In this section
@article{Finegan:2022:10.1021/acsenergylett.2c01996,
author = {Finegan, DP and Squires, I and Dahari, A and Kench, S and Jungjohann, KL and Cooper, SJ},
doi = {10.1021/acsenergylett.2c01996},
journal = {ACS Energy Letters},
pages = {4368--4378},
title = {Machine-learning-driven advanced characterization of battery electrodes},
url = {http://dx.doi.org/10.1021/acsenergylett.2c01996},
volume = {7},
year = {2022}
}
TY - JOUR
AB - Materials characterization is fundamental to our understanding of lithium ion battery electrodes and their performance limitations. Advances in laboratory-based characterization techniques have yielded powerful insights into the structure–function relationship of electrodes, yet there is still far to go. Further improvements rely, in part, on gaining a deeper understanding of complex physical heterogeneities in the materials. However, practical limitations in characterization techniques inhibit our ability to combine data directly. For example, some characterization techniques are destructive, thus preventing additional analyses on the same region. Fortunately, artificial intelligence (AI) has shown great potential for achieving representative, 3D, multi-modal datasets by leveraging data collected from a range of techniques. In this Perspective, we give an overview of recent advances in lab-based characterization techniques for Li-ion electrodes. We then discuss how AI methods can combine and enhance these techniques, leading to substantial acceleration in our understanding of electrodes.
AU - Finegan,DP
AU - Squires,I
AU - Dahari,A
AU - Kench,S
AU - Jungjohann,KL
AU - Cooper,SJ
DO - 10.1021/acsenergylett.2c01996
EP - 4378
PY - 2022///
SN - 2380-8195
SP - 4368
TI - Machine-learning-driven advanced characterization of battery electrodes
T2 - ACS Energy Letters
UR - http://dx.doi.org/10.1021/acsenergylett.2c01996
UR - http://hdl.handle.net/10044/1/104179
VL - 7
ER -
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