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Record W2606871992 · doi:10.1109/tnano.2017.2694428

Formation Mechanism of Silicon Nanowires Using Chemical/Electrochemical Process

2017· article· en· W2606871992 on OpenAlexafffund
Parsoua Abedini Sohi, Mojtaba Kahrizi

Bibliographic record

VenueIEEE Transactions on Nanotechnology · 2017
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanowireSilicon nanowiresSiliconMechanism (biology)Materials scienceElectrochemistryProcess (computing)NanotechnologyOptoelectronicsElectrodeChemistryComputer sciencePhysics

Abstract

fetched live from OpenAlex

In this work, Si nanostructure arrays are fabricated using a low-cost chemical/electrochemical etching method. The technique consists of two consecutive chemical and electrochemical etching steps. A mask-less and nonlithographical technique of anisotropic wet etching of silicon samples in hydroxide solutions was used to generate pyramid shape seeding points. The subsequent fabrication stages consist of electrochemically etching to generate nanowires. The growth mechanism of the nanowires was investigated experimentally in order to find out the effects of various fabrication parameters on the physical properties of the nanowires like their structures, shapes, sizes, aspect ratio, and morphologies. Modeling and simulation of the nanowires growth are performed using multiphysics software tool, COMSOL, in order to explain and confirm the experimental results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.243
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2017
Admission routes2
Has abstractyes

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