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Record W3025903301 · doi:10.1149/ma2020-012306mtgabs

(Invited) All Solid-State Batteries: From Interface to New-Type Electrolytes

2020· article· en· W3025903301 on OpenAlexaff
Andy Xueliang Sun

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsElectrolyteIonic conductivityMaterials scienceSulfideElectrochemistryConductivityIonic bondingNanotechnologyAtomic layer depositionLithium (medication)Chemical engineeringElectrodeIonLayer (electronics)ChemistryMetallurgyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

All-state-state lithium batteries (ASSLBs) have gained worldwide attention because of the high ionic conductivity of SEs, intrinsic safety and improved energy density. In ASSLBs, solid-state electrolyte is a key component. Compared with polymer-based and oxide-based electrolytes, sulfide-based electrolyte generally exhibits the highest ionic conductivity (10-3 ~ 10-2 S/cm) and favorable mechanical property. However, the serious interfacial challenge, bad air stability, narrow electrochemical windows of sulfide-based electrolyte significantly impede development of sulfide-based ASSLBs. Generally, an artificial, uniform and ultrathin interfacial layer is critical to address these challenges. Atomic layer deposition (ALD) and molecular layer deposition (MLD) are unique coating techniques that can realize excellent coverage and conformal deposition with precisely controllable at the nanoscale level due to its self-limiting nature, which are ideal for addressing the challenges of interface in SSLBs. In this talk, I will demonstrate to apply ALD/MLD to (i) stabilize the interface between cathode electrodes and electrolytes and prevent the formation of intrinsically high resistance layers, (ii) suppress elemental inter-diffusion during the operation of SSLBs, (iii) fabricate facile ionic transportation channels to facilitate ion exchange between different components of SSLBs, and (iv) buffer volume changes during cycling of SSLBs [1] . In this talk, I will talk about halide-based electrolyte for ASSLBs. Compared with sulfide-based electrolyte, both experimental and theoretical results recently demonstrate that halide-based electrolytes have more advantages including high RT ionic conductivity (>10-3 S cm-1, theoretically possible 10-2 S cm-1), wide electrochemically stable window (possible up to 6 V), high air-stability, good stability toward oxide cathode materials, and even salable water synthesis strategy [2-3]. References: 1. Zhao, X. Sun .Addressing Interfacial Issues in Liquid-based and Solid-State Batteries by Atomic and Molecular Layer Deposition. Joule. 2, 2583-2604(2018). 2. Li, J. Liang, X. Sun, et al., H2O-Mediated Synthesis of Superionic Halide Solid Electrolyte. Angewandte Chemie International Edition, 2019, http://dx.doi.org/10.1002/anie.201909805 . 3. Li, J. Liang, X. Sun, et al.,. Air-Stable Li3InCl6 Electrolyte with High Voltage Compatibility for All-Solid-State Batteries. Energy Environ. Sci.,2019, 12, 2665 - 267.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0220.013

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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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Citations0
Published2020
Admission routes1
Has abstractyes

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