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Record W4385268388 · doi:10.1139/cjc-2023-0047

Arrayed and entangled silicon nanowires using Au nanoparticle catalysts prepared by pulsed laser-induced dewetting

2023· article· en· W4385268388 on OpenAlexafffundvenue
Alison Joy Fulton, Yujun Shi

Bibliographic record

VenueCanadian Journal of Chemistry · 2023
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsDewettingNanowireCatalysisNanoparticleNanotechnologySaturation (graph theory)Chemical engineeringChemical vapor depositionSiliconChemistryFabricationMaterials scienceOptoelectronicsThin filmOrganic chemistry

Abstract

fetched live from OpenAlex

The use of pulsed laser-induced dewetting (PLiD) is reported as a novel approach in the fabrication of Au nanoparticle (NP) catalytic arrays for the growth of Si nanowires (NWs) by chemical vapor deposition using SiCl 4 in the presence of H 2 . On polished Si substrates, PLiD generates Au NP catalysts with long-range order and narrow size distributions. It has been shown that the monodispersed distribution of Au NPs provides consistent diameter control of the as-grown Si NWs. A systematic exploration of the Si NW synthesis time, temperature, and gas flow rates illustrates a level of tunability in terms of morphology, be it arrayed or entangled Si NWs, with varying experimental parameters. An investigation of the effect of growth temperature also showed that Si NWs can be synthesized at temperatures as low as 700 °C when using SiCl 4 as the precursor. The use of porous Si substrates enabled direct observation of the diameter-dependent growth due to the simultaneous presence of three Au NP size distributions. Growth from the small- and medium-sized Au NP catalysts occurred first, followed by that from the large-sized Au NPs, which was only observed at extended times or high SiCl 4 flow rates. The delayed onset of growth from the large-sized Au NPs is due to the longer time to achieve Si super-saturation of larger catalyst NPs. The morphology and diameter control of the as-grown Si NWs reported in this work makes this approach potentially useful toward applications such as nanoelectronics, sensors, and lithium ion battery electrodes depending on the desired morphology.

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 score0.514

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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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