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Record W2107455356 · doi:10.1186/1556-276x-8-155

Lithography-free fabrication of silicon nanowire and nanohole arrays by metal-assisted chemical etching

2013· article· en· W2107455356 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNanoscale Research Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceDewettingNanotechnologySiliconIsotropic etchingNanochemistryEtching (microfabrication)FabricationLithographyNanoparticleAnnealing (glass)WaferSubstrate (aquarium)NanostructureNanowireOptoelectronicsThin filmComposite material

Abstract

fetched live from OpenAlex

We demonstrated a novel, simple, and low-cost method to fabricate silicon nanowire (SiNW) arrays and silicon nanohole (SiNH) arrays based on thin silver (Ag) film dewetting process combined with metal-assisted chemical etching. Ag mesh with holes and semispherical Ag nanoparticles can be prepared by simple thermal annealing of Ag thin film on a silicon substrate. Both the diameter and the distribution of mesh holes as well as the nanoparticles can be manipulated by the film thickness and the annealing temperature. The silicon underneath Ag coverage was etched off with the catalysis of metal in an aqueous solution containing HF and an oxidant, which form silicon nanostructures (either SiNW or SiNH arrays). The morphologies of the corresponding etched SiNW and SiNH arrays matched well with that of Ag holes and nanoparticles. This novel method allows lithography-free fabrication of the SiNW and SiNH arrays with control of the size and distribution.

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.

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 score0.041
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.256
Teacher spread0.240 · 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