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Record W2610696806 · doi:10.1021/acs.jpcc.7b01116

Effect of Model Polycyclic Aromatic Compounds on the Coalescence of Water-in-Oil Emulsion Droplets

2017· article· en· W2610696806 on OpenAlexafffund
Cuiying Jian, Qingxia Liu, Hongbo Zeng, Tian Tang

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersCanadian Centre for Clean Coal/Carbon and Mineral Processing TechnologiesNatural Sciences and Engineering Research Council of Canada
KeywordsCoalescence (physics)TolueneHeptaneAdsorptionEmulsionChemistrySteric effectsChemical physicsMoleculeMolecular dynamicsChemical engineeringOrganic chemistryComputational chemistryPhysics

Abstract

fetched live from OpenAlex

A series of molecular dynamics simulations were performed to investigate the effect of polycyclic aromatic compounds (PACs) on the coalescence of two water droplets in oil (i.e., n -heptane and toluene). Our simulations revealed that, in both solvents, the presence of PACs can significantly hinder the coalescence or even completely prevent it. Detailed structural and kinetic analysis provided insights into the underlying mechanisms for the coalescence inhibition. In n -heptane, regardless of their concentration, the PAC molecules formed an adsorption layer on the water droplets which, if the concentration is sufficiently high, is able to introduce strong steric hindrance and shield the water–water interaction. While the formation of an adsorption layer was also observed in toluene at sufficiently high PAC concentration, the prevention of coalescence at relatively low concentration is mainly driven by the unadsorbed, free-floating PAC molecules in the bulk toluene. The simulation results reported here fully agree with our previous experimental observations and well interpreted the experimental results from the atomic level.

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 score0.038
Threshold uncertainty score0.290

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 science0.0010.000
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 head0.267
Teacher spread0.256 · 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

Citations31
Published2017
Admission routes2
Has abstractyes

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