Multi‐Dimensional Characterization of Battery Materials

Ralf F. Ziesche; Thomas M. M. Heenan; Pooja Kumari; Jarrod Williams; Weiqun Li; Matthew E. Curd; Timothy L. Burnett; Ian Robinson; Dan J. L. Brett; Matthias J. Ehrhardt; Paul D. Quinn; Layla B. Mehdi; Philip J. Withers; Melanie M. Britton; Nigel D. Browning; Paul R. Shearing

doi:10.1002/aenm.202300103

Multi‐Dimensional Characterization of Battery Materials

Ralf F. Ziesche, Thomas M. M. Heenan, Pooja Kumari, Jarrod Williams, Weiqun Li, Matthew E. Curd, Timothy L. Burnett, Ian Robinson, Dan J. L. Brett, Matthias J. Ehrhardt, Paul D. Quinn, Layla B. Mehdi, Philip J. Withers, Melanie M. Britton, Nigel D. Browning, Paul R. Shearing^*

^*Corresponding author for this work

Chemistry

Research output: Contribution to journal › Review article › peer-review

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Abstract

Demand for low carbon energy storage has highlighted the importance of imaging techniques for the characterization of electrode microstructures to determine key parameters associated with battery manufacture, operation, degradation, and failure both for next generation lithium and other novel battery systems. Here, recent progress and literature highlights from magnetic resonance, neutron, X‐ray, focused ion beam, scanning and transmission electron microscopy are summarized. Two major trends are identified: First, the use of multi‐modal microscopy in a correlative fashion, providing contrast modes spanning length‐ and time‐scales, and second, the application of machine learning to guide data collection and analysis, recognizing the role of these tools in evaluating large data streams from increasingly sophisticated imaging experiments.

Original language	English
Article number	2300103
Number of pages	20
Journal	Advanced Energy Materials
Volume	13
Issue number	23
Early online date	1 May 2023
DOIs	https://doi.org/10.1002/aenm.202300103
Publication status	Published - 16 Jun 2023

Bibliographical note

This work was carried out with funding from The Faraday Institution (faraday.ac.uk; EP/S003053/1), grant number FIRG0013 (Characterisation Project). P.R.S. acknowledges funding from The Royal Academy of Engineering (CiET1718/59).

Keywords

correlative microscopy
in situ imaging
Li‐ion batteries
microscopy
multi‐dimensional characterization
time‐resolved

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Ziesche, R. F., Heenan, T. M. M., Kumari, P., Williams, J., Li, W., Curd, M. E., Burnett, T. L., Robinson, I., Brett, D. J. L., Ehrhardt, M. J., Quinn, P. D., Mehdi, L. B., Withers, P. J., Britton, M. M., Browning, N. D., & Shearing, P. R. (2023). Multi‐Dimensional Characterization of Battery Materials. Advanced Energy Materials, 13(23), Article 2300103. https://doi.org/10.1002/aenm.202300103

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title = "Multi‐Dimensional Characterization of Battery Materials",

abstract = "Demand for low carbon energy storage has highlighted the importance of imaging techniques for the characterization of electrode microstructures to determine key parameters associated with battery manufacture, operation, degradation, and failure both for next generation lithium and other novel battery systems. Here, recent progress and literature highlights from magnetic resonance, neutron, X‐ray, focused ion beam, scanning and transmission electron microscopy are summarized. Two major trends are identified: First, the use of multi‐modal microscopy in a correlative fashion, providing contrast modes spanning length‐ and time‐scales, and second, the application of machine learning to guide data collection and analysis, recognizing the role of these tools in evaluating large data streams from increasingly sophisticated imaging experiments.",

keywords = "correlative microscopy, in situ imaging, Li‐ion batteries, microscopy, multi‐dimensional characterization, time‐resolved",

author = "Ziesche, {Ralf F.} and Heenan, {Thomas M. M.} and Pooja Kumari and Jarrod Williams and Weiqun Li and Curd, {Matthew E.} and Burnett, {Timothy L.} and Ian Robinson and Brett, {Dan J. L.} and Ehrhardt, {Matthias J.} and Quinn, {Paul D.} and Mehdi, {Layla B.} and Withers, {Philip J.} and Britton, {Melanie M.} and Browning, {Nigel D.} and Shearing, {Paul R.}",

note = "This work was carried out with funding from The Faraday Institution (faraday.ac.uk; EP/S003053/1), grant number FIRG0013 (Characterisation Project). P.R.S. acknowledges funding from The Royal Academy of Engineering (CiET1718/59).",

year = "2023",

month = jun,

day = "16",

doi = "10.1002/aenm.202300103",

language = "English",

volume = "13",

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T1 - Multi‐Dimensional Characterization of Battery Materials

AU - Ziesche, Ralf F.

AU - Heenan, Thomas M. M.

AU - Kumari, Pooja

AU - Williams, Jarrod

AU - Li, Weiqun

AU - Curd, Matthew E.

AU - Burnett, Timothy L.

AU - Robinson, Ian

AU - Brett, Dan J. L.

AU - Ehrhardt, Matthias J.

AU - Quinn, Paul D.

AU - Mehdi, Layla B.

AU - Withers, Philip J.

AU - Britton, Melanie M.

AU - Browning, Nigel D.

AU - Shearing, Paul R.

N1 - This work was carried out with funding from The Faraday Institution (faraday.ac.uk; EP/S003053/1), grant number FIRG0013 (Characterisation Project). P.R.S. acknowledges funding from The Royal Academy of Engineering (CiET1718/59).

PY - 2023/6/16

Y1 - 2023/6/16

N2 - Demand for low carbon energy storage has highlighted the importance of imaging techniques for the characterization of electrode microstructures to determine key parameters associated with battery manufacture, operation, degradation, and failure both for next generation lithium and other novel battery systems. Here, recent progress and literature highlights from magnetic resonance, neutron, X‐ray, focused ion beam, scanning and transmission electron microscopy are summarized. Two major trends are identified: First, the use of multi‐modal microscopy in a correlative fashion, providing contrast modes spanning length‐ and time‐scales, and second, the application of machine learning to guide data collection and analysis, recognizing the role of these tools in evaluating large data streams from increasingly sophisticated imaging experiments.

AB - Demand for low carbon energy storage has highlighted the importance of imaging techniques for the characterization of electrode microstructures to determine key parameters associated with battery manufacture, operation, degradation, and failure both for next generation lithium and other novel battery systems. Here, recent progress and literature highlights from magnetic resonance, neutron, X‐ray, focused ion beam, scanning and transmission electron microscopy are summarized. Two major trends are identified: First, the use of multi‐modal microscopy in a correlative fashion, providing contrast modes spanning length‐ and time‐scales, and second, the application of machine learning to guide data collection and analysis, recognizing the role of these tools in evaluating large data streams from increasingly sophisticated imaging experiments.

KW - correlative microscopy

KW - in situ imaging

KW - Li‐ion batteries

KW - microscopy

KW - multi‐dimensional characterization

KW - time‐resolved

U2 - 10.1002/aenm.202300103

DO - 10.1002/aenm.202300103

M3 - Review article

SN - 1614-6832

VL - 13

JO - Advanced Energy Materials

JF - Advanced Energy Materials

IS - 23

M1 - 2300103

ER -

Multi‐Dimensional Characterization of Battery Materials

Abstract

Bibliographical note

Keywords

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