研究论文

芳香醚类化合物13C NMR化学位移模拟

  • 廖立敏 ,
  • 黄茜 ,
  • 李建凤
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  • 1. 内江师范学院 化学化工学院, 四川 内江 641100;
    2. 重庆大学 化学化工学院, 重庆 400044

收稿日期: 2015-09-06

  修回日期: 2016-07-09

  网络出版日期: 2016-09-05

基金资助

Youth Foundation of Education Bureau, Sichuan Province (13ZB0003), Natural Science Foundation of Education Bureau, Sichuan Province (15ZB0272).

Modeling of 13C NMR Chemical Shifts in Aromatic Ethers

  • LIAO Li-min ,
  • HUANG Xi ,
  • LI Jian-feng
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  • 1. College of Chemistry and Chemical Engineering, Neijiang Normal University, Neijiang 641100, China;
    2. College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China

Received date: 2015-09-06

  Revised date: 2016-07-09

  Online published: 2016-09-05

Supported by

Youth Foundation of Education Bureau, Sichuan Province (13ZB0003), Natural Science Foundation of Education Bureau, Sichuan Province (15ZB0272).

摘要

构建了基于分子三维结构计算的原子电负性距离矢量(atomic electronegative space distance vector, AESDV),用以描述各芳香醚类化合物分子中不同等价碳原子的化学微环境,并结合原子自身杂化状态指数(AHSI),建立了13C核磁共振定量结构波谱关系的多元线性回归模型,复相关系数(R)为0.964,标准误差(SD)为8.673.
经留一法交互检验的复相关系数(RCV)为0.948,标准误差(SDCV)为10.362.随机抽出样本进行外部检验,得到测试集的复相关系数(Rtest1Rtest2)分别为0.979和0.939,标准误差(SDtest1SDtest2)分别为6.400和10.162.研究结果表明,使用该方法所建模型具有良好的预测能力和稳定性.

本文引用格式

廖立敏 , 黄茜 , 李建凤 . 芳香醚类化合物13C NMR化学位移模拟[J]. 波谱学杂志, 2016 , 33(3) : 368 -377 . DOI: 10.11938/cjmr20160302

Abstract

Atomic electronegative space distance vector (AESDV) which characterizing the microenvironment of equivalent carbon atoms was constructed based on the three-dimensional structures of aromatic ethers. The AESDV was then employed in combination with atomic hybridation state index (AHSI) to calculate 13C NMR chemical shifts of 46 carbon atoms in 9 aromatic ethers, and to explore quantitative structure spectroscopy relationship. With multiple linear regression (MLR), a model with a correlation coefficient (R) of 0.964 and a standard deviation (SD) of 8.763 was obtained. Cross-validation of the model was performed by leave-one-out procedure (LOO), resulting in a correlation coefficient (RCV) of 0.948 and a standard deviation (SDCV) of 10.362. Furthermore, the 9 aromatic ethers were randomly assigned into a training set and a test set, respectively, to validate this model, and the correlation coefficients Rtest1 and Rtest2 were found to be 0.979 and 0.939, and the standard deviations SDtest1 and SDtest2 to be 6.400 and 10.162, respectively. The results showed that the model established can be used to predict 13C NMR chemical shifts in aromatic ethers.

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