Structural Characterization and Retention Time Prediction for Components of Essential Oil of Meconopsis Integrifolia Flowers

A molecular structural characterization (MSC) method called reduced molecular electronegativity-distance vector (MEDVR) was used to describe the molecular structures of 55 components of meconopsis integrifolia flowers. By use of stepwise multiple regression (SMR) and partial least square (PLS) methods, a model with the correlation coefficient (R1) of 0.987 and the standard deviation (SD1) of 1.377 could be obtained. Then through multiple linear regression (MLR), another model with the correlation coefficient (R2) of 0.989 and standard deviation (SD2) of 1.395 could be constructed. Furthermore, in virtue of variable screening by the stepwise multiple regression technique (SMR), 8 vectors were selected to build up another model with its correlation coefficient (R3) and standard deviation (SD3) of 0.989 and 1.366, respectively. Then all the three models were evaluated by performing cross-validation with the leave-one-out (LOO) procedure, and the correlation coefficients (QCV) were 0.981, 0.976 and 0.979, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.     

Structural Characterization of Volatile Components of Rosa Banksiae Ait for Estimation and Prediction of Their Linear Retention Indices and Retention Times

The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.     

多元络合物光度法测定冶炼残渣中痕量铟

本文研究了InI-4与碱性染料反应形成多元离子缔合物用于痕量铟的测定.探讨了酸度、温度及表面活性剂对形成多元配合物反应的影响,比较了单显色剂(乙基罗丹明B)与多显色剂(乙基罗丹明B-丁基罗丹明B)的测定结果,单显色剂测定竖罐炼锌焦结物和蒸馏电吸尘焦结物中铟含量的结果分别为2.49 μg/g(RSD为4.72%,n=4)和0.920 μg/g(RSD为4.94%,n=4);双显色剂体系测定竖罐炼锌焦结物和蒸馏电吸尘焦结物中铟含量的结果分别为2.50 μg/g(RSD为1.37%,n=4)和0.98 μg/g(RSD为1.73%,n=4).方法可用于冶炼残渣中铟的测定.     

基于分子参数的药物小肠吸收预测模型

选择100个化合物作为数据集,随机选取其中80个为训练集,其他分子为验证集,并为每个化合物分子计算了30个参数.通过采用五种不同多元线性回归分析方法对其训练模拟,建立了数学模型,并用验证集检验了所建模型的预测能力.结果发现向后筛选法为最优小肠吸收建模方法.由该法所建模型的统计结果良好(R2>0.80),应用于验证集时也表现出较强预测能力.该模型确定了对小肠吸收影响较大的分子参数,有助于指导进一步的新药筛选和开发.     

无机化学多元信息化教学模式的构建与实践研究

针对现代大学生的移动阅读习惯和信息化学习需求,构建了无机化学多元信息化教学模式。介绍了构建这种教学模式所需微信公众号、微助教和QQ群作业的使用方法。初步探讨了无机化学多元信息化教学模式在教学中的实践效果,弥补了传统教学中的不足。     

自适应小波算法用于近红外光谱的多元校正

实现了一种构建自适应小波滤波器的方法,并将其用于近红外光谱数据的多元校正。该方法根据一定的目标函数,针对信号的特性自适应地构造小波滤波器。用该法构建的滤波器对烟草样品的近红外光谱进行压缩,并将压缩后的数据采用偏最小二乘法建模,实现了烟草样品常规组分的定量分析。     

借鉴梅西大学经验开展农科有机化学在线教学

Massey University is a world-leading university in remote-instruction, online and mixed teaching based on the Stream teaching platform. This paper analyzed the advantages of the Stream teaching platform, resource utilization, course format, teaching team cooperation and course evaluation in Massey University. The paper also analyzed the enlightenment of teaching experience of Massey University to undergraduate teaching. During the outbreak of COVID-19 epidemic, drawing on Massey's experience, combined with the rapid development of information technology in China, the teaching team of organic chemistry adopted the multi-teaching strategy of "teacher-centered and student-centered", and the teaching mode of "teacher-led and self-taught by students". The online teaching practice of organic chemistry was carried out in agriculture university. It has been demonstrated that this model can effectively improve teaching effects.     

基于废旧锂电池回收制备LixMO(x=0.79,0.30,0.08;M=Ni/Co/Mn)材料作为锂-氧气电池正极催化剂的电化学性能研究

锂-氧气电池因其超高的理论比容量而受到科研界的广泛关注, 但其存在较为严重的充放电极化和较差的循环稳定性等问题, 从而极大地限制其商业化进程. 因此设计出有效的正极催化剂是解决锂-氧气电池面临的这些棘手问题的必要手段. 通过对不同充电状态的废旧锂电池正极进行回收制得三种不同锂含量的多元金属氧化物Li_xMO (x=0.79, 0.30, 0.08; M=Ni/Co/Mn), 并分别用作锂-氧气电池正极催化剂. 系统研究了Li_xMO材料中锂含量及晶体结构对其电化学性能的影响. 电化学测试结果表明, 与Li_0.79MO和Li_0.08MO催化剂相比, 基于Li_0.30MO为正极催化剂的锂-氧气电池在电流密度100 mA•g^–1和限定容量800 mAh•g^–1的条件下具有较高的放电比容量(14655.9 mAh•g^–1)、较低的充电电压(3.83 V)和较高的能量转换效率(72.2%). 而且该电池体系在充放电循环140圈后充电终止电压仍低于4.3 V. 最终认为制得的Li_0.30MO材料具有优异的催化性能归因于其稳定的层状-岩盐相复合结构以及结构中富含的氧化镍相和氧空位之间的协同作用. 这些优点能够促进放电产物的可逆形成与分解, 从而提高锂-氧气电池循环性能.     

基于LASSO算法的光谱变量选择方法研究

光谱分析技术由于具有简单、快速、无损等优势,在复杂体系的定性和定量分析中得到了广泛应用。然而光谱中往往包含成百上千的波长点,有些波长点与研究的目标性质并不相关,加大了计算量并降低了模型的预测准确度。因此,在建立模型前需要进行变量选择。最小绝对收缩与选择算子（LASSO）可将回归系数收缩为0,进而达到变量选择的目的。该研究将LASSO用于三元调和油样品近红外光谱和生物样品拉曼光谱的变量选择,基于偏最小二乘（PLS）和多元线性回归（MLR）模型,分别对香油和肌氨酸的含量进行定量分析,并与无信息变量消除－PLS（UVE－PLS）、蒙特卡罗结合UVE－PLS（MCUVE－PLS）和随机检验－PLS（RT－PLS）3种变量选择方法进行比较。结果表明,基于LASSO的变量选择方法保留的变量数最少,运算速度最快。对三元调和油样品,LASSO－PLS预测的准确度最高;对生物样品,LASSO－MLR预测的准确度最高。因此,基于LASSO的变量选择算法有望在光谱分析领域中得到良好应用。