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Accelerating Battery Characterization Using Neutron and Synchrotron Techniques: Toward a Multi-Modal and Multi-Scale Standardized Experimental Workflow

机译:使用中子和同步加速器技术加速电池表征:迈向多模态和多尺度标准化实验工作流程

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摘要

Li-ion batteries are the essential energy-storage building blocks of modern society. However, producing ultra-high electrochemical performance in safe and sustainable batteries for example, e-mobility, and portable and stationary applications, demands overcoming major technological challenges. Materials engineering and new chemistries are key aspects to achieving this objective, intimately linked to the use of advanced characterization techniques. In particular, operando investigations are currently attracting enormous interest. Synchrotron- and neutron-based bulk techniques are increasingly employed as they provide unique insights into the chemical, morphological, and structural changes inside electrodes and electrolytes across multiple length scales with high time/spatial resolutions. However, data acquisition, data analysis, and scientific outcomes must be accelerated to increase the overall benefits to the academic and industrial communities, requiring a paradigm shift beyond traditional single-shot, sophisticated experiments. Here a multi-scale and multi-technique integrated workflow is presented to enhance bulk characterization, based on standardized and automated data acquisition and analysis for high-throughput and high-fidelity experiments, the optimization of versatile and tunable cells, as well as multi-modal correlative characterization. Furthermore, new mechanisms, methods and organizations such as artificial intelligence-aided modeling-driven strategies, coordinated beamtime allocations, and community-unified infrastructures are discussed in order to highlight perspectives in battery research at large scale facilities.
机译:锂离子电池是现代社会必不可少的储能基石。然而,在安全和可持续的电池中产生超高的电化学性能,例如电动汽车以及便携式和固定式应用,需要克服重大的技术挑战。材料工程和新化学是实现这一目标的关键方面,与先进表征技术的使用密切相关。特别是,手法调查目前引起了极大的兴趣。基于同步子和中子的体技术越来越多地被采用,因为它们提供了对电极和电解质内部的化学、形态和结构变化的独特见解,具有很高的时间/空间分辨率。然而,必须加速数据采集、数据分析和科学成果,以增加学术界和工业界的整体利益,这需要超越传统的单次复杂实验的范式转变。本文介绍了一种多尺度和多技术的集成工作流程,以增强批量表征,基于高通量和高保真实验的标准化和自动化数据采集和分析、多功能和可调谐单元的优化以及多模态相关表征。此外,还讨论了新的机制、方法和组织,如人工智能辅助建模驱动的策略、协调的波束时间分配和社区统一的基础设施,以突出大规模设施电池研究的前景。

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