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Record W3024413671 · doi:10.1149/ma2020-01124mtgabs

(Invited) Deep Analysis of Lithium Metal Anodes for Rechargeable Batteries

2020· article· en· W3024413671 on OpenAlexaff
Patrick Bouchard, Abdelbast Guerfi, Kim Chisu, M Cho, Michel L. Trudeau, Karim Zaghib

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsLithium metalLithium (medication)ElectrolyteMaterials scienceAnodeRaman spectroscopyX-ray photoelectron spectroscopyIonic conductivityElectrodeMetalNanotechnologyChemical engineeringMetallurgyChemistryEngineering

Abstract

fetched live from OpenAlex

The commercial use of lithium metal batteries was delayed because of dendrite formation on the surface of the lithium electrode, and the difficulty finding a suitable electrolyte that has both the mechanical strength and ionic conductivity required for solid electrolytes. Recently, strategies have developed to overcome these difficulties, so that these batteries are currently an option for different applications, including electric cars. Deep analysis of lithium metal by XPS, In situ SEM, SEM, TEM and Raman spectroscopy will be showed. In this work, we review these strategies, and discuss the different routes that are promising for progress in the near future.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.030
GPT teacher head0.257
Teacher spread0.227 · 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 teacher head, 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
Published2020
Admission routes1
Has abstractyes

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