主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2026, Vol. 45 ›› Issue (7): 13-21.

• • 上一篇    下一篇

化学气体灭火剂性能参数与分子结构构效关系的研究进展

张肖, 周晓猛   

  1. (中国民航大学 民航热灾害防控与应急重点实验室,天津 300300)
  • 收稿日期:2025-07-11 修回日期:2025-11-06 出版日期:2026-07-15 发布日期:2026-07-15
  • 作者简介:张肖,中国民航大学副教授,主要从事洁净高效化学气体灭火技术研究,天津市东丽区津北公路2898号,300300。
  • 基金资助:
    国家重点研发计划项目(2023YFC3010201)

Research progress on structure-activity relationship between performance parameters and molecular structures of chemical gas fire extinguishing agents

Zhang Xiao, Zhou Xiaomeng   

  1. (Key Laboratory of Civil Aviation Thermal Hazards Prevention and Emergency Response, Civil Aviation University of China, Tianjin 300300, China)
  • Received:2025-07-11 Revised:2025-11-06 Online:2026-07-15 Published:2026-07-15

摘要: 针对清洁高效化学气体灭火剂需满足的环境友好、灭火高效、存储稳定、安全低毒等性能要求,本文详细总结新型气体灭火剂应用性能参数的快速评测方法,深入分析化学气体灭火剂分子结构对其应用性能的影响规律,探讨分子结构与应用性能间的定量构效关系、气体灭火剂关键性能参数预测方法和分子优选技术,从而实现大量潜在灭火剂快速靶向优选,指导具有市场应用潜力的新型化学气体灭火剂的高效研发。

关键词: 化学气体灭火剂, 分子优选, 构效关系, 预测模型

Abstract: In view of the performance requirements that ideal chemical gas fire extinguishing agents need to meet, such as environmental friendliness, high fire extinguishing efficiency, stable storage, safety and low toxicity, this paper summarizes in detail the rapid evaluation methods for the application performance parameters of new gas fire extinguishing agents, deeply analyzes the influence law of the molecular structure of chemical gas fire extinguishing agents on their application performance. Explore the quantitative structure-activity relationship between molecular structure and application performance, the prediction model of key performance parameters of gas fire extinguishing agents, and the molecular optimization technology, so as to achieve rapid targeted optimization of massive potential fire extinguishing agents and guide the efficient research and development of new chemical gas fire extinguishing agents with market application potential.

Key words: chemical gas fire extinguishing agents, molecular optimization, structure-activity relationship, prediction model