多技术关联分析在催化剂表征中的应用
Application of Multi-Technique Correlation Analysis in Catalyst Characterization
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- DOI:
- 作者:
- 魏媛媛，刘晓艺，靳丽丽，沈 刚
Wei Yuanyuan, Liu Xiaoyi , Jin Lili, Shen Gang
- 作者单位:
- 中国石油石油化工研究院大庆化工研究中心
PetroChina Petrochemical Research Institute Daqing Chemical Engineering Research Center
- 关键词:
- 多相催化；表征技术；构效关系；关联分析
multiphase catalysis; characterization techniques; structure-activity relationship; correlation analysis
- 摘要:
- 摘要：多相催化剂的性能优化与其构效关系密切相关，而构效关系的调控又依赖于全面而精准的表征技术。单一的表征技术往往只能反应催化剂的单一物化性质，存在固有局限性。文中系统介绍了 X射线衍射（XRD）、扫描电子显微镜（SEM）、氮气吸附—脱附（BET）、程序升温化学吸附（TPD/TPR）以及热重分析（TGA）等常用表征技术的基本原理及应用。重点探讨了如何通过多技术关联分析方法，对单一技术获得的信息进行交叉验证和深度融合，进而形成对催化剂结构、性质、表面活性位点、微观形貌及热稳定性的整体认识。结合典型实例和前沿研究案例，详细阐述了该策略在揭示活性中心本质、探究反应机理以及催化剂失活原因等方面的应用，并对原位动态表征技术与机器学习辅助数据分析等前沿发展方向进行了展望。
Abstract: The performance optimization of multiphase catalysts is closely related to their structure-activity relationships, and theregulation of these relationships relies on comprehensive and precise characterization techniques. Single characterization techniquesoften only reflect certain physicochemical properties of the catalyst and have inherent limitations. This paper systematicallyintroduces the basic principles and applications of commonly used characterization techniques, including X-ray diffraction (XRD),scanning electron microscopy (SEM), nitrogen adsorption-desorption (BET), temperature-programmed chemisorption (TPD/ TPR),and thermogravimetric analysis (TGA). It focuses on how to use multi-technique correlation analysis methods to cross-validate anddeeply integrate information obtained by single techniques, thereby forming a comprehensive understanding of the catalyst'sstructure, properties, surface active sites, microscopic morphology, and thermal stability. Using typical research examples andcutting-edge cases, the paper elaborates on the application of this strategy in revealing the nature of active centers, investigatingreaction mechanisms, and understanding catalyst deactivation, and it also looks forward to emerging directions such as in situdynamic characterization techniques and machine learning-assisted data analysis.