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Abstract Detailed studies of interfacial gas-phase chemical reactions are important for understanding factors that control materials synthesis and environmental conditions that govern materials performance and degradation. Out of the many materials characterization methods that are available for interpreting gas–solid reaction processes,in situandoperandotransmission electron microscopy (TEM) is perhaps the most versatile, multimodal materials characterization technique. It has successfully been utilized to study interfacial gas–solid interactions under a wide range of environmental conditions, such as gas composition, humidity, pressure, and temperature. This stems from decades of R&D that permit controlled gas delivery and the ability to maintain a gaseous environment directly within the TEM column itself or through specialized side-entry gas-cell holders. Combined with capabilities for real-time, high spatial resolution imaging, electron diffraction and spectroscopy, dynamic structural and chemical changes can be investigated to determine fundamental reaction mechanisms and kinetics that occur at site-specific interfaces. This issue ofMRS Bulletincovers research in this field ranging from technique development to the utilization of gas-phase microscopy methods that have been used to develop an improved understanding of multilength-scaled processes incurred during materials synthesis, catalytic reactions, and environmental exposure effects on materials properties. Graphical abstract
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Quantitative gas-phase transmission electron microscopy: Where are we now and what comes next?
Abstract Based on historical developments and the current state of the art in gas-phase transmission electron microscopy (GP-TEM), we provide a perspective covering exciting new technologies and methodologies of relevance for chemical and surface sciences. Considering thermal and photochemical reaction environments, we emphasize the benefit of implementing gas cells, quantitative TEM approaches using sensitive detection for structured electron illumination (in space and time) and data denoising, optical excitation, and data mining using autonomous machine learning techniques. These emerging advances open new ways to accelerate discoveries in chemical and surface sciences. Graphical abstract
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- PAR ID:
- 10489564
- Publisher / Repository:
- Cambridge University Press (CUP)
- Date Published:
- Journal Name:
- MRS Bulletin
- Volume:
- 49
- Issue:
- 2
- ISSN:
- 0883-7694
- Format(s):
- Medium: X Size: p. 174-183
- Size(s):
- p. 174-183
- Sponsoring Org:
- National Science Foundation
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