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In situ SERS and in situ Raman: deciphering interfacial phenomena and processes
Jingyi Wei1, Sixian Yu2, Tianxiang Zhou1
1Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Material Sciences and Technology, China University of Geosciences, Beijing 100083, China. hanfeng@cugb.edu.cn.
This review details in situ Raman and surface-enhanced Raman spectroscopy (SERS) for studying surface reactions. It highlights key principles, advances, and challenges for in situ SERS applications in chemical analysis.
Area of Science:
- Surface Science
- Spectroscopy
- Chemical Analysis
Background:
- Raman and SERS provide vibrational spectral data for surface analysis.
- These techniques are sensitive to molecular electron density, aiding chemical reaction studies.
- Challenges exist in developing in situ SERS substrates and analyzing complex data.
Purpose of the Study:
- To systematically review in situ Raman/SERS principles and advances for surface reaction studies.
- To compile species identifications and spectral features from recent in situ studies.
- To discuss challenges and future perspectives for in situ SERS.
Main Methods:
- Brief introduction to SERS mechanisms and influencing factors.
- Discussion of interfacial phenomena (species transition, electron density, crystalline structure changes).
- Compilation and analysis of reported in situ SERS studies on surface reactions.
Main Results:
- Overview of SERS mechanisms and factors affecting in situ measurements.
- Systematic compilation of species identification and spectral features in surface reactions.
- Identification of interfacial phenomena crucial for in situ SERS analysis.
Conclusions:
- In situ Raman/SERS are powerful tools for detailed surface reaction analysis.
- Further development is needed for robust in situ SERS substrates and data interpretation.
- Future research should integrate advanced computational methods like machine learning for deeper insights.
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