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Record W2085549178 · doi:10.1116/1.4913877

Promising method toward realization of ultra-low-cost silicon microrod array with nanotip

2015· article· en· W2085549178 on OpenAlexafffund
Bahareh Yaghootkar, Mojtaba Kahrizi

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2015
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsSiliconEtching (microfabrication)AnodizingSubstrate (aquarium)Materials scienceNanotechnologyOptoelectronicsAnodeFabricationPyramid (geometry)OpticsElectrodeChemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

A low-cost two-step method for fabrication of silicon microrod array with nanotip on a low-doped p-type substrate was demonstrated. The two-step process involved anisotropic and electrochemical etching of single crystalline silicon samples. The silicon surface was first textured using anisotropic etching to generate the pyramid-shaped structure on the surface and was further followed by electrochemical anodic etching to create silicon microrod arrays. The vertical silicon microrod arrays are a direct product of the anodization stage, where the shape of the pyramid structures was altered and transformed into free-standing microrods. The effects of several parameters including the time, the pyramid size uniformity, and HF concentration on the final products were studied. It was observed that the diameters of the pyramids were decreased as the anodic etching time was extended to 10 min. Beyond 10 min, anodic etching did not cause any further diameter change. Experiments revealed that in order to realize silicon microrods, the size of the pyramids was required to be greater than the space charge region width. An optimal range of HF concentration, where the silicon microrods can be obtained was determined.

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)0.002
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 score0.082
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.240
Teacher spread0.222 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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