MétaCan
Menu
Back to cohort
Record W4407673850 · doi:10.1021/acscatal.4c07970

Basal-Plane Pores Activate Monolayer MoS<sub>2</sub> for the Hydrogen Evolution Reaction

2025· article· en· W4407673850 on OpenAlexafffund
Holly M. Fruehwald, Yossarian Liebsch, Umair Javed, H. Lebius, C. Grygiel, Radia Rahali, Jani Kotakoski, Rodney D. L. Smith

Bibliographic record

VenueACS Catalysis · 2025
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueAustrian Science FundDeutsche ForschungsgemeinschaftCanada Foundation for Innovation
KeywordsMonolayerBasal planeCatalysisChemistryHydrogenMaterials scienceCrystallographyPhotochemistryStereochemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Identification of catalytically relevant sites in solid-state materials and our ability to manipulate such sites is critical to designing improved electrocatalysts. In this work, we prepare a series of monolayer MoS 2 using chemical vapor deposition and install varied concentrations of defects through swift heavy ion irradiation. Electron micrographs indicate that the ion irradiation procedure generates pores within MoS 2 flakes, and Raman microscopic maps show that the defects exert a strongly localized influence. The localization is strong enough that spectra acquired across individual particles can be classified in a binary fashion: regions are either affected by the irradiation-induced pore or appear as pristine MoS 2 . Besides providing insight into the nature of the defects within the monolayers, this feature enables spatial resolution of regions with significant densities of such pores. This capability is used to quantify the defect density across the sample series and show that the pores located within the MoS 2 flakes are particularly active sites for electrocatalytic hydrogen evolution.

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)0.000
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 score0.007
Threshold uncertainty score0.408

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 integrity0.0000.000
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 head0.241
Teacher spread0.232 · 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

Citations16
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueACS CatalysisSame topic2D Materials and ApplicationsFrench-language works237,207