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Record W2886709299 · doi:10.1021/acs.jpcc.8b06243

New Interpretation of the Performance of Nickel-Based Air Electrodes for Rechargeable Zinc–Air Batteries

2018· article· en· W2886709299 on OpenAlexafffund
Zachary P. Cano, Moon Gyu Park, Dong Un Lee, Jing Fu, Hao Liu, Michael Fowler, Zhongwei Chen

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsNickelZincElectrodeMaterials scienceMetallurgyChemistry

Abstract

fetched live from OpenAlex

Rechargeable zinc–air batteries with high energy density, cycle life, and calendar life require corrosion-resistant support materials in the air electrode. Nickel-based air electrodes have shown promise in this regard as a substitute for conventional carbon-based air electrodes, but their performance in zinc–air batteries has not been studied in-depth. Specifically, the effect of the nickel (oxy)hydroxide passivating film on the electrode’s catalytic performance and durability requires investigation. To fill this research gap, a method involving electrochemical estimation of the nickel (oxy)hydroxide film capacity was used to link the growth of the film to performance losses experienced on the air electrode after battery cycling. The main cause of voltage loss was the nickel (oxy)hydroxide film growing overtop of and inside the catalyst-coated nickel aggregates. This resulted in significant activation and mass transfer losses, where the latter losses were caused by the film growing overtop of the catalyst and accounted for at least 65% of the total voltage degradation at 10 mA cm–2. Potential modifications to the electrode structure which could mitigate these voltage losses are discussed, including reducing the nickel particle aggregate size, using high-aspect ratio catalysts, and physically separating the catalyst and nickel particles with nonfilm-forming conductive additives.

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 score0.031
Threshold uncertainty score0.208

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 head0.009
GPT teacher head0.256
Teacher spread0.248 · 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

Citations29
Published2018
Admission routes2
Has abstractyes

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