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Record W2332269594 · doi:10.1021/cs400384h

MoS<sub>2</sub> Nanosheets: A Designed Structure with High Active Site Density for the Hydrogen Evolution Reaction

2013· article· en· W2332269594 on OpenAlexaff
Zhuangzhi Wu, Baizeng Fang, Zhiping Wang, Changlong Wang, Zhihong Liu, Fangyang Liu, Wei Wang, Akram Alfantazi, Dezhi Wang, David P. Wilkinson

Bibliographic record

VenueACS Catalysis · 2013
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTafel equationActive siteElectrocatalystCatalysisMaterials scienceMicrostructureAnnealing (glass)Chemical engineeringWater splittingHydrogenDensity functional theoryStack (abstract data type)NanotechnologySpecific surface areaChemical physicsChemistryElectrochemistryPhysical chemistryComputational chemistryComposite materialElectrodeOrganic chemistryComputer sciencePhotocatalysis

Abstract

fetched live from OpenAlex

Two-dimensional MoS 2 nanosheets (NSs) with high active site density were designed for the hydrogen evolution reaction (HER) through a microdomain reaction method. The effect of the annealing temperature on the microstructure and the HER performance of MoS 2 NSs was examined, and a plausible relation between the stack structures of the MoS 2 catalysts and their HER performance was also explored. The MoS 2 NS electrocatalyst obtained at 550 °C reveals the best HER performance with a relatively small Tafel slope of 68 mV/dec. Both the exposed surface area and active site density are very important for providing a large amount of active sites. The present work has been proved to be an efficient route to achieve a high active site density and a relatively large surface area, which might have potential use in photoelectrocatalytic water splitting.

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)0.000
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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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

Citations376
Published2013
Admission routes1
Has abstractyes

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