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Record W2791714377 · doi:10.1021/acs.langmuir.8b00210

Passivation of Plasmonic Colors on Bulk Silver by Atomic Layer Deposition of Aluminum Oxide

2018· article· en· W2791714377 on OpenAlexafffund
Jean‐Michel Guay, Graham Killaire, Peter G. Gordon, Seán T. Barry, Pierre Berini, Arnaud Weck

Bibliographic record

VenueLangmuir · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPassivationMaterials scienceAtomic layer depositionSilver nanoparticlePlasmonTarnishDeposition (geology)Layer (electronics)AluminiumSilver oxideNanoparticleOxideThin filmNanotechnologyConformal coatingChemical vapor depositionCorrosionOptoelectronicsChemical engineeringMetallurgyCoating

Abstract

fetched live from OpenAlex

We report the passivation of angle-independent plasmonic colors on bulk silver by atomic layer deposition (ALD) of thin films of aluminum oxide. The colors are rendered by silver nanoparticles produced by laser ablation and redeposition on silver. We then apply a two-step approach to aluminum oxide conformal film formation via ALD. In the first step, a low-density film is deposited at low temperature to preserve and pin the silver nanoparticles. In the second step, a second denser film is deposited at a higher temperature to provide tarnish protection. This approach successfully protects the silver and plasmonic colors against tarnishing, humidity, and temperature, as demonstrated by aggressive exposure trials. The processing time associated with deposition of the conformal passivation layers meets industry requirements, and the approach is compatible with mass manufacturing.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations22
Published2018
Admission routes2
Has abstractyes

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