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Record W1993622292 · doi:10.1021/nl900271q

Facet-Dependent Electronic Properties of Hexagonal Silicon Nanowires under Progressive Hydroxylation and Surface Reconstruction

2009· article· en· W1993622292 on OpenAlexafffund
Xiaodan Xu, Peyman Servati

Bibliographic record

VenueNano Letters · 2009
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanowireDensity functional theorySurface reconstructionSiliconBand gapMaterials scienceFacet (psychology)Ab initioCrystallographyMolecular physicsHeterojunctionElectronic structureChemistryComputational chemistryNanotechnologyOptoelectronicsSurface (topology)Geometry

Abstract

fetched live from OpenAlex

The effects of surface reconstruction and progressive hydroxylation on the electronic properties of [110] hexagonal silicon nanowires are investigated by ab initio calculations within the density functional theory. Progressive hydroxylation changes the density of states close to valence band maxima and leads to a general decrease in the band gap. The magnitude of band gap reduction is dependent on the facet where the hydroxyl group is bonded. While a high reduction in band gap (10%) is observed for hydroxylation on (111) facets, for (001) facets the reduction is more pronounced (21%) only when there is a (3 x 1) reconstruction. The reduction in band gap is generally accompanied by an off-center radial shift in the location of the charge density arising from the HOMO. These results go to show the impact of surface reconstruction and termination groups on the electronic properties of Si nanowires, which are important for using these materials for biosensor and transistor applications.

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 score0.003
Threshold uncertainty score0.417

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 head0.192
Teacher spread0.183 · 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

Citations18
Published2009
Admission routes2
Has abstractyes

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