MétaCan
Menu
Back to cohort
Record W4295872844 · doi:10.1088/2043-6262/ac8dec

Effect of surfactant and etching time on p-type porous silicon formation through potentiostatic anodization

2022· article· en· W4295872844 on OpenAlexaff
Gul Zeb, Xuan Tuan Le

Bibliographic record

VenueAdvances in Natural Sciences Nanoscience and Nanotechnology · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsCégep Saint-Jean-sur-RichelieuCMC Microsystems (Canada)
Fundersnot available
KeywordsAnodizingEtching (microfabrication)Materials scienceElectrolytePorous siliconChemical engineeringSiliconElectrodePorosityPulmonary surfactantElectrochemistryConstant currentNanotechnologyChemistryOptoelectronicsComposite materialCurrent (fluid)Layer (electronics)Aluminium

Abstract

fetched live from OpenAlex

Abstract Electrochemical anodization provides the scalability required for structuring porous silicon (PSi) layers for mass production; hence, new and feasible processes are highly sought-after. We investigate the effect of surfactant (additive) and etching time on the morphology of PSi matrix in a simplistic two-electrode anodization cell using aqueous HF electrolyte. Instead of the conventional galvanostatic mode (constant current density), we use the rarely reported technique of potentiostatic anodization (constant applied potential) for engineering PSi surface morphology. We demonstrate that under a constant applied potential, channel-like morphology, pyramids or well-ordered macropores are easily achieved through either increasing the processing time or adding a small amount of surfactant into the electrolyte. Our results provide better understanding of the mechanism underlying the formation of PSi and propose a practical solution for obtaining application-specific macrostructure of PSi.

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)0.001
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 score0.015
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.258
Teacher spread0.254 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Natural Sciences Nanoscience and NanotechnologySame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207