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Record W4401014899 · doi:10.1016/j.device.2024.100468

Spraying Li6PS5Cl and silver-carbon multilayers to facilitate large-scale fabrication of all-solid-state batteries

2024· article· en· W4401014899 on OpenAlexaff
Christopher Doerrer, Michael Metzler, Guillaume Matthews, Junfu Bu, Dominic Spencer Jolly, Peter G. Bruce, Mauro Pasta, Patrick S. Grant

Bibliographic record

VenueDevice · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research CouncilFaraday InstitutionHenry Royce Institute
KeywordsFabricationCarbon fibersNanotechnologySolid-stateScale (ratio)Materials scienceProcess engineeringEnvironmental scienceEngineering physicsEngineeringComposite materialPhysicsComposite number

Abstract

fetched live from OpenAlex

In recent years, solid-state battery (SSB) performance has steadily improved with the use of sulfide solid electrolytes (SEs). However, most research has focused on small (diameter < 10 mm), thick (separator > 500 μm) pellet-type cells that use non-scalable manufacturing routes and yield a low cell energy density. Technical applications require thinner and larger sheet-type cells made by scalable techniques. We examine the applicability of a scalable layer-by-layer spray printing approach for manufacturing sheet-type SSB components. Sprayed sulfide SE separators with thickness as thin as 10 μm and high ionic conductivity of 1 mS cm −1 are fabricated, along with a sprayed composite cathode that delivered a capacity retention of 63% after 800 cycles. Finally, the flexibility of spray printing for process integration is demonstrated by the fabrication of an anode-free cell consisting of a sprayed Ag-C layer and a sprayed SE layer.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.276
Teacher spread0.250 · 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".

Quick stats

Citations16
Published2024
Admission routes1
Has abstractyes

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