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Record W2000740354 · doi:10.1021/la803036v

Localization vs Conduction: Anionic Excitations in Alkanethiol Self-Assembled Monolayers

2008· article· en· W2000740354 on OpenAlexafffund
Étienne Garand, Md. Golam Moula, P. Rowntree

Bibliographic record

VenueLangmuir · 2008
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Guelph
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsOverlayerExcited stateChemistryMonolayerMethyleneElectronPhotochemistryElectron transferChemical physicsAtomic physicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Low-energy (6-11 eV) electron injection into Xe-coated self-assembled alkanethiol monolayers (SAMs) is reported. At most energies, the presence of the Xe film has negligible effect on the incident electrons, which penetrate the overlayer and induce significant C--H bond rupture at the terminal methyl sites and the subsurface methylene sites of the organic substrates. However, irradiation at 7.7 +/- 0.2 eV can lead to resonant electronic excitations of the Xe adsorbates to create transient anionic states in the Xe overlayer. Transfer of anionic excitations from the Xe overlayer to the SAM initially prepares excited anionic states at the terminal CH3 groups and leads to highly selective dissociations at the methyl sites, with negligible conduction along the alkane chain which would lead to subsurface C-H bond rupture at the methylene sites. These results demonstrate that the mobility of electronically excited charged states along the alkanethiol chains is significantly less than that of simple excess electrons and that highly site-selective chemical modifications can be induced by low-energy electrons in these highly homogeneous organic films.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.204
Teacher spread0.194 · 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

Citations0
Published2008
Admission routes2
Has abstractyes

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