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Record W2051662853 · doi:10.1063/1.1367402

Surface reactivity of Si nanowires

2001· article· en· W2051662853 on OpenAlexafffund
Xuhui Sun, Hongying Peng, Yun Tang, W. S. Shi, Ning‐Bew Wong, Chun‐Sing Lee, S. T. Lee, Tsun‐Kong Sham

Bibliographic record

VenueJournal of Applied Physics · 2001
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Hong KongCity University of Hong Kong
KeywordsX-ray photoelectron spectroscopyMaterials scienceTransmission electron microscopyElectron energy loss spectroscopyScanning electron microscopeSiliconEnergy-dispersive X-ray spectroscopyReactivity (psychology)NanowireNanostructureChemical engineeringCopperNanotechnologyOxideSpectroscopySurface modificationMetallurgyComposite material

Abstract

fetched live from OpenAlex

The chemical reactivity of hydrogen-passivated surface of silicon nanowires (SiNWs) towards the reductive deposition of silver and copper ions from solution is reported. SiNWs synthesized by laser ablation were used in the investigation. The surface properties of SiNWs after the removal of the surface oxide were studied. It is found that the surface silicon of the SiNWs can readily reduce silver (I) and copper (II) ions to metal aggregates of various morphologies on the SiNW surface at room temperature. The reaction products have been characterized with scanning electron microscopy, energy dispersive x-ray spectroscopy, high-resolution transmission electron microscopy, electron energy loss spectroscopy, and x-ray photoelectron spectroscopy. By varying the concentration of Ag(I) ions in the solution, nanostructures of silver with different shapes and sizes can be obtained. This approach for synthesis of metal nanostructures offers a potential method for the preparation of desired metal catalysts.

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 score0.077
Threshold uncertainty score0.307

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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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

Citations36
Published2001
Admission routes2
Has abstractyes

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