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Record W2345989811 · doi:10.1021/acsami.6b03326

Exclusively Gas-Phase Passivation of Native Oxide-Free Silicon(100) and Silicon(111) Surfaces

2016· article· en· W2345989811 on OpenAlexfundno aff
Ye Tao, Roland Hauert, Christian L. Degen

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
FundersEuropean Research CouncilNational Center of Competence in Research Quantum Science and TechnologyUniversität ZürichConcordia UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsMaterials sciencePassivationSiliconGas phaseSilicon oxideOxidePhase (matter)NanotechnologyChemical engineeringOptoelectronicsMetallurgyPhysical chemistryOrganic chemistryLayer (electronics)Silicon nitride

Abstract

fetched live from OpenAlex

Reactions in the gas phase are of primary technological importance for applications in nano- and microfabrication technology and in the semiconductor industry. We present exclusively gas-phase protocols to chemically passivate oxide-free Si(111) and Si(100) surfaces with short-chain alkynes. The resulting surfaces showed equal or better oxidation resistance than most existing liquid-phase-derived surfaces and rivaled the outstanding stability of a full-coverage Si(111)-propenyl surface.1,2 The most stable surface (Si(111)-ethenyl) grew one-fifth of a monolayer of oxide (0.04 nm) after 1 month of air exposure. We monitored the regrowth of oxides on passivated Si(111) and Si(100) surfaces by X-ray photoelectron spectroscopy (XPS) and observed a significant crystal-orientation dependence of initial rates when total oxide thickness was below approximately one monolayer (0.2 nm). This difference was correlated with the desorption kinetics of residual surface Si-F bonds formed during HF treatment. We discuss applications of the technology and suggest future directions for process optimization.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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.

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

Citations14
Published2016
Admission routes1
Has abstractyes

Explore more

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