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Record W2768535049 · doi:10.1039/c7cp07024a

Visible light driven plasmonic photochemistry on nano-textured silver

2017· article· en· W2768535049 on OpenAlexafffund
Jaspreet Walia, Jean‐Michel Guay, Oleksiy Krupin, Fabio Variola, Pierre Berini, Arnaud Weck

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceCanada Research Chairs
KeywordsPlasmonVisible spectrumNano-Silver NanoPhotochemistryMaterials scienceNanotechnologyOptoelectronicsOpticsChemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

Plasmon assisted generation of silver sulfate from dodecanethiol is demonstrated on a nano-textured silver substrate with a strong surface plasmon resonance in the visible range. The observed photo-physical processes are attributed to hot charge carriers that are generated from the excitation of surface plasmon resonances using 532 nm laser light. Excited charge carriers are responsible for cleaving the alkane chain, and for generating reactive oxygen species which rapidly photooxidize the exposed sulfur atoms. The ability to drive photochemical reactions with photon energies in the visible range rather than in the UV, on nano-textured silver surfaces, will enable researchers to study photochemical transformations for a wide variety of applications. The strong optical absorbance across the visible range, combined with the fact that the substrates can be fabricated over large areas, naturally makes them candidates for solar driven photochemical applications, and for large scale plasmonic reactors.

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 score0.003

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 head0.014
GPT teacher head0.253
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; 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

Citations11
Published2017
Admission routes2
Has abstractyes

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