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Record W2066900600 · doi:10.1002/pssc.200982505

The role of a precursor state in thiophene chemisorption on Si(111)–7×7

2009· article· en· W2066900600 on OpenAlexaff
Alfred J. Weymouth, R. H. Miwa, G. P. Srivastava, A. B. McLean

Bibliographic record

VenuePhysica status solidi. C, Conferences and critical reviews/Physica status solidi. C, Current topics in solid state physics · 2009
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsQueen's University
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsChemisorptionThiopheneScanning tunneling microscopeAdsorptionChemistryChemical physicsAb initioMoleculeComputational chemistryKinetic Monte CarloPhysical chemistryMonte Carlo methodMaterials scienceNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The adsorption of thiophene on Si(111)–7×7 has been studied with scanning tunneling microscopy (STM) and kinetic Monte Carlo (kMC) modelling. Previous experimental studies of this system clearly demonstrated that thiophene prefers to chemisorb on the faulted half of the 7×7 unit cell. The STM studies reported here concur with this and provide further information about thiophene site preference as a function of coverage. Additionally, an ab initio theoretical investigation of this system demonstrated that the occupancy of available adsorption sites could not be explained using equilibrium binding energies, as these were calculated to be the same for all experimentally identified adsorption sites (≈1.0 eV). To investigate the possibility that site selection is kinetically controlled, a kMC model was developed. This model places the molecule in a mobile precursor state, allowing the molecule to traverse the surface before chemisorbing. The kMC model was found to reproduce the STM data, providing compelling evidence that site occupancy in this system is indeed kinetically controlled at room temperature. Activation energy differences, for each the four unique chemisorption geometries, could be extracted from a fit of the kMC model predictions to the experimental data (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.302
Teacher spread0.283 · 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.

Study designTheoretical or conceptual
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

Citations5
Published2009
Admission routes1
Has abstractyes

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