1.2，反应时间为5 h 时，反应温度对收率的影响见表2．由表2可见，随着反应温度的升高，目标产物的收率先增加后减少．因此，选择较佳的反应温度为120 ℃．2.1.3 反应时间对收率的影响 反应时间对产物的收率有一定的影响．当n 中间体A ∶n 中间体B =1∶1.2，反应温度为 120 ℃时，反应时间对收率的影响见表3．由表3可见，随着反应时间的增加，产物的收率先增加后趋于平稳．时间短反应不充分，但时间延长对收率影响不大．因此，选择较佳的反应时间为5 h．利用较佳工艺条件进行了3次重现性实验，平均收率为80.3%．2.2 结构表征2.2.1 目标产物的1H-NMR 数据 1H-NMR （600 MHz，CDCl 3）δH ：0.88（t，3H，J =6.9 Hz，-CH 3），1.25-1.33（m，20H，-（CH 2）10-），4.17（t，2H，J =7.2 Hz，-N +CH 2-），5.61（s，2H，O=C-CH 2N +-），7.06-7.58（m，5H，苯环上H），7.77（d，2H，J =7.8 Hz，咪唑环4，5位H），10.08（s，1H，咪唑环2位H），11.49（s，1H，-NH-）．2.2.2 目标产物的IR 数据 IR （KBr），ν/cm -1：3 189 cm -1处为酰胺键中的N-H 伸缩振动吸收峰；3 131 cm -1，3 050 cm -1处为咪唑环和苯环上C-H 伸缩振动吸收峰；2 919，2 850 cm -1处为-CH 3，-CH 2-的对称和反对称伸缩振动吸收峰；1 697 cm -1 处为酰胺键中C=O 伸缩振动吸收峰；1 562 cm -1 处为酰胺键中N-H 弯曲振动吸收峰；1 602，1 501 cm -1 处为苯环骨架振动吸收峰；1 445 cm -1 处为-CH 2-的剪切式振动吸收峰；721 cm -1处为长烷基链-（CH 2）n -的弯曲振动吸收峰． 表1 反应物投料比对收率的影响 中间体A∶中间体B 项目 1∶1.0 1∶1.2 1∶1.5 1∶2 收率/% 69.2 80.9 78.3 65.6 表2 反应温度对收率的影响 反应温度/℃ 项目 100 110 120 130 收率/% 43.7 60.2 81.6 79.1 表3 反应时间对收率的影响 反应时间/h 项目 3 4 5 6收率/% 57.2 65.2 82.7 83.240 高 师 理 科 学 刊 第37卷3 结论本文通过两步反应合成N-十二烷基-3-苯胺甲酰甲基咪唑氯盐．通过单因素考察，确定了目标产物合成的较佳工艺条件为：中间体A，B的投料比1∶1.2，反应温度为 120 ℃，反应时间为5 h，反应收率为80.3%．通过1HNMR 和 IR 对目标产物结构进行表征，证明合成的为目标产物．参考文献：[1] Shi L J，Li N，Yan H，et al．Aggregation behavior of long chain N-Aryl Imidazolium bromide in aqueous solution[J]．Langmuir，2011，27（5）：1618-1625[2] Chauhan V，Singh S，Bhadani A．Synthesis，characterization and surface properties of long chainβ-hydroxy-γ-alkyloxy-N-methylimidazolium 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