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Record W2956571975 · doi:10.3390/ma12142305

Fabrication of Porous Gold Film Using Graphene Oxide as a Sacrificial Layer

2019· article· en· W2956571975 on OpenAlexaff
Anas Alazzam, Nahla Alamoodi, Mohammad Abutayeh, Ion Stiharu, Vahé Nerguizian

Bibliographic record

VenueMaterials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNanoporous metals and alloys
Canadian institutionsConcordia UniversityÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceGrapheneFabricationOxideNanotechnologyPorosityLayer (electronics)Etching (microfabrication)Substrate (aquarium)Thin filmMicrofabricationSpin coatingComposite materialMetallurgy

Abstract

fetched live from OpenAlex

An original and simple fabrication process to produce thin porous metal films on selected substrates is reported. The fabrication process includes the deposition of a thin layer of gold on a substrate, spin coating of a graphene oxide dispersion, etching the gold film through the graphene oxide layer, and removing the graphene oxide layer. The porosity of the thin gold film is controlled by varying the etching time, the thickness of the gold film, and the concentration of the graphene oxide dispersion. Images by scanning electron and metallurgical microscopes show a continuous gold film with random porosity formed on the substrate with a porosity size ranging between hundreds of nanometers to tens of micrometers. This general approach enables the fabrication of porous metal films using conventional microfabrication techniques. The proposed process is implemented to fabricate electrodes with patterned porosity that are used in a microfluidic system to manipulate living cells under dielectrophoresis. Porous electrodes are found to enhance the magnitude and spatial distribution of the dielectrophoretic force.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2019
Admission routes1
Has abstractyes

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