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<p>This review outlines a standardized, AI-accelerated architecture for real-time battery characterization, integrating neutron and synchrotron techniques with coordinated multi-modal analysis. Case studies spanning lithium-ion and solid-state batteries demonstrate how this framework enhances phase mapping, degradation modeling, and the detection of early failures. The approach incorporates modular commercial cell formats, FAIR-compliant metadata structures, and beamline automation to achieve integrated datasets within &lt; 1&#xa0;month, enabling a 3–5 × improvement in cycle-accurate diagnostic resolution. By combining advanced instrumentation, intelligent data pipelines, and global scheduling protocols, this roadmap advances reproducibility, real-time predictability, and translational impact in next-generation battery research.</p>

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Toward standardized, AI-driven multi-modal battery characterization: a review of neutron and synchrotron strategies across scales

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Obinna Onyebuchi Barah,
  • Ukagwu Kelechi John,
  • Val Hyginus Udoka Eze,
  • Chikadibia Kalu Awa Uche,
  • Stephen Ndubuisi Nnamchi

摘要

This review outlines a standardized, AI-accelerated architecture for real-time battery characterization, integrating neutron and synchrotron techniques with coordinated multi-modal analysis. Case studies spanning lithium-ion and solid-state batteries demonstrate how this framework enhances phase mapping, degradation modeling, and the detection of early failures. The approach incorporates modular commercial cell formats, FAIR-compliant metadata structures, and beamline automation to achieve integrated datasets within < 1 month, enabling a 3–5 × improvement in cycle-accurate diagnostic resolution. By combining advanced instrumentation, intelligent data pipelines, and global scheduling protocols, this roadmap advances reproducibility, real-time predictability, and translational impact in next-generation battery research.