|本期目录/Table of Contents|

[1]高月.活性化合物热稳定性预测技术研究进展[J].中国安全生产科学技术,2019,15(5):63-67.[doi:10.11731/j.issn.1673-193x.2019.05.010]
 GAO Yue.Research progress on prediction technologies for thermal stability of active compounds[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(5):63-67.[doi:10.11731/j.issn.1673-193x.2019.05.010]
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活性化合物热稳定性预测技术研究进展
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
15
期数:
2019年5期
页码:
63-67
栏目:
职业安全卫生管理与技术
出版日期:
2019-05-31

文章信息/Info

Title:
Research progress on prediction technologies for thermal stability of active compounds
文章编号:
1673-193X(2019)-05-0063-05
作者:
高月12
(1.应急管理部化学品登记中心,山东 青岛 266071;2. 中国石油化工股份有限公司青岛安全工程研究院 化学品安全控制国家重点实验室,山东 青岛 266071)
Author(s):
GAO Yue12
(1. National Registration Center for Chemicals, Ministry of Emergency Management, Qingdao Shandong 266071, China;
2. State Key Laboratory of Chemicals Safety and Control, SINOPEC Safety Engineering Institute, Qingdao Shandong 266071, China)
关键词:
活性化合物热稳定性分子结构预测定量结构-性质相关性
Keywords:
active compounds thermal stability molecular structure prediction QSPR
分类号:
X937
DOI:
10.11731/j.issn.1673-193x.2019.05.010
文献标志码:
A
摘要:
为了研究活性化合物热稳定性预测技术,调研了国内外活性化合物热稳定性预测技术的发展情况,综述了活性化合物起始放热温度、分解热、自加速分解温度的预测方法,着重介绍了定量结构-性质相关性(QSPR)研究方法在热稳定性预测领域的应用情况,分析了活性化合物热稳定性预测早期研究情况。基于量子力学计算的QSPR研究情况、QSPR数据样本的选取、分子描述符的选取、QSPR建模方法的选择,提出了热稳定性QSPR预测领域中存在的问题,并对热稳定性QSPR预测技术未来的发展方向进行了展望。
Abstract:
In order to study the prediction technologies for the thermal stability of active compounds, the development status of prediction technologies for thermal stability of active compounds at home and abroad was investigated. The prediction methods of initial exothermic temperature, decomposition heat and selfaccelerating decomposition temperature of active compounds were summarized, and the application of the quantitative structureproperty relationship (QSPR) method in the field of thermal stability prediction was introduced emphatically. The early research on the thermal stability prediction of active compounds, the QSPR research situation based on quantum mechanics calculation, the selection of QSPR data samples, the determination of molecular descriptors and the selection of QSPR modeling methods were analyzed, then the existing problems in the QSPR prediction field of thermal stability were put forward, and the future development directions for QSPR prediction technology of thermal stability were prospected.

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备注/Memo

备注/Memo:
收稿日期: 2019-01-31
作者简介: 高月,硕士,助理工程师,主要从事化学品危险性研究。
更新日期/Last Update: 2019-06-11