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Record W4285399163 · doi:10.1149/ma2022-0162435mtgabs

(Digital Presentation) All-Solid-State Lithium Batteries: From Materials and Interface Design to Practical Pouch Cell Engineering

2022· article· en· W4285399163 on OpenAlexaff
Changhong Wang

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsElectrolyteIonic conductivityMaterials scienceChemical engineeringLithium (medication)NanotechnologyQuasi-solidFast ion conductorElectrochemistryConductivityChemistryElectrodeEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

All-solid-state lithium batteries (ASSLBs) have gained substantial attention because of their intrinsic safety and high energy density.1 However, the commercialization of ASSLBs has been stymied by insufficient ionic conductivity of solid-state electrolytes, significant interfacial challenges, as well as the large gap between fundamental research and practical engineering. Over the past several years, we have been dedicated to developing ASSLBs from solid electrolyte synthesis to interface design to engineering practical solid-state pouch cells. First, a wet-chemistry method with a low cost was proposed to produce solid-state electrolytes at the kilogram level with a high room-temperature ionic conductivity (> 1 mS.cm-1).2 Second, the interfacial challenges of ASSLBs have been well addressed via increasing the ionic conductivity of interfacial buffer layers,3 manipulating interfacial nanostructures,4, 5 using single-crystal cathodes,6 deciphering interfacial reaction mechanisms,7 and constructing artificial solid electrolyte interphases (SEI),8 which successfully boosted interfacial ion and electron transport kinetics.9 Resultantly, ASSLBs demonstrated superior electrochemical performance. Third, practical solid-state pouch cells with high energy density have been engineered. Recently, a solvent-free process was proposed to fabricate freestanding and ultrathin inorganic solid electrolyte membranes.10 Furthermore, a feasible solid-liquid transformable interface was devised to improve the solid-solid ionic contact and accommodate the significant volume change of solid-state pouch cells.11, 12 The resultant solid-state pouch cells successfully demonstrated high energy density and unparalleled safety. In summary, our research not only provides an in-depth understanding of solid electrolyte synthesis and rational interface design but also offers feasible strategies to commercialize ASSLBs with high energy density, low cost, and excellent safety. References C. Wang, J. Liang, Y. Zhao, M. Zheng, X. Li and X. Sun, Energy Environ. Sci., 2021, 14, 2577-2619. C. Wang, J. Liang, J. Luo, J. Liu, X. Li, F. Zhao, R. Li, H. Huang, S. Zhao, L. Zhang, J. Wang and X. Sun, Sci. Adv., 2021, 7, eabh1896. C. Wang, J. Liang, S. Hwang, X. Li, Y. Zhao, K. Adair, C. Zhao, X. Li, S. Deng, X. Lin, X. Yang, R. Li, H. Huang, L. Zhang, S. Lu, D. Su and X. Sun, Nano Energy, 2020, 72, 104686. C. Wang, X. Li, Y. Zhao, M. N. Banis, J. Liang, X. Li, Y. Sun, K. R. Adair, Q. Sun, Y. Liu, F. Zhao, S. Deng, X. Lin, R. Li, Y. Hu, T.-K. Sham, H. Huang, L. Zhang, R. Yang, S. Lu and X. Sun, Small Methods, 2019, 3, 1900261. C. Wang, J. Liang, M. Jiang, X. Li, S. Mukherjee, K. Adair, M. Zheng, Y. Zhao, F. Zhao, S. Zhang, R. Li, H. Huang, S. Zhao, L. Zhang, S. Lu, C. V. Singh and X. Sun, Nano Energy, 2020, 76, 105015. C. Wang, R. Yu, S. Hwang, J. Liang, X. Li, C. Zhao, Y. Sun, J. Wang, N. Holmes, R. Li, H. Huang, S. Zhao, L. Zhang, S. Lu, D. Su and X. Sun, Energy Storage Mater., 2020, 30, 98-103. C. Wang, S. Hwang, M. Jiang, J. Liang, Y. Sun, K. Adair, M. Zheng, S. Mukherjee, X. Li, R. Li, H. Huang, S. Zhao, L. Zhang, S. Lu, J. Wang, C. V. Singh, D. Su and X. Sun, Adv. Energy Mater., 2021, 11, 2100210. C. Wang, Y. Zhao, Q. Sun, X. Li, Y. Liu, J. Liang, X. Li, X. Lin, R. Li, K. R. Adair, L. Zhang, R. Yang, S. Lu and X. Sun, Nano Energy, 2018, 53, 168-174. C. Wang, K. Adair and X. Sun, Acc. Mater. Res., 2022, 3, 21-32. C. Wang, R. Yu, H. Duan, Q. Lu, Q. Li, K. R. Adair, D. Bao, Y. Liu, R. Yang, J. Wang, S. Zhao, H. Huang and X. Sun, ACS Energy Lett., 2022, DOI: 10.1021/acsenergylett.1c02261, 410-416. C. Wang, Q. Sun, Y. Liu, Y. Zhao, X. Li, X. Lin, M. N. Banis, M. Li, W. Li, K. R. Adair, D. Wang, J. Liang, R. Li, L. Zhang, R. Yang, S. Lu and X. Sun, Nano Energy, 2018, 48, 35-43. C. Wang, K. R. Adair, J. Liang, X. Li, Y. Sun, X. Li, J. Wang, Q. Sun, F. Zhao, X. Lin, R. Li, H. Huang, L. Zhang, R. Yang, S. Lu and X. Sun, Adv. Funct. Mater., 2019, 29, 1900392.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.157
Threshold uncertainty score0.527

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1570.070

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.251
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2022
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