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Hindered surface diffusion of bonded molecular clusters mediated by surface defects

2020· article· en· W3004493233 on OpenAlexafffund
William S. Huxter, Chandra Veer Singh, J. Nogami

Bibliographic record

VenuePhysical Review Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicSurface Chemistry and Catalysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsMaterials scienceAnnealing (glass)Chemical physicsCluster (spacecraft)Surface diffusionSurface (topology)DiffusionNanotechnologyMetalAdsorptionComposite materialPhysical chemistryMetallurgyThermodynamicsChemistry

Abstract

fetched live from OpenAlex

The design of low dimensional materials through surface assisted self-assembly requires a better understanding of the factors that limit and control surface diffusion. We reveal how substrate surface defects hinder the mobility of submonolayer organic adsorbates on a metal surface with the model CuPc/Cu(111) system. Postdeposition annealing bonds CuPc molecules into dendritelike clusters that are often mobile at room temperature. Surface defects on Cu(111) create energetic barriers that prevent CuPc cluster motion on the metal surface. This phenomenon was unveiled by the motion of small clusters that show rigid-body diffusion solely in the available space in between defects. When clusters are sufficiently surrounded by defects, they become completely pinned in place and become immobilized.

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 score0.007
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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