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Record W1980598008 · doi:10.1021/jp072011l

Experimental and Theoretical Studies of Trimethylene Sulfide-Derived Nanostructures on p- and n-Type H-Silicon(100)-2 × 1

2007· article· en· W1980598008 on OpenAlexaff
Stanislav A. Dogel, Gino A. DiLabio, Janik Zikovsky, Jason Pitters, Robert A. Wolkow

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2007
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsDangling bondSiliconScanning tunneling microscopeDopantNanostructureCovalent bondCrystallographyMaterials scienceNanotechnologyDensity functional theoryChemistryChemical physicsComputational chemistryDopingOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

The nanoscale structuring of molecules on silicon surfaces is one approach for combining the tuneable properties of chemical species with the functionality of semiconductor materials. In this study, we report on the growth characteristics of trimethylene sulfide (TMS) on p- and n-type H−Si(100)-2 × 1. The nanostructures formed by TMS on either surface are indistinguishable by scanning tunneling microscopy (STM). However, high-resolution electron energy loss spectroscopy (HREELS) and modeling by density functional theory indicate that the molecular attachment mechanism differs with dopant type. Our results show that TMS adds to a surface silicon dangling bond through the formation of a Si−S bond on p-type silicon and through the formation of a Si−C bond on n-type silicon. In both cases, the added TMS undergoes ring opening following covalent bond formation with the surface. The different ring-opened radicals are able to abstract a hydrogen atom from one of two neighboring silicon dimers. The overall reaction produces TMS-derived nanostructures that grow via a square-wave pattern on the neighboring edges of two dimer rows.

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 score0.303

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 head0.263
Teacher spread0.254 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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