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Record W4415028987 · doi:10.1002/aenm.202503537

Optimizing Lithium Nucleation Overpotential in Anode‐Free Garnet‐Based Hybrid Solid‐State Batteries

2025· article· en· W4415028987 on OpenAlexafffund
Subhajit Sarkar, Joshua Budde, Ingo Bardenhagen, Julian Schwenzel, Todd C. Sutherland, Venkataraman Thangadurai

Bibliographic record

VenueAdvanced Energy Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFaraday efficiencyElectrolyteOverpotentialLithium (medication)NucleationFast ion conductor

Abstract

fetched live from OpenAlex

Abstract Anode‐free solid‐state lithium batteries promise high‐energy‐density storage but remain limited by unstable interfaces between the solid electrolyte and in situ‐formed lithium. In this work, a hybrid solid–liquid electrolyte strategy is developed using a localized high‐concentration electrolyte containing AlCl 3 and LiPO 2 F 2 , a trilayer garnet‐type Li 6.4 Ga 0.1 La 2.9 Ba 0.1 Zr 1.6 Ta 0.4 O 12 solid electrolyte. This configuration forms a mechanically stable and ionically conductive solid‐liquid electrolyte interphase, enabling 81% Coulombic efficiency after 300 cycles at 2 mA cm −2 and 1 mAh cm −2 in Cu/Li half‐cells. A single‐layer anode‐free hybrid solid‐state pouch cell demonstrated excellent long‐term cycling performance, retaining 75% of its initial capacity after 400 cycles at 1C (1.25 mA cm −2 ), with a Coulombic efficiency of 99% without external pressure and at room temperature. At the lab scale, this hybrid electrolyte approach shows both scalability and performance, providing a potential practical pathway to next‐generation anode‐free hybrid solid‐state lithium 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 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 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 score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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