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Record W3113488080 · doi:10.1149/ma2020-0251019mtgabs

(Keynote) Halide-Based Solid-State Batteries: Electrolyte, Stability, Interface and Electrode

2020· article· en· W3113488080 on OpenAlexaff
Xueliang Sun

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsElectrolyteHalideIonic conductivityFast ion conductorLithium (medication)Materials scienceQuasi-solidCathodeBattery (electricity)Chemical engineeringOxideElectrodeNanotechnologyChemistryInorganic chemistryPhysical chemistryPhysicsThermodynamicsEngineeringDye-sensitized solar cell

Abstract

fetched live from OpenAlex

All-state-state lithium batteries (ASSLBs) have gained worldwide attention because of intrinsic safety and increased energy density. Compared with other types of solid-state electrolytes including oxide-based, polymer-based and sulfide-based electrolytes, recently-developed halide-based solid-state electrolytes (SSEs) have garnered considerable attention for all-solid-state lithium batteries (ASSLBs) due to the high ionic conductivity, high oxidation voltage and good stability toward oxide cathode materials [1]. However, there are still many challenges in halide-based solid-state electrolytes for ASSLBs including controllable and mass-production synthesis, achieving high humidity tolerance and demonstrate high-performance of ASSLBs; in particular, increased understanding of mechanisms during synthesis and tuning their properties of the electrolytes as well as interface with electrode materials[1]. In this talk, (i) I will demonstrate synthesis strategy [2-3], in particular, new and salable water-mediated synthesis method [2]. (ii) I will report a systematic study on the correlations among structural evolution, Li+ migration properties, and humidity stability resulting of the halide-based electrolytes, along with in-situ characterization for understanding of the mechanisms [4], (iii) Full cell battery performance will be optimized [5], and (iv) humidity ability [6]. In the end, energy densities of ASSLBs using different solid-state electrolytes in ASSLBs will be discussed. References: 1. X. Li, J. Liang, X. Yang, K. Adair, C. Wang, F. Zhao, X. Sun. Progress and Perspectives of Halide-based Lithium Conductors for All-Solid-State Batteries. Energy Environ. Sci., 13, 1429-1461 (2020). 2. X. Li, J. Liang, X. Sun, et al., H2O-Mediated Synthesis of Superionic Halide Solid Electrolyte. Angewandte Chemie International Edition, 58,1-7(2019). 3. X. Li, J. Liang, X. Sun, et al.,. Air-Stable Li3InCl6 Electrolyte with High Voltage Compatibility for All-Solid-State Batteries. Energy Environ. Sci., 12, 2665 - 267 (2019). 4. X. Li, J. Liang, X. Sun, et al.,. Origin of Superionic Halide Solid Electrolytes with High Humidity Tolerance, 2020, J. Am. Chem. Soc. 142, 7012-7022 (2020). 5. C. Wang, X. Li, J. Liang, X. Sun, et al., Eliminating Interfacial Resistance in All-Inorganic Batteries by In-situ Interfacial Growth of Halide-based Electrolyte. Nano Energy, 2020, in press. X. Li, J. Liang, X. Sun, et al., Origin of Superionic Li3Y1-xInxCl6 Halide Solid Electrolytes with High 6. Humidity Tolerance, Nano Letters, in press, 2020.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.179
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1790.077

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreOther

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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Citations0
Published2020
Admission routes1
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