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Record W2002203037 · doi:10.1103/physrevb.70.201406

Growth dynamics of pulsed laser deposited Pt nanoparticles on highly oriented pyrolitic graphite substrates

2004· article· en· W2002203037 on OpenAlexaff
Richard Dolbec, Éric Irissou, Mohamed Chaker, Daniel Guay, Federico Rosei, My Alı El Khakani

Bibliographic record

VenuePhysical Review B · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPulsed laser depositionNanoparticleMaterials scienceKinetic energyEnergy (signal processing)NanotechnologyScanning tunneling microscopeSubstrate (aquarium)GraphiteCondensed matter physicsAnalytical Chemistry (journal)PhysicsThin filmChemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Platinum nanoparticles were grown by pulsed laser deposition (PLD) on highly oriented pyrolitic graphite substrates and characterized by scanning tunneling microscopy. Unexpectedly, as the nominal Pt thickness $(t)$ is increased from $0.1\phantom{\rule{0.3em}{0ex}}\text{to}\phantom{\rule{0.3em}{0ex}}20\phantom{\rule{0.3em}{0ex}}\mathrm{nm}$, the mean diameter $({d}_{m})$ of the Pt nanoparticles follows the power law ${d}_{m}\ensuremath{\propto}{t}^{1∕Z}$ with a dynamic exponent $Z=4.7\ifmmode\pm\else\textpm\fi{}10\phantom{\rule{0.2em}{0ex}}%$. This growth law is found to be valid for incident kinetic energy $({K}_{E})$ of the ablated species involved in the growth process ranging from $4\phantom{\rule{0.3em}{0ex}}\text{to}\phantom{\rule{0.3em}{0ex}}130\phantom{\rule{0.3em}{0ex}}\mathrm{eV}∕\mathrm{at.}$ We also show that the shape of isolated Pt nanoparticles can be greatly influenced by ${K}_{E}$. Our results point out that PLD Pt nanoparticles nucleate and grow on the substrate rather than being formed in the ablation plume.

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 score0.002
Threshold uncertainty score0.004

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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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

Citations76
Published2004
Admission routes1
Has abstractyes

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Same venuePhysical Review BSame topicnanoparticles nucleation surface interactionsFrench-language works237,207