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Machine learning accelerates identification of lithiated phases in X-ray images of battery hosts

Journal Article · · Patterns

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
DOE Contract Number:
AC02-06CH11357
OSTI ID:
2205173
Journal Information:
Patterns, Vol. 3, Issue 12; ISSN 2666-3899
Country of Publication:
United States
Language:
English

References (8)

Fingerprinting Redox Heterogeneity in Electrodes during Extreme Fast Charging journal May 2020
Mapping polaronic states and lithiation gradients in individual V2O5 nanowires journal June 2016
Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials journal December 2022
Reaction Heterogeneity in LiFePO4 Agglomerates and the Role of Intercalation-Induced Stress journal April 2022
Quantitative Visualization of Salt Concentration Distributions in Lithium-Ion Battery Electrolytes during Battery Operation Using X-ray Phase Imaging journal January 2018
Stress and Strain in Silicon Electrode Models journal January 2015
Single-particle measurements of electrochemical kinetics in NMC and NCA cathodes for Li-ion batteries journal January 2018
Unconventional Charge Transport in MgCr2O4 and Implications for Battery Intercalation Hosts journal July 2022