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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 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 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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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