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Record W4319598566 · doi:10.3390/coatings13020375

Synthesis of Optoelectronic Nanostructures on Silicon and Gold-Coated Silicon via High-Intensity Laser Pulses at Varied Pulse Durations

2023· article· en· W4319598566 on OpenAlexafffund
Nishant Singh Jamwal, Amirkianoosh Kiani

Bibliographic record

VenueCoatings · 2023
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSiliconMaterials scienceX-ray photoelectron spectroscopySpectroscopyOptoelectronicsNanostructureScanning electron microscopeBand gapLaserAnalytical Chemistry (journal)OpticsNanotechnologyChemistryNuclear magnetic resonance

Abstract

fetched live from OpenAlex

This work defines the generation of nanostructures on silicon and gold-coated silicon substrates by tuning the pulse duration of our proposed method: ultra-short laser pulses for in situ nanostructure generation (ULPING) under ambient conditions. The method is a single-step novel method which is efficient in synthesizing nanostructures on the substrates. We observed a higher nanofiber generation at a shorter pulse duration using Scanning Electron Microscopy (SEM) imaging. Silicon oxide formation was confirmed by Energy-dispersive X-ray spectroscopy (EDX) and X-ray photoelectron spectroscopy (XPS) analysis and a band gap of 8.19 eV was achieved for the Si + Au sample, which was determined by the Reflection Electron Energy Loss Spectroscopy (REELS) spectra. A high valence band offset of 4.93 eV was measured for the silicon-based samples for the Si/SiO2 interface. The addition of gold nanoparticles decreased the band gap and we observed a blue shift in optical conductivity for samples with nanofibers using optical spectroscopy.

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 score0.021
Threshold uncertainty score0.770

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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 routes2
Has abstractyes

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