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Record W1995082365 · doi:10.1063/1.4896135

Field-assisted patterned dissolution of silver nanoparticles in phosphate glass

2014· article· en· W1995082365 on OpenAlexafffund
Alexander Andreyuk, Jacques Albert

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaAalto-Yliopisto
KeywordsMaterials sciencePhosphate glassDissolutionDopingAnalytical Chemistry (journal)Absorption (acoustics)Silver nanoparticlePrecipitationOptoelectronicsNanoparticleChemical engineeringNanotechnologyComposite materialChemistry

Abstract

fetched live from OpenAlex

Phosphate glass samples doped with silver ions through a Na+-Ag+ ion-exchange process were treated in a hydrogen atmosphere at temperatures near 430 °C for durations ranging from 4 to 5 h. Such treatment causes metallic silver precipitation at the surface as well as nanoclustering of silver atoms under the surface under conditions very similar to those used for silicate glasses. The presence of silver clusters resulted in a characteristic coloring of the glass and was verified by the observation of a plasmon resonance peak near 410–420 nm in the absorption spectra. Applying a DC voltage between 1.4 and 2 kV at temperatures between 120 and 130 °C led to dissolution of the clusters in the area under the positive electrode, thereby bleaching the glass color. The use of a patterned doped-silicon electrode further led to the formation of a 300 nm thick surface relief on the glass surface and of a volume complex permittivity grating extending at least 4 μm under the surface. Such volume complex refractive index gratings may find applications in passive or active (laser) photonic devices in rare-earth doped phosphate glasses, where conventional bulk grating formation techniques have limited applicability.

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)0.000
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 score0.064
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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