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Record W2507601663 · doi:10.1021/acs.jpcc.5b09211

Retention of Bond Direction in Surface Reaction: A Comparative Study of Variously Aligned <i>p</i>-Dihalobenzenes on Cu(110)

2015· article· en· W2507601663 on OpenAlexafffund
Lydie Leung, Tingbin Lim, Zhanyu Ning, J. C. Polanyi, Wei Ji, Chenguang Wang

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaRenmin University of ChinaNational Natural Science Foundation of ChinaMinistry of Science and Technology of the People's Republic of ChinaXerox
KeywordsRecoilChemistryAntibonding molecular orbitalAtom (system on chip)HalogenSurface (topology)CrystallographyElectronComputational chemistryMolecular physicsAtomic physicsGeometryPhysicsOrganic chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Previous studies indicated that the reagent bond direction of a bond being broken in surface reaction dominated the subsequent product recoil direction. Here we test this in an STM study of the electron-induced bond breaking for three clearly different alignments of each of two dihalobenzene reactions on Cu(110). A strong correlation was observed between the physisorbed adsorbate bond direction and the subsequent recoil direction of the chemisorbed halogen-atom product. The correlation was also evident in the theoretical modeling for the case of variously aligned diiodobenzene. The theory employed the impulsive two-state (I2S) approach to compute the reaction dynamics following electron attachment. This showed that the correlation between the prior bond direction and the subsequent product angular distribution was due to the directionality of the antibonding repulsion responsible for extending the molecule’s carbon–halogen bond, en route to reaction. Retention of bond direction in reaction dominated the effect of differing roughness of the surface along markedly different crystal axes.

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 score0.050
Threshold uncertainty score0.260

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 head0.019
GPT teacher head0.252
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

Citations11
Published2015
Admission routes2
Has abstractyes

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