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Record W4416779706 · doi:10.1021/acs.langmuir.5c03931

Constructing Nanoparticle-Integrated CoWO <sub>4</sub> Microflakes as a Promising Cathode Material for Aqueous Zinc-Ion Batteries

2025· article· en· W4416779706 on OpenAlexaff
Lin Huang, Xin Wang, Cuixia Cheng

Bibliographic record

VenueLangmuir · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsAqueous solutionCathodeEnergy storageCalcinationTungstateCyclic voltammetryBattery (electricity)Electrolyte

Abstract

fetched live from OpenAlex

Exploring a satisfactory cathode material for aqueous zinc-ion batteries (AZIBs) represents a critical advancement toward sustainable, safe, and cost-effective energy storage solutions. In this study, cobalt tungstate (CoWO 4 ) hierarchical microflakes are successfully fabricated via a facile, scalable chemical precipitation-calcination strategy with the aid of hydrazine. The structural evolution is systematically investigated through calcination at 400–700 °C. When assembled by CoWO 4 /Zn batteries, a redox couple at approximately 1.4/1.7 V appears on the cyclic voltammetry profiles. The hierarchical microflakes demonstrate impressive zinc-ion storage performance compared to their microrod counterparts. Specifically, the optimized architecture delivers enhanced initial discharge capacity (177.6 mAh g –1 ), exceptional cycling stability (92.9% capacity retention with reference to the 50th after 1000 cycles) at 0.1 A g –1, improved rate capability (73.7 mAh g –1 at 2 A g –1 ), and a faster zinc-ion diffusion coefficient (9.85 × 10 –13 cm 2 s –1 ). This is attributed to the enlarged active surface area and optimized ion transport pathways. This study not only presents the first demonstration of Zn/CoWO 4 half-cell performance in an aqueous electrolyte but also establishes a viable strategy for designing hierarchical transition-metal tungstate architectures for advanced energy storage 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.075
Threshold uncertainty score0.874

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 science0.0000.000
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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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 routes1
Has abstractyes

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