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Record W4292327138 · doi:10.1021/acsanm.2c01991

CoNi Nanoparticle-Decorated ZIF-67-Derived Hollow Carbon Cubes as a Bifunctional Electrocatalyst for Zn–Air Batteries

2022· article· en· W4292327138 on OpenAlexafffund
Yingjie He, Zahra Abedi, Chuyi Ni, Sarah Milliken, Kevin O’Connor, Douglas G. Ivey, Jonathan G. C. Veinot

Bibliographic record

VenueACS Applied Nano Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundAlberta Innovates
KeywordsBifunctionalMaterials scienceElectrocatalystNanoparticleChemical engineeringCarbon fibersBattery (electricity)CarbonizationNanotechnologyElectrochemistryElectrodeComposite materialCatalysisChemistryPhysical chemistryOrganic chemistryScanning electron microscopePower (physics)

Abstract

fetched live from OpenAlex

CoNi nanoparticle-decorated hollow carbon cubes (CoNi-HCCs) were synthesized using ZIF-67 as a sacrificial template. The synthesis utilizes the thermal instability of ZIF-67, creating hollow carbon nanostructures while facilitating carbonization and introducing metal nanoparticles (e.g., CoNi). Prototype Zn–air batteries equipped with CoNi-HCCs exhibited a promising discharge potential of 1.21 V and a charge potential of 2.04 V at 20 mA cm–2. These values are superior to those of Pt–Ru, whose discharge and charge potentials were 1.20 and 2.08 V, respectively. CoNi-HCCs also displayed a high peak power density of 159.6 mW cm–2, which is significantly higher than the value for Pt–Ru (120.2 mW cm–2). After 90 h of bifunctional cycling at 10 mA cm–2, CoNi-HCCs only experienced an efficiency loss of 3.4% and maintained 55.3% battery efficiency, much more durable than Pt–Ru (41.7% after only 60 h).

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), Insufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

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