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Record W2316092416 · doi:10.1021/jp4120018

Interactions between Carbon Nanoparticles in a Droplet of Organic Solvent

2014· article· en· W2316092416 on OpenAlexafffund
Maxim Paliy, Styliani Consta, Jinrong Yang

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoparticleFullereneCarbon nanotubeDispersion (optics)Chemical physicsSolventChemistryIntermolecular forceCounterionElectrosprayCarbon fibersNanotechnologyCarbon NanoparticlesMolecular dynamicsElectrostaticsChemical engineeringMoleculeMaterials scienceIonComputational chemistryOrganic chemistryPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

We report the first detailed molecular dynamics study of two carbon nanoparticles (nanotubes or fullerenes) embedded in a droplet of chloroform. The carbon nanoparticles are negatively charged and are accompanied by the positive counterions of sodium. On the one hand, this setup is inspired by the carbon nanoparticle salts—“nanotubides” and “fullerides”—proposed recently as a convenient way to address the difficult problem of the dispersion of carbon nanoparticulates in the organic solvents. On the other hand, the electrospray of carbon nanoparticle suspensions has been shown to be a promising technique for the production of thin nanoparticle coatings. We explore the combination of these two approaches by using molecular simulations. We find that depending on the overall electrostatic charge balance the two nanoparticles can exist in the droplet either in the “salted-out” bound state or in a “solvent-separated” state. The latter opens up the possibility for the efficient dispersion of the nanoparticles in the charged droplet environment. We also find that depending on the charge of the nanoparticles the mother droplet may break evenly or unevenly with respect to the mass but evenly with respect to charge. The understanding of the fragmentation paths and the intermolecular interactions of the nanoparticles in droplets provides insight into the manner that the droplet chemistry can be manipulated so that effective dispersion of nanoparticulates can be achieved.

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 score0.347
Threshold uncertainty score0.200

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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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