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Record W4362473307 · doi:10.1021/acsaem.3c00179

Unraveling the Origin of Lithiophilicity toward a Molten Li Metal: Zn Metal as Trojan Horse

2023· article· en· W4362473307 on OpenAlexafffund
Shirin Kaboli, Wen Zhu, Daniel Clément, Martin Dontigny, Frédéric Gendron, Kamyab Amouzegar, Abdelbast Guerfi, Ashok K. Vijh, Michel L. Trudeau, Andrea Paolella

Bibliographic record

VenueACS Applied Energy Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsHydro-Québec
FundersHydro-Québec
KeywordsMaterials scienceIntermetallicMetalAlloyScanning electron microscopeSubstrate (aquarium)Chemical engineeringEnergy-dispersive X-ray spectroscopyMetallurgyFOIL methodComposite material

Abstract

fetched live from OpenAlex

In this work, we investigated the origin of lithiophilicity of a Cu foil substrate modified by a sputtered Zn thin film (Cu@Zn) in contact with a molten Li metal to understand the reaction mechanism between Li and Cu@Zn. We studied the reaction between the molten Li metal and the Cu surface during Li solidification via in situ scanning electron microscopy (SEM), subsequently performed post-mortem energy dispersive spectroscopy (EDS) and Grazing Incidence X-ray Diffraction (GIXRD) on the coatings to analyze the chemistry of the reaction products, and compared the results for different thicknesses of nanometric Zn films (5–50 nm). For the first time in the literature, we report the existence of a metastable ternary Li-Cu-Zn alloy at 300 °C after the reaction of Cu@Zn with the molten Li metal. We also report the segregation of Cu and Zn by formation of Cu-Zn intermetallic compounds during the cooling down step. The results of our in situ study are pivotal to clarify the interfacial reactions occurring between a lithiophilic current collector and a molten Li metal and have utmost importance for designing advanced anode materials for future solid-state battery applications.

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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.254
Teacher spread0.230 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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