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Bao YF, Zhu MY, Zhao XJ, Chen HX, Wang X, Ren B. Nanoscale chemical characterization of materials and interfaces by tip-enhanced Raman spectroscopy. Chem Soc Rev 2024. [PMID: 39229965 DOI: 10.1039/d4cs00588k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Materials and their interfaces are the core for the development of a large variety of fields, including catalysis, energy storage and conversion. In this case, tip-enhanced Raman spectroscopy (TERS), which combines scanning probe microscopy with plasmon-enhanced Raman spectroscopy, is a powerful technique that can simultaneously obtain the morphological information and chemical fingerprint of target samples at nanometer spatial resolution. It is an ideal tool for the nanoscale chemical characterization of materials and interfaces, correlating their structures with chemical performances. In this review, we begin with a brief introduction to the nanoscale characterization of materials and interfaces, followed by a detailed discussion on the recent theoretical understanding and technical improvements of TERS, including the origin of enhancement, TERS instruments, TERS tips and the application of algorithms in TERS. Subsequently, we list the key experimental issues that need to be addressed to conduct successful TERS measurements. Next, we focus on the recent progress of TERS in the study of various materials, especially the novel low-dimensional materials, and the progresses of TERS in studying different interfaces, including both solid-gas and solid-liquid interfaces. Finally, we provide an outlook on the future developments of TERS in the study of materials and interfaces.
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Affiliation(s)
- Yi-Fan Bao
- Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
| | - Meng-Yuan Zhu
- Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
| | - Xiao-Jiao Zhao
- Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
| | - Hong-Xuan Chen
- Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
| | - Xiang Wang
- Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
| | - Bin Ren
- Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
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Masson JF, Biggins JS, Ringe E. Machine learning for nanoplasmonics. NATURE NANOTECHNOLOGY 2023; 18:111-123. [PMID: 36702956 DOI: 10.1038/s41565-022-01284-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 10/27/2022] [Indexed: 06/18/2023]
Abstract
Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data.
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Affiliation(s)
- Jean-Francois Masson
- Département de chimie, Quebec Center for Advanced Materials, Regroupement québécois sur les matériaux de pointe, and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage, Université de Montréal, Montréal, Quebec, Canada.
| | - John S Biggins
- Engineering Department, University of Cambridge, Cambridge, UK.
| | - Emilie Ringe
- Department of Material Science and Metallurgy, University of Cambridge, Cambridge, UK.
- Department of Earth Science, University of Cambridge, Cambridge, UK.
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Predicting stress–strain behavior of carbon nanotubes using neural networks. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07430-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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