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Record W4410870108 · doi:10.1002/adfm.202506270

Fluorinated Interphase Enabled by Lithium Salt‐Driven Electrical Double‐Layer Modulation for Advanced Zinc Metal Batteries

2025· article· en· W4410870108 on OpenAlexafffund
Ziwei Zhao, Pengcheng Li, Yuxuan Wu, Ziwei Chai, Hao Zhang, Ge Li

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilCanada First Research Excellence FundUniversity of Alberta
KeywordsMaterials scienceLithium metalInterphaseZincLithium (medication)MetalLayer (electronics)Salt (chemistry)NanotechnologyModulation (music)Inorganic chemistryChemical engineeringElectrodeMetallurgyElectrolyteOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract High‐concentration electrolytes show promise in zinc metal batteries, but low zinc salt solubility limits options. This study introduces a highly soluble, reduction‐active lithium salt into conventional electrolytes. By modifying the solvation structure and electric double layer, the reversibility of the zinc anode is enhanced. At the cathode, ion absorption on the surface leads to a restructured electric double layer, thereby reducing the dissolution of active materials. With this electrolyte, Zn//Cu half‐cells exhibit over 1300 cycle lifespan with an average columbic efficiency of 99.60%. Furthermore, the full‐cell with high mass loading (5 mg cm −2 ) NaV 3 O 8 ·1.5 H 2 O (NVO) cathode and a low negative‐to‐positive (N/P) ratio of 3 retains 95% capacity after 250 cycles. Even at −45 °C, the capacity retention of the battery is almost 100% after 500 cycles. This work highlights the potential of high‐concentration lithium salts to improve the stability of aqueous zinc batteries by modulating solvation structures and interfacial chemistry.

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 score0.328
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
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.278
Teacher spread0.263 · 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

Citations9
Published2025
Admission routes2
Has abstractyes

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