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Record W2968449927 · doi:10.1021/acsnano.9b05226

Single Atomic Vacancy Catalysis

2019· article· en· W2968449927 on OpenAlexaff
Jieun Yang, Yan Wang, Maureen J. Lagos, Viacheslav Manichev, Raymond Fullon, Xiuju Song, Damien Voiry, Sudip Chakraborty, Wenjing Zhang, Philip E. Batson, L. C. Feldman, Torgny Gustafsson, Manish Chhowalla

Bibliographic record

VenueACS Nano · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMcMaster University
FundersAir Force Office of Scientific ResearchDivision of Electrical, Communications and Cyber SystemsDivision of Civil, Mechanical and Manufacturing Innovation
KeywordsVacancy defectTafel equationCatalysisAtom (system on chip)Transition metalMaterials scienceChemical physicsStrain (injury)Electrical resistivity and conductivityNanotechnologyChemistryCondensed matter physicsElectrochemistryCrystallographyPhysical chemistryPhysicsElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Single atom catalysts provide exceptional activity. However, measuring the intrinsic catalytic activity of a single atom in real electrochemical environments is challenging. Here, we report the activity of a single vacancy for electrocatalytically evolving hydrogen in two-dimensional (2D) MoS 2 . Surprisingly, we find that the catalytic activity per vacancy is not constant but increases with its concentration, reaching a sudden peak in activity at 5.7 × 10 14 cm –2 where the intrinsic turn over frequency and Tafel slope of a single atomic vacancy was found to be ∼5 s –1 and 44 mV/dec, respectively. At this vacancy concentration, we also find a local strain of ∼3% and a semiconductor to metal transition in 2D MoS 2 . Our results suggest that, along with increasing the number of active sites, engineering the local strain and electrical conductivity of catalysts is essential in increasing their 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)0.000
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 score0.003
Threshold uncertainty score0.011

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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

Citations157
Published2019
Admission routes1
Has abstractyes

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