振动光谱技术工业应用的概述  

Overview of Industrial Application of Vibration Spectroscopy Technology

作　　者：刘东奇 张红娟 李雯慧[1] 魏锋[1] Liu Dongqi;Zhang Hongjuan;Li Wenhui;Wei Feng(Key Laboratory of Optoelectronic Chemical Materials and Devices,Ministry of Education,School of Optoelectronic Materials and Technology,Jianghan University,Hubei,430056)

机构地区：[1]江汉大学光电材料与技术学院,柔性光电材料与技术教育部重点实验室,湖北430056

出　　处：《当代化工研究》2025年第3期5-8,共4页Modern Chemical Research

摘　　要：作为工业生产和科学研究中最常用的表征方法之一,振动光谱分析技术可用于物质的定性和定量分析,被广泛应用在化工、能源、材料、医药和生命科学等各个领域。然而,由于不同类型的振动光谱技术有各自的特点和优势,因此,根据实际使用场景选择合适的振动光谱技术,对提高生产研发效率、优化生产工艺、降低成本有着重要意义。此外,随着近年来人工智能技术的发展,利用机器学习方法来提高光谱解析能力成为了新的研究方向。对红外光谱法(IR)、拉曼(Raman)光谱法、太赫兹(THz)光谱法、和频振动光谱法(SFG)四种常见分子振动光谱技术的原理、特点及研究应用进行了综述,并介绍了机器学习算法在振动光谱领域的最新进展,以期为振动光谱技术更好地运用和发展提供参考。Vibration spectroscopy stands as a pivotal characterization method within industrial production and scientific research domains,offering both qualitative and quantitative analysis of substances.Its application extends across various sectors,including the chemical industry,energy,materials science,medicine,and life sciences.However,because different types of vibration spectrum technology have their own characteristics and advantages,it is of great significance to choose the appropriate vibration spectrum technology according to the actual use scenario to improve the efficiency of production research and development,optimize production technology and reduce costs.Additionally,the integration of artificial intelligence,notably machine learning,has emerged as an innovative approach to augment spectral analysis capabilities.The principle,characteristics and research applications of four common molecular vibration spectroscopy techniques,namely infrared spectroscopy(IR),Raman spectroscopy,THz spectroscopy and(SFG)spectroscopy,are reviewed.The latest progress of machine learning algorithms in the field of vibration spectroscopy is introduced in order to provide references for better application and development of vibration spectroscopy.

关 键 词：振动光谱技术 和频振动光谱法 红外光谱法 拉曼光谱法 机器学习算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O433[自动化与计算机技术—控制科学与工程]