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Record W2370243202 · doi:10.1002/cctc.201600107

Influence of Carbon on Molybdenum Carbide Catalysts for the Hydrogen Evolution Reaction

2016· article· en· W2370243202 on OpenAlexaff
Chaoyun Tang, Zhuangzhi Wu, Dezhi Wang

Bibliographic record

VenueChemCatChem · 2016
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMinistry of Education and Child Care
FundersCentral South UniversityNational Natural Science Foundation of China
KeywordsTafel equationCatalysisExchange current densityCarbon fibersCarbideCarbonizationMolybdenumHydrogenMaterials scienceInorganic chemistryElectrical resistivity and conductivityChemical engineeringChemistryOrganic chemistryMetallurgyComposite materialPhysical chemistryElectrochemistryAdsorption

Abstract

fetched live from OpenAlex

Abstract The influence of carbon on molybdenum carbide catalysts for the hydrogen evolution reaction (HER) is discussed. The carbon content is adjusted by varying the molar ratio of molybdenum and glucose sources and the holding time in the carbonization process. The carbon plays a crucial role in the determination of phase formation, surface area, and electrical resistance, which are associated with the final HER activity. There is a contradiction between the reduced active sites and improved electrical resistance that results from the reduced content of carbon, and a balance can be achieved with a holding time of 9 h to provide the best HER activity with a low Tafel slope of 55 mV dec−1 and a high exchange current density of 0.047 mA cm−2. Importantly, the transformation of the order of the free carbon improves the electrical conductivity remarkably to result in a great improvement in the final HER 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations37
Published2016
Admission routes1
Has abstractyes

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