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Record W2025084627 · doi:10.1021/jp076531s

Strongly Enhanced Interaction between Evaporated Pt Nanoparticles and Functionalized Multiwalled Carbon Nanotubes via Plasma Surface Modifications:  Effects of Physical and Chemical Defects

2008· article· en· W2025084627 on OpenAlexaff
De‐Quan Yang, E. Sacher

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsX-ray photoelectron spectroscopyMaterials sciencePyrolytic carbonChemical engineeringNanoparticleTransmission electron microscopyCarbon nanotubeArgonSurface modificationCarbon fibersOxygenNanotechnologyAnalytical Chemistry (journal)ChemistryComposite materialPyrolysisOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Oxygen and argon plasmas were used to modify multiwalled carbon nanotubes (CNTs) to improve their interfacial interaction with subsequently deposited Pt nanoparticles. In contradistinction to what was found in the case of highly oriented pyrolytic graphite (HOPG), X-ray photoelectron spectroscopy (XPS) confirms the introduction of chemical functionalizations by oxygen plasma treatment; however, as in the case of HOPG, argon plasma treatment produced physical defects. Transmission electron microscopy (TEM) provided visual evidence of the interaction of subsequently evaporated Pt with treated CNTs, showing it to have been enhanced by both plasma treatments. XPS and TEM analyses demonstrate that the enhancement is due to similar interactions of Pt nanoparticles with both types of treated CNTs, although not to the same extent: X-ray photoelectron spectroscopy gives no evidence of chemical bonds formed for either plasma treatment. The morphology of the Pt nanoparticles changes with the deposition rate, which may be influenced by the limited availability of the CNT surface.

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 score0.012
Threshold uncertainty score0.609

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 head0.012
GPT teacher head0.250
Teacher spread0.237 · 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

Citations85
Published2008
Admission routes1
Has abstractyes

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