MétaCan
Menu
Back to cohort
Record W2324589062 · doi:10.1021/nn503721h

How Adsorbate Alignment Leads to Selective Reaction

2014· article· en· W2324589062 on OpenAlexafffund
Fang Cheng, Wei Ji, Lydie Leung, Zhanyu Ning, J. C. Polanyi, Chenguang Wang

Bibliographic record

VenueACS Nano · 2014
Typearticle
Languageen
FieldEngineering
TopicSurface Chemistry and Catalysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNanotechnologyChemical physicsChemistry

Abstract

fetched live from OpenAlex

There has been much interest in the effect of adsorbate alignment in a surface reaction. Here we show its significance for an electron-induced reaction occurring along preferred axes of the asymmetric Cu(110) surface, characterized by directional copper rows. By scanning tunneling microscopy (STM), we found that the heterocyclic aromatic reagent, physisorbed meta-iodopyridine, lay with its carbon-iodine either along the rows of Cu(110), "A", or perpendicular, "P". Electron-induced dissociative attachment with the C-I bond initially along "A" gave a chemisorbed I atom and chemisorbed vertical pyridyl, singly surface-bound, whereas that with C-I along "P" gave a chemisorbed I atom and a horizontal pyridyl, doubly bound. An impulsive two-state model, involving a short-lived antibonding state of C-I, accounted for the different product surface binding in terms of closer Cu···Cu atomic spacing along "A" accommodating only one binding site of the pyridyl ring recoiling from I and wider spacing along "P" accommodating simultaneously both binding sites, N-Cu and C-Cu, in the meta-position on the recoiling pyridyl ring. STM studies combined with dynamical modeling can be seen as a way to improve understanding of the role of surface alignment in determining reactive outcomes in induced reaction at asymmetric crystalline surfaces.

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 score0.032
Threshold uncertainty score0.346

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueACS NanoSame topicSurface Chemistry and CatalysisFrench-language works237,207