Pascalammetry with operando microbattery probes: Sensing high stress in solid-state batteries
Abstract
Energy storage science calls for techniques to elucidate ion transport over a range of conditions and scales. We introduce a new technique, pascalammetry, in which stress is applied to a solid-state electrochemical device and induced faradaic current transients are measured and analyzed. Stress-step pascalammetry measurements are performed on operando microbattery probes (Li2O/Li/W) and Si cathodes, revealing stress-assisted Li+ diffusion. We show how non-Cottrellian lithium diffusional kinetics indicates stress, a prelude to battery degradation. An analytical solution to a diffusion/activation equation describes this stress signature, with spatiotemporal characteristics distinct from Cottrell’s classic solution for unstressed systems. These findings create an unprecedented opportunity for quantitative detection of stress in solid-state batteries through the current signature. Generally, pascalammetry offers a powerful new approach to study stress-related phenomena in any solid-state electrochemical system.
- Authors:
-
- Univ. of Maryland, College Park, MD (United States)
- Publication Date:
- Research Org.:
- Univ. of Maryland, College Park, MD (United States); Energy Frontier Research Centers (EFRC) (United States). Nanostructures for Electrical Energy Storage (NEES)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1499919
- Grant/Contract Number:
- SC0001160
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Science Advances
- Additional Journal Information:
- Journal Volume: 4; Journal Issue: 6; Journal ID: ISSN 2375-2548
- Publisher:
- AAAS
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 25 ENERGY STORAGE; 36 MATERIALS SCIENCE
Citation Formats
Larson, Jonathan M., Gillette, Eleanor, Burson, Kristen, Wang, Yilin, Lee, Sang Bok, and Reutt-Robey, Janice E. Pascalammetry with operando microbattery probes: Sensing high stress in solid-state batteries. United States: N. p., 2018.
Web. doi:10.1126/sciadv.aas8927.
Larson, Jonathan M., Gillette, Eleanor, Burson, Kristen, Wang, Yilin, Lee, Sang Bok, & Reutt-Robey, Janice E. Pascalammetry with operando microbattery probes: Sensing high stress in solid-state batteries. United States. https://doi.org/10.1126/sciadv.aas8927
Larson, Jonathan M., Gillette, Eleanor, Burson, Kristen, Wang, Yilin, Lee, Sang Bok, and Reutt-Robey, Janice E. Fri .
"Pascalammetry with operando microbattery probes: Sensing high stress in solid-state batteries". United States. https://doi.org/10.1126/sciadv.aas8927. https://www.osti.gov/servlets/purl/1499919.
@article{osti_1499919,
title = {Pascalammetry with operando microbattery probes: Sensing high stress in solid-state batteries},
author = {Larson, Jonathan M. and Gillette, Eleanor and Burson, Kristen and Wang, Yilin and Lee, Sang Bok and Reutt-Robey, Janice E.},
abstractNote = {Energy storage science calls for techniques to elucidate ion transport over a range of conditions and scales. We introduce a new technique, pascalammetry, in which stress is applied to a solid-state electrochemical device and induced faradaic current transients are measured and analyzed. Stress-step pascalammetry measurements are performed on operando microbattery probes (Li2O/Li/W) and Si cathodes, revealing stress-assisted Li+ diffusion. We show how non-Cottrellian lithium diffusional kinetics indicates stress, a prelude to battery degradation. An analytical solution to a diffusion/activation equation describes this stress signature, with spatiotemporal characteristics distinct from Cottrell’s classic solution for unstressed systems. These findings create an unprecedented opportunity for quantitative detection of stress in solid-state batteries through the current signature. Generally, pascalammetry offers a powerful new approach to study stress-related phenomena in any solid-state electrochemical system.},
doi = {10.1126/sciadv.aas8927},
journal = {Science Advances},
number = 6,
volume = 4,
place = {United States},
year = {Fri Jun 08 00:00:00 EDT 2018},
month = {Fri Jun 08 00:00:00 EDT 2018}
}
Web of Science
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