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Record W4211064742 · doi:10.3390/molecules27041199

Part II: NiMoO4 Nanostructures Synthesized by the Solution Combustion Method: A Parametric Study on the Influence of Material Synthesis and Electrode-Fabrication Parameters on the Electrocatalytic Activity in the Hydrogen Evolution Reaction

2022· article· en· W4211064742 on OpenAlexafffund
Mahmoud Bassam Rammal, Vincent El-Ghoubaira, Sasha Omanovic

Bibliographic record

VenueMolecules · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsElectrocatalystCalcinationMaterials scienceChemical engineeringCarbon blackNafionCatalysisCarbon fibersOxideNon-blocking I/OInorganic chemistryElectrodeComposite numberElectrochemistryChemistryComposite materialMetallurgyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Earth-abundant NiMo-oxide nanostructures were investigated as efficient electrocatalytic materials for the hydrogen evolution reaction (HER) in acidic media. Synthesis and non-synthesis parameters were thoroughly studied. For the non-synthesis parameters, the variation in Nafion loading resulted in a volcano-like trend, while the change in the electrocatalyst loading showed that the marginal benefit of high loadings attenuates due to mass-transfer limitations. The addition of carbon black to the electrocatalyst layer improved the HER performance at low loadings. Different carbon black grades showed a varying influence on the HER performance. Regarding the synthesis parameters, a calcination temperature of 500 °C, a calcination time between 20 and 720 min, a stoichiometric composition (Ni/Mo = 1), an acidic precursor solution, and a fuel-lean system were conditions that yielded the highest HER activity. The in-house NiMoO4/CB/Nafion electrocatalyst layer was found to offer a better long-term performance than the commercial Pt/C.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.016
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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