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Record W4407356816 · doi:10.1016/j.fub.2025.100045

Uncovering the role of atmosphere on thermal stability of NASICON type solid electrolytes and oxide-based cathode materials via high temperature X-ray diffraction

2025· article· en· W4407356816 on OpenAlexaff
Wen Zhu, Andrea Paolella, Sylvio Savoie, G. Girard, Abdelbast Guerfi, Ashok K. Vijh, Chisu Kim, Karim Zaghib

Bibliographic record

VenueFuture Batteries · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsConcordia UniversityHydro-Québec
Fundersnot available
KeywordsFast ion conductorMaterials scienceThermal stabilityCathodeElectrolyteOxideDiffractionAtmosphere (unit)Solid solutionChemical engineeringAnalytical Chemistry (journal)ElectrodeChemistryPhysical chemistryMetallurgyThermodynamicsOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Thermal stability of NASICON type solid electrolytes, Li 1.4 Al 0.4 Ti 1.6 (PO 4 ) 3 (LATP) and Li 1.25 Al 0.25 Ge 1.75 (PO 4 ) 3 (LAGP), were studied against LiCoO 2 (LCO), Al-doped LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NCM), LiMn 2 O 4 (LMO) and LiCoPO 4 (LCP) in both air and inert gas. An in-situ high temperature X-ray diffractometer was employed to monitor phase changes during the co-sintering of the electrolytes-cathode composites. The effect of atmosphere on the thermal stability of LATP/LAGP is closely related to the stability of cathode material in the composite. LATP and LAGP are less stable in air than in inert gas when in contact with NCM and LCO. However, their thermal stabilities are similar in both air and inert gas when mixed with LMO and LCP. In the composite samples of LATP/LAGP+LMO, only traces of the impurities were detected at 700 °C due to the decomposition of LATP/LAGP. The initial lithium rich LMO loses approximately 5 % of its lithium but retains the same crystal structure. Therefore, the LATP/LAGP + LMO could be promising composite cathodes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.526

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 head0.003
GPT teacher head0.191
Teacher spread0.189 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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