Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
| dc.contributor.author | Jiang, Zhisen | en |
| dc.contributor.author | Li, Jizhou | en |
| dc.contributor.author | Yang, Yang | en |
| dc.contributor.author | Mu, Linqin | en |
| dc.contributor.author | Wei, Chenxi | en |
| dc.contributor.author | Yu, Xiqian | en |
| dc.contributor.author | Pianetta, Piero | en |
| dc.contributor.author | Zhao, Kejie | en |
| dc.contributor.author | Cloetens, Peter | en |
| dc.contributor.author | Lin, Feng | en |
| dc.contributor.author | Liu, Yijin | en |
| dc.contributor.department | Chemistry | en |
| dc.date.accessioned | 2020-08-17T13:47:58Z | en |
| dc.date.available | 2020-08-17T13:47:58Z | en |
| dc.date.issued | 2020 | en |
| dc.description.abstract | The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles’ evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/ binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity. | en |
| dc.description.sponsorship | Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. The hard X-ray phase contrast tomography was conducted at the Nano-Imaging beamline ID16A-NI at the ESRF, Grenoble, France. F.L. acknowledges support from the National Science Foundation under the Grants DMR-1832613 and CBET-1912885. K.Z. is grateful for the support by the National Science Foundation through the Grants CMMI-1726392 and DMR-1832707. The NMC powders were produced at the U.S. Department of Energy’s (DOE) CAMP (Cell Analysis, Modeling and Prototyping) Facility, Argonne National Laboratory. The CAMP Facility is fully supported by the DOE Vehicle Technologies Program (VTP) within the core funding of the Applied Battery Research (ABR) for Transportation Program. The work done at IOP was supported by funding from National Key R&D Program of China (Grant No. 2016YFB0100100), National Natural Science Foundation of China (Grant Nos. 51822211 and 11574281), and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51421002). The engineering support from D. Van Campen, D. Day, and V. Borzenets for the TXM experiment at beamline 6-2C of SSRL is gratefully acknowledged. | en |
| dc.identifier.doi | https://doi.org/10.1038/s41467-020-16233-5 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/99723 | en |
| dc.identifier.volume | 11 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Springer Nature | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes | en |
| dc.title.serial | Nature Communications | en |
| dc.type | Article - Refereed | en |