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Record W4409541287 · doi:10.1002/batt.202500135

Insight into the Hybrid Zn–Co/Air Batteries Coupling Faradic Redox and Oxygen Catalytic Reactions

2025· article· en· W4409541287 on OpenAlexaff
Wenxu Shang, Yongfu Liu, Yi He, Peng Tan

Bibliographic record

VenueBatteries & Supercaps · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Anhui Province
KeywordsRedoxCatalysisOxygenCoupling (piping)ChemistryMaterials sciencePhotochemistryInorganic chemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Hybrid Zn–Co/air batteries achieve both high energy density and high energy efficiency by coupling the oxygen catalytic reaction of Zn–air batteries and the Faradic redox reaction of Zn–Co batteries. However, challenges exist in practical applications, including low utilization rate of active material, insufficient oxygen catalytic activity, and unmatched reaction interfaces. These limitations hinder the performance of hybrid Zn–Co/air batteries and restrict their ability in broader application scenarios. This work reviews the recent development of hybrid Zn–Co/air batteries and focuses on their core issues. In terms of active material structure design, advancements are made in microstructure optimization, defect engineering, ion doping, and electrochemical activation. In the area of catalytic activity optimization, improvements are achieved through the optimization of support materials, structural engineering, and defect engineering. In the field of interface optimization, progress has been made in hydrophilicity and hydrophobicity design, gas transfer channel optimization, and electrode structure design. Finally, this work summarizes the future research directions and technical challenges to promote the commercialization of hybrid Zn–Co/air batteries. The in‐depth analysis aims to provide valuable guidance to researchers to develop the next‐generation high‐performance hybrid Zn–Co/air 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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