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Record W2152028197 · doi:10.1073/pnas.1006657107

Imprinting self-assembled patterns of lines at a semiconductor surface, using heat, light, or electrons

2010· article· en· W2152028197 on OpenAlexafffund
Iain R. McNab, J. C. Polanyi, Amir Zabet-Khosousi, Werner A. Hofer

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Toronto
FundersRoyal SocietyNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsScanning tunneling microscopeDimerMoleculeSiliconImprinting (psychology)PerpendicularChemical physicsSemiconductorElectronMaterials scienceMolecular physicsChemistryNanotechnologyOptoelectronicsPhysicsGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

The fabrication of nano devices at surfaces makes conflicting demands of mobility for self-assembly (SA) and immobility for permanence. The solution proposed in earlier work from this laboratory involved pattern formation in physisorbed molecules by SA, followed by localized reaction to chemically imprint the pattern substantially unchanged, a procedure we termed molecular-scale imprinting (MSI). Here, as proof of generality we extended this procedure, previously applied to imprinting circles on Si(111)-7 × 7, to SA lines of 1-chloropentane (CP) on Si(100)-2 × 1. The physisorbed lines consisted of pairs of CP that grew perpendicular to the Si dimer rows, as shown by scanning tunneling microscopy and ab initio theory. Chemical reaction of these lines with the surface was triggered in separate experiments by three different modes of energization: heat, electrons, or light. In all cases the CP molecules underwent MSI with a Si atom beneath so that the physisorbed lines of CP pairs were imprinted as chemisorbed lines of Cl pairs.

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 score0.011
Threshold uncertainty score0.261

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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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

Citations28
Published2010
Admission routes2
Has abstractyes

Explore more

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