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Record W4221026864 · doi:10.1002/elan.202100704

On the Counter‐intuitive Heterogeneous Electron Transfer Barrier Properties of Alkanethiolate Monolayers on Gold: Smooth versus Rough Surfaces

2022· article· en· W4221026864 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElectroanalysis · 2022
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsNational Institute for NanotechnologyUniversity of British ColumbiaUniversity of Alberta
FundersUniversity of Utah
KeywordsNucleationMonolayerSurface finishElectron transferChemical physicsMaterials scienceSurface roughnessRough surfaceSurface (topology)ElectronNanotechnologyMorphology (biology)ChemistryPhysical chemistryComposite materialMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

Abstract Alkanethiolate monolayers formed on rough gold surfaces can, somewhat surprisingly, act as stronger barriers to heterogeneous electron transfer than those on smooth gold surfaces. This paper presents a possible explanation for this observation by constructing simple geometric models of a “rough” and “smooth” gold surface to examine how microscopic roughness differences can affect the nucleation/growth of the adlayer and size/density of structural defects. Expectedly, the number of defects predicted for adlayers formed on smooth gold is lower than any of those for rough gold. The counter‐intuitive result is that the sizes of a small portion of the defects in the adlayer on the smooth surface are larger than any of those found on the rough surface. The potential implications of these results are discussed.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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