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Record W2328311699 · doi:10.1021/jp108538m

The Effect of Deposition Rate on the Morphology of Fe Nanoparticles on Highly Oriented Pyrolytic Graphite, As Studied by X-ray Photoelectron Spectroscopy and Atomic Force Microscopy

2011· article· en· W2328311699 on OpenAlexaff
Long Chen, A. Yelon, E. Sacher

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2011
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsX-ray photoelectron spectroscopyPyrolytic carbonHighly oriented pyrolytic graphiteMaterials scienceAnalytical Chemistry (journal)Deposition (geology)Atomic layer depositionSubstrate (aquarium)NanoparticleLayer (electronics)NanotechnologyChemical engineeringScanning tunneling microscopeChemistry

Abstract

fetched live from OpenAlex

The effect of deposition rate on the morphology of Fe nanoparticles (NPs) on highly oriented pyrolytic graphite (HOPG) surfaces has been studied by atomic force microscopy (AFM) and in situ X-ray photoelectron spectroscopy (XPS). AFM provided the NP dimensions and the extent of surface coverage, while XPS (both core and valence levels) indicated the interaction of Fe NPs with the HOPG substrate and showed the evolutions of binding energies, full widths at half-maxima, and component peak intensity ratios, all as a function of deposition rate. The results indicate that Fe NPs react with both substrate and residual gases in the high vacuum of the instrument, forming carbide and oxide surface contaminant layers around the NPs. The NP dimensions are essentially independent of deposition rate and of the amount deposited, but the NP surface coverage is inversely related to the deposition rate, with a higher rate resulting in a lower surface coverage. Combining the NP surface coverage and the XPS peak intensity ratios, we find a relationship between deposition rate and surface contaminant layer thickness.

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)0.001
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 score0.006
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.247
Teacher spread0.242 · 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
Published2011
Admission routes1
Has abstractyes

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