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Record W4296001796 · doi:10.1088/2399-1984/ac9022

Layer-structured NiFe nanosheets on CoNi nanowires for enhanced oxygen evolution reaction

2022· article· en· W4296001796 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNano Futures · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Ottawa
FundersNational Natural Science Foundation of China
KeywordsOverpotentialTafel equationOxygen evolutionNanowireMaterials scienceNanosheetCatalysisWater splittingNanotechnologyChemical engineeringElectrolysis of waterElectrolysisElectrochemistryChemistryElectrodePhysical chemistryPhotocatalysis

Abstract

fetched live from OpenAlex

Abstract Efficient electrocatalysts are critical for the oxygen evolution reaction (OER) that occurs during water electrolysis. Herein, a simple and low-cost strategy of assembling CoNi nanowire arrays with NiFe nanosheets on flexible carbon cloth (CC) support as an efficient OER catalyst is developed. This unique ‘nanosheets on nanowires’ structure design increases its specific surface area, enabling access to more active sites. The resulting NiFe@H-CoNi/CC catalyst exhibits excellent OER activity (280 mV overpotential at 100 mA cm −2 ) with a Tafel slope of 36 mV dec −1 and also has outstanding durability at high current operation conditions (over 100 h at 100 mA cm −2 ). Moreover, in-situ Raman analysis suggests that the NiOOH is the realistic OER active phase. This ‘nanosheet on nanowire’ design gives a means for fabricating OER catalysts that are both high-performance and long-lasting.

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.

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 score0.195
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 studies0.0010.000
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 head0.231
Teacher spread0.223 · 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