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Record W2045498003 · doi:10.1063/1.3562367

Indications of field-directing and self-templating effects on the formation of organic lines on silicon

2011· article· en· W2045498003 on OpenAlexaff
Janik Zikovsky, Stanislav A. Dogel, Mark Salomons, Jason Pitters, Gino A. DiLabio, Robert A. Wolkow

Bibliographic record

VenueThe Journal of Chemical Physics · 2011
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsDangling bondSiliconScanning tunneling microscopeChemical physicsNanostructurePerpendicularDimerMoleculeNanotechnologyAtom (system on chip)Materials scienceSurface diffusionChemistryCrystallographyOptoelectronicsAdsorptionGeometryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

It has previously been shown that multimolecular organic nanostructures form on H-Si(100)-2×1 via a radical mediated growth process. In this mechanism, growth begins through the addition of a molecule to a silicon surface dangling bond, followed by the abstraction of a neighboring H atom and generation of a new dangling bond on the neighboring site. Nanostructures formed by this mechanism grow along one edge of a dimer row. Here, we explored the possibility of using lithographically prepared, biased metal contacts on the silicon surface to generate an electric field that orients molecules during the growth process to achieve growth in the perpendicular-to-row direction. The formation of some nanostructures in a direction that was nearly perpendicular to the dimer rows was achieved, whereas such features were not formed in the absence of the field. Analysis of the scanning tunneling microscopy images suggests that the formation of these nanostructures may involve self-templating effects in addition to dangling bond diffusion rather than a straightforward addition∕abstraction mechanism. These initial results offer some indication that a molecular pattern writer can be achieved.

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 score0.004
Threshold uncertainty score0.106

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.198
Teacher spread0.189 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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