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Record W2034115592 · doi:10.1002/pssa.200925332

Mesoporous GaAs double layers for layer transfer processes

2009· article· en· W2034115592 on OpenAlexfundno aff
Enrique Garralaga Rojas, Barbara Terheiden, Heiko Plagwitz, Carsten Hampe, D. Tutuc, R. J. Haug, Rolf Brendel

Bibliographic record

Venuephysica status solidi (a) · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
FundersInstitute of Gender and Health
KeywordsHydrofluoric acidMaterials scienceElectrolyteEtching (microfabrication)PorosityMesoporous materialLayer (electronics)Substrate (aquarium)Surface finishCurrent densityChemical engineeringNanoporousComposite materialNanotechnologyElectrodeCatalysisChemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract Mesoporous GaAs double layers with different porosities and thicknesses up to 7 µm are formed on highly doped p‐type 4 inch GaAs substrates by means of electrochemical etching in highly concentrated hydrofluoric acid electrolytes. Small 〈111〉 oriented pyramids form at the interface between porous layer and substrate if etching current densities below 7.5 mA cm−2are applied. Porous double layers with different porosities are obtained by varying simultaneously the etching current density and the electrolyte concentration. The porosity of the porous GaAs layers decreases nearly linearly from 69 to 53% with electrolyte concentrations increasing from 30 to 50 wt.%, respectively. The etching process increases the mean roughness of the porous layer surface from 0.15 to 0.24 nm. magnified image

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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.025
GPT teacher head0.287
Teacher spread0.262 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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