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Record W3127004357 · doi:10.1002/celc.202001501

Tuning Overall Water Splitting on an Electrodeposited NiCoFeP Films

2021· article· en· W3127004357 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

VenueChemElectroChem · 2021
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsOverpotentialWater splittingOxygen evolutionNanomaterial-based catalystElectrochemistryCathodeCatalysisBifunctionalMaterials scienceAnodeTransition metalChemical engineeringNanotechnologyElectrocatalystElectrodeInorganic chemistryChemistryPhysical chemistryNanoparticle

Abstract

fetched live from OpenAlex

Abstract The regulation of geometric and electronic structures is very important to transition metal phosphides with high catalytic properties. Herein, nanofilms consisting of NiCoFeP nanospheres were electrochemically deposited onto 3D Ni foam for the catalysis of water splitting. Depending on the amount of iron, the NiCoFeP nanocatalysts exhibit different surface topographies and chemical binding energies, which have a great influence on electrochemical performances. The optimal NiCoP‐0.01 M Fe films show robust bifunctional electrocatalytic activities with an overpotential of 118 mV (10 mA cm −2 ) for the hydrogen evolution reaction (HER) and 300 mV (50 mA cm −2 ) for the oxygen evolution reaction (OER). The overall water splitting employing this proposed nanocatalyst at both electrodes only presents a cell voltage of 1.60 V at the current density of 10 mA cm −2 , far lower than that (1.73 V) with the commercial Pt/C cathode and RuO 2 anode combination.

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 score0.034
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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