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Record W4413344748 · doi:10.1016/j.gce.2025.08.002

Controllable etching construction of nickel-based Prussian blue analog nanocages for stabilized energy storage in aqueous nickel-zinc batteries

2025· article· en· W4413344748 on OpenAlexaff
Ziming Qiu, Songtao Zhang, Xingye Lu, Zhenyang Meng, Shixian Wang, Shuai Cao, Qian Li, Tianchen Wang, Yi Xu, Mohsen Shakouri, Yecan Pi, Huan Pang

Bibliographic record

VenueGreen Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersGraduate Research and Innovation Projects of Jiangsu ProvinceYangzhou UniversityNational Natural Science Foundation of China
KeywordsPrussian blueNickelNanocagesZincEtching (microfabrication)Materials scienceAqueous solutionEnergy storageMetallurgyNanotechnologyChemistryElectrochemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Aqueous nickel-zinc batteries (NZBs) are well-suited for large-scale energy storage owing to their safety and low cost. Yet, their nickel-based cathodes encounter issues like particle fragmentation caused by lattice stress accumulation and irreversible phase changes during charge-discharge cycles. In this study, nickel-cobalt Prussian blue analog nanocages (NC-NiCo-PBA) with an octahedral cavity structure were successfully prepared using ammonia complex etching. Structural characterization revealed that the nanocage retained an intact PBA skeleton, with the specific surface area enhanced to 151.38 m 2 /g, which was a 5.2% increase over the original solid particles. The octahedral hollow cavity structure significantly reduces ion transfer distance and alleviates volume strain, thereby markedly improving electrochemical performance. This study provides a new approach for the structural design of PBA-based cathode materials and validates the critical role of hollow nanostructures in enhancing the energy storage performance of aqueous 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 score0.370
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 communication0.0000.000
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 head0.006
GPT teacher head0.227
Teacher spread0.220 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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