Tan, Hwee Jien, Powell, Matthew A., Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Bird, Tobias A., Day, Sarah J., Pandey, Gaurav C., Walker, David, Huband, Steven, Price, Stephen W.T., Perez, Gabriel E., Piper, Louis F.J. and Menon, Ashok S. (2026) Standardizing operando diffraction studies for battery systems. ACS Energy Letters, 11 (9). pp. 5994-6006. ISSN 2380-8195 (Online) (doi:10.1021/acsenergylett.6c01791)

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Abstract

Operando diffraction experiments enable real-time, nondestructive correlation of the electrochemical behavior of battery cells with the physicochemical changes occurring in their internal components. These experiments can now be directly performed on standard cell formats, identical to those used for electrochemical testing, at unprecedented scattering, temporal, and spatial resolutions. This can generate significant volumes of new multimodal datasets, opening exciting possibilities for probing and parameterizing complex battery performance and degradation mechanisms. To fully leverage the wealth of data produced by such experiments via data-driven machine learning methods, the open sharing of data via standardized, machine-readable formats, in line with FAIR principles, is essential. Currently, this is hindered by differences in the cell design, experimental protocols, and data-processing workflows. In this perspective, we discuss the need for standardizing operando battery diffraction experiments, the associated challenges, and propose a NeXus-based application definition for the standardized exchange of experimental metadata and results as a pathway toward reproducible research practices and transparent analytical workflows.

Item Type:	Article
Uncontrolled Keywords:	operando diffraction, battery systems, X-ray diffraction, Neutron diffraction, standardization, FAIR data, NeXus, battery diagnostics, machine learning, autonomous experimentation
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	11 Sep 2026 11:03
URI:	https://gala.gre.ac.uk/id/eprint/54396

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