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Record W4321769557 · doi:10.1002/aenm.202370033

Synergistic Approach toward Developing Highly Compatible Garnet‐Liquid Electrolyte Interphase in Hybrid Solid‐State Lithium‐Metal Batteries (Adv. Energy Mater. 8/2023)

2023· article· en· W4321769557 on OpenAlexaff
Subhajit Sarkar, Bowen Chen, Chengtian Zhou, Shahram Nouri Shirazi, Frederieke Langer, Julian Schwenzel, Venkataraman Thangadurai

Bibliographic record

VenueAdvanced Energy Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectrolyteMaterials scienceInterphaseLithium metalLithium (medication)Energy densityChemical engineeringFast ion conductorInorganic chemistryElectrodeEngineering physicsChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Garnet Type Solid Electrolytes In article number 2203897, Venkataraman Thangadurai and co-workers report the use of an AlCl3 Lewis acid and fluoroethylene carbonate dual additive based non-aqueous liquid electrolyte stabilizing garnet solid-liquid electrolyte interphase, towards improved durability and high critical current density for hybrid solid-state lithium metal batteries.

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 score0.003

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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