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Record W2040213608 · doi:10.1088/1468-6996/12/4/045001

Metal-assisted chemical etching using sputtered gold: a simple route to black silicon

2011· article· en· W2040213608 on OpenAlexaff
Agnieszka Kurek, Seán T. Barry

Bibliographic record

VenueScience and Technology of Advanced Materials · 2011
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsCarleton University
Fundersnot available
KeywordsBlack siliconMaterials scienceSiliconEtching (microfabrication)Aqua regiaSputteringIsotropic etchingMetalNanotechnologyOptoelectronicsSonicationComposite materialChemical engineeringMetallurgyLayer (electronics)Thin film

Abstract

fetched live from OpenAlex

We report an accessible and simple method of producing ‘black silicon’ with aspect ratios as high as 8 using common laboratory equipment. Gold was sputtered to a thickness of 8 nm using a low-vacuum sputter coater. The structures were etched into silicon substrates using an aqueous H2O2/HF solution, and the gold was then removed using aqua regia. Ultrasonication was necessary to produce columnar structures, and an etch time of 24 min gave a velvety, non-reflective surface. The surface features after 24 min etching were uniformly microstructured over an area of square centimetres.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.271
Teacher spread0.248 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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