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Record W2315795651 · doi:10.1166/jnn.2009.se43

Nickel-Based 3D Electrocatalyst Layers for Production of Hydrogen by Water Electrolysis in an Acidic Medium

2009· article· en· W2315795651 on OpenAlexaff
Ziad Bou-Saleh, Sasha Omanovic

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsMcGill University
FundersNational Institute for International Education
KeywordsElectrocatalystMaterials sciencePolyanilineNickelChemical engineeringElectrolysisSubstrate (aquarium)CatalysisInorganic chemistryElectrodeElectrochemistryChemistryComposite materialMetallurgyOrganic chemistryElectrolytePolymer

Abstract

fetched live from OpenAlex

Electrocatalytic activity of three-dimensional nickel-based layers in the hydrogen evolution reaction (HER) was investigated in an acidic medium. It was demonstrated that patterning of a glassy carbon electrode substrate with a 3D polyaniline (PANI) matrix is a convenient way of increasing the electrocatalytically active surface area of electrodeposited Ni, and hence its apparent electrocatalytic activity. The optimized PANI/Ni electrocatalyst layer showed a significantly higher activity in the HER then a two-dimensional control Ni-plate surface. It was also demonstrated that it is possible to produce a Ni-based HER electrocatalyst layer by synthesizing Ni nanoparticles and supporting them on Vulcan carbon. This electrocatalyst also offered a significantly higher electrocatalytic activity in the HER then the control surface, but lower then the optimized PANI/Ni electrocatalyst. The electrocatalytic activity of the optimized PANI/Ni layer was also compared to the activity of a 3D catalyst produced by coating a porous RVC cube substrate with Ni. This electrocatalyst showed the highest HER electrocatalytic activity among the investigated layers when tested under potentiodynamic polarization conditions. However, under the potentiostatic conditions, the optimized PANI/Ni layer showed the highest electrocatalytic activity.

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)0.001
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 score0.008
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.258
Teacher spread0.249 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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