New engineering science insights into the electrodes pairing of electrochemical energy storage devices assisted by machine learning
- DOI
- 10.21203/rs.3.rs-831006/v1
- Published
- 2022-05-18
- Container
- Not recorded
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.21203/rs.3.rs-831006/v1,
title = {New engineering science insights into the electrodes pairing of electrochemical energy storage devices assisted by machine learning},
author = {Longbing Qu and Peiyao Wang and Benyamin Motevalli and Qinghua Liang and Kangyan Wang and Wen-Jie Jiang and Jefferson Zhe Liu and Dan Li},
year = {2022},
doi = {10.21203/rs.3.rs-831006/v1},
url = {https://doi.org/10.21203/rs.3.rs-831006/v1}
}RIS
TY - JOUR TI - New engineering science insights into the electrodes pairing of electrochemical energy storage devices assisted by machine learning AU - Longbing Qu AU - Peiyao Wang AU - Benyamin Motevalli AU - Qinghua Liang AU - Kangyan Wang AU - Wen-Jie Jiang AU - Jefferson Zhe Liu AU - Dan Li PY - 2022 DO - 10.21203/rs.3.rs-831006/v1 UR - https://doi.org/10.21203/rs.3.rs-831006/v1 ER -
APA
Qu, L., Wang, P., Motevalli, B., Liang, Q., Wang, K., Jiang, W., Liu, J. Z., & Li, D. (2022). New engineering science insights into the electrodes pairing of electrochemical energy storage devices assisted by machine learning. https://doi.org/10.21203/rs.3.rs-831006/v1
Source records
- crossref · retrieved 2026-09-25T20:47:29.573Z