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Record W1979785388 · doi:10.1116/1.4869299

Recycling gold nanohole arrays

2014· article· en· W1979785388 on OpenAlexafffund
Donna Hohertz, Sean F. Romanuik, Bonnie L. Gray, K. L. Kavanagh

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2014
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmmonium hydroxideMonolayerSurface plasmon resonanceNitric acidHydrogen peroxideAqueous solutionAdsorptionHydroxideMaterials sciencePlasmonNanotechnologyChemistryAnalytical Chemistry (journal)Inorganic chemistryChemical engineeringOptoelectronicsOrganic chemistryNanoparticle

Abstract

fetched live from OpenAlex

The authors report the impact of common cleaning methods on the stability of gold nanohole arrays used as extraordinary optical transmission surface plasmon resonance sensors. Their optical sensitivity, physical structure, and surface contamination levels were measured before and after multiple cycles of monolayer adsorption and removal with various wet chemicals (sulfochromic acid, piranha, or ammonium hydroxide: hydrogen peroxide) and dry oxygen plasma etchants. While these oxidative chemical and plasma etches remove organic monolayers and other contaminants, the oxidation and associated heating also damages the gold nanostructures to varying degrees. The authors observed decreases in the arrays' optical sensitivities via changes in the shapes and positions of their surface plasmon resonance peaks. The optimum recycling process was a room temperature, aqueous ammonium hydroxide: hydrogen peroxide treatment (15 min) commonly referred to as Radio Corporation of America Clean 1, followed by immersion in dilute nitric acid (0.1M, 30 min). This method was effective at removing an alkanethiol self-assembled monolayer of 11-mercaptoundecanoic acid; after six recycles, no loss in optical sensitivity was detected with minimal changes in the gold film thickness (−10%), hole area (−10%), and hole circularity (+6%).

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 score0.207
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.205
Teacher spread0.201 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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