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Record W2891517951 · doi:10.1063/1.5041373

Macroporous silicon formation by electrochemical anodization of n-type silicon without illumination

2018· article· en· W2891517951 on OpenAlexafffund
Alison Joy Fulton, Vinayaraj Ozhukil Kollath, Kunal Karan, Yujun Shi

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsMaterials scienceAnodizingPorous siliconSiliconDissolutionChemical engineeringMicroelectronicsRaman spectroscopyNanoporeCrystallinityNanotechnologyOptoelectronicsComposite materialOptics

Abstract

fetched live from OpenAlex

This work reports the electrochemical anodization of low-doped n-type silicon in aqueous hydrofluoride (HF) solution without the use of external illumination to generate macroporous silicon with a thin mesoporous transition layer. We have shown that pore formation during the electrochemical anodization of low-doped n-Si in the dark is due to the avalanche breakdown mechanism. Studies of dissolution valence revealed a competition between divalent direct and tetravalent indirect dissolution processes. The effect of pore morphology on anodization parameters such as applied potential, HF concentration, and anodization time was systematically investigated. The fabricated porous silicon has well-separated and straight macropores of pore diameters ranging from 89 ± 9 to 285 ± 28 nm and with limited branching or interconnectivity. Pore diameter uniformity is maintained throughout the porous layer. XRD and Raman spectroscopy have shown that the porous Si fabricated here is highly crystalline, retaining its original crystallinity. The fabricated porous Si presented in this work with tunable pore sizes, depths, and surface features can have potential applications in various fields of microelectronics, photonics, and sensors.

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 score0.002
Threshold uncertainty score0.451

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 head0.236
Teacher spread0.229 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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