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Record W4412136891 · doi:10.1021/acsami.5c06192

Dendrite Suppression in Zn Batteries through a Heteroepitaxial Residual Stress Shield

2025· article· en· W4412136891 on OpenAlexafffund
Musanna Galib, Jia Xu, Amardeep Amardeep, Jian Liu, Mauricio Ponga

Bibliographic record

VenueACS Applied Materials & Interfaces · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsMaterials scienceResidual stressShieldDendrite (mathematics)Composite materialStress (linguistics)OptoelectronicsMetallurgyForensic engineering

Abstract

fetched live from OpenAlex

Dendrite formation is a long-standing problem for the commercial application of aqueous zinc ion batteries (AZIBs). Here, we investigate the effect of heteroepitaxial residual stresses due to layered coatings on dendrite suppression. We found that atomic and molecular layered coatings can substantially reduce dendritic growth in AZIBs by providing shielding due to residual stresses, even at a single layer or a few layers of coatings. Through a combined experimental and numerical approach, we demonstrate that the residual stresses developed due to the coating of the Zn anodes significantly reduced the chemical potential polarization around dendrite embryos, forcing the deposition of zinc in the regions adjacent to the protuberances. This, in turn, results in a slower rate of dendritic growth and, eventually, dendrite suppression. The fundamental understanding of the effect of residual stresses due to coatings demonstrated herein can be extended to various metal anode batteries, such as Li or Na.

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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.013
GPT teacher head0.275
Teacher spread0.261 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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