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Record W4310975803 · doi:10.1039/d2ta04259b

Metastable properties of a garnet type Li<sub>5</sub>La<sub>3</sub>Bi<sub>2</sub>O<sub>12</sub> solid electrolyte towards low temperature pressure driven densification

2022· article· en· W4310975803 on OpenAlexafffund
Daniele Campanella, Sergey Krachkovskiy, Giovanni Bertoni, Gian Carlo Gazzadi, Maryam Golozar, Shirin Kaboli, Sylvio Savoie, Gabriel Girard, Alina Cristina Gheorghe Nita, Kirill Okhotnikov, Zimin Feng, Abdelbast Guerfi, Ashok K. Vijh, Raynald Gauvin, Daniel Bélanger, Andrea Paolella

Bibliographic record

VenueJournal of Materials Chemistry A · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsMcGill UniversityHydro-QuébecUniversité du Québec à Montréal
FundersMitacsHydro-Québec
KeywordsMetastabilityMaterials scienceElectrolyteImpuritySolid solutionType (biology)ThermodynamicsHot pressingCrystallographyAnalytical Chemistry (journal)MineralogyPhysical chemistryChemistryMetallurgyPhysicsElectrode

Abstract

fetched live from OpenAlex

Hot-pressing leads to low temperature densification of garnet type Li 5 La 3 Bi 2 O 12 solid electrolyte and density functional theory calculations allow to explore its metastable properties under the formation of impurities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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.

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

Citations5
Published2022
Admission routes2
Has abstractyes

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