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Record W1965878897 · doi:10.1039/c5cp00924c

Gold nanoparticle array formation on dimpled Ta templates using pulsed laser-induced thin film dewetting

2015· article· en· W1965878897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDewettingMaterials scienceTemplateNanoparticlePulsed laserNanotechnologyThin filmColloidal goldPulsed laser depositionLaserChemical engineeringOptoelectronicsOptics

Abstract

fetched live from OpenAlex

Here we show that pulsed laser-induced dewetting (PLiD) of a thin Au metallic film on a nano-scale ordered dimpled tantalum (DT) surface results in the formation of a high quality Au nanoparticle (NP) array. In contrast to thermal dewetting, PLiD does not result in deformation of the substrate, even when the Au film is heated to above its melting point. PLiD causes local heating of only the metal film and thus thermal oxidation of the Ta substrate can be avoided, also because of the high vacuum (low pO2) environment employed. Therefore, this technique can potentially be used to fabricate NP arrays composed of high melting point metals, such as Pt, not previously possible using conventional thermal annealing methods. We also show that the Au NPs formed by PLiD are more spherical in shape than those formed by thermal dewetting, likely demonstrating a different dewetting mechanism in the two cases. As the metallic NPs formed on DT templates are electrochemically addressable, a longer-term objective of this work is to determine the effect of NP size and shape (formed by laser vs. thermal dewetting) on their electrocatalytic properties.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.433
Threshold uncertainty score1.000

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 head0.028
GPT teacher head0.239
Teacher spread0.211 · 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