Device-level synchrotron-based <i>operando</i> characterization of electrochemistry at scale

Electrochemical energy conversion devices, such as fuel cells and water electrolyzers, offer high power densities and efficiencies, along with quiet, emission-free operation, making them attractive candidates for large-scale applications. Advancing these technologies requires the development of materials including catalysts, membranes, and diffusion media that can deliver the performance and durability metrics necessary for practical deployment. However, the true/relevant active states of materials often exist only under operating conditions of electrochemical devices, making them inaccessible to conventional <em>ex situ</em> characterization techniques. Thus, characterization of electrochemical devices under operating conditions, namely <em>operando</em>, can offer critical and valuable insights for guiding/informing material development. Synchrotron-based hard X-ray (&gt;5 keV) techniques are particularly well-suited for <em>operando</em> characterization of electrochemical devices due to their high penetration depth, and relatively weak interactions, enabling the probing of various internal components. The various synchrotron X-ray based characterization techniques can provide comprehensive information including chemical (valence) states, coordination environments and structure across different length scales. The use of these techniques provides unique opportunities to capture material’s active states and track evolving dynamics. In this perspective, we provide an overview of device-level <em>operando</em> characterization using synchrotron-based X-ray techniques and highlight its potential to inform and accelerate materials development towards the large-scale deployment of electrochemical technologies.

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Publication Details

Journal
Carbon Future
Published
2025-11-13
DOI
https://doi.org/10.26599/cf.2025.9200059
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Device-level synchrotron-based operando characterization of electrochemistry at scale

Héctor D. Abruña, Qihao Li
Carbon Future
Machine Learning in Materials Science
article

Device-level synchrotron-based operando characterization of electrochemistry at scale

Héctor D. Abruña, Qihao Li
article en

Abstract

Electrochemical energy conversion devices, such as fuel cells and water electrolyzers, offer high power densities and efficiencies, along with quiet, emission-free operation, making them attractive candidates for large-scale applications. Advancing these technologies requires the development of materials including catalysts, membranes, and diffusion media that can deliver the performance and durability metrics necessary for practical deployment. However, the true/relevant active states of materials often exist only under operating conditions of electrochemical devices, making them inaccessible to conventional <em>ex situ</em> characterization techniques. Thus, characterization of electrochemical devices under operating conditions, namely <em>operando</em>, can offer critical and valuable insights for guiding/informing material development. Synchrotron-based hard X-ray (&gt;5 keV) techniques are particularly well-suited for <em>operando</em> characterization of electrochemical devices due to their high penetration depth, and relatively weak interactions, enabling the probing of various internal components. The various synchrotron X-ray based characterization techniques can provide comprehensive information including chemical (valence) states, coordination environments and structure across different length scales. The use of these techniques provides unique opportunities to capture material’s active states and track evolving dynamics. In this perspective, we provide an overview of device-level <em>operando</em> characterization using synchrotron-based X-ray techniques and highlight its potential to inform and accelerate materials development towards the large-scale deployment of electrochemical technologies.

Carbon FutureVol. 2(4)
Cornell University (US), New York State College of Agriculture & Life Sciences (US), New York State College of Veterinary Medicine (US)
U.S. Department of Energy, New York State Energy Research and Development Authority
Openalex Percentile: Top 73%
Machine Learning in Materials Science
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