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Record W4406940661 · doi:10.1088/1361-6528/adafae

Electronic structures of atomic silicon dimer wires as a function of length

2025· article· en· W4406940661 on OpenAlexafffund
Furkan Altincicek, Lucian Livadaru, Christopher C. Leon, Taras Chutora, Roshan Achal, Jeremiah Croshaw, Jason Pitters, Robert A. Wolkow

Bibliographic record

VenueNanotechnology · 2025
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsNational Research Council CanadaNational Institute for NanotechnologyUniversity of Alberta
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsMaterials scienceDimerDangling bondSiliconDensity functional theoryBand gapValence (chemistry)Condensed matter physicsChemical physicsCrystallographyMolecular physicsOptoelectronicsComputational chemistryNuclear magnetic resonanceChemistryPhysics

Abstract

fetched live from OpenAlex

Bare silicon dimers on hydrogen-terminated Si(100) have two dangling bonds. These are atomically localized regions of high state density near to and within the bulk silicon band gap. We studied bare silicon dimers as monomeric units. Silicon dimer wires are much more stable than wires composed of individual dangling bonds. Dimer wires composed of 1-5 dimers were intentionally fabricated and characterized by STM techniques combined with density functional theory to provide detailed insights into geometric and electronic structure. Structural and dynamic qualities displayed by short wires were shown to be similar to the characteristics of a relatively long 37 dimer wire. Rather than adding two states into the band gap, experiment and theory reveal that each dimer adds one empty state into the gap and one filled state into the valence bands. Coupling among these states provides a conduction pathway with small bulk coupling.

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 score0.023
Threshold uncertainty score0.341

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 head0.003
GPT teacher head0.210
Teacher spread0.207 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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