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Record W2313026689 · doi:10.1021/ef400097h

Probing the Effect of Side-Chain Length on the Aggregation of a Model Asphaltene Using Molecular Dynamics Simulations

2013· article· en· W2313026689 on OpenAlexafffund
Cuiying Jian, Tian Tang, Subir Bhattacharjee

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaWestern Canada Research Grid
KeywordsAsphalteneSide chainStackingMolecular dynamicsChemistryChemical physicsKineticsMoleculeComputational chemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

A series of molecular dynamics simulations were performed to investigate the effect of aliphatic side-chain length on the aggregation behavior of a model asphaltene in water. We found that the extent of aggregation has a nonmonotonic relationship with the side-chain length. Asphaltene molecules with very short or very long side chains can form dense aggregates, whereas those with intermediate chain lengths cannot. Through analysis of the kinetics and driving forces of the aggregation, the role of the side chains in affecting the aggregation behavior was elucidated. Long side chains hinder the formation of parallel stacking structures of the polyaromatic cores while also favoring aggregation through hydrophobic association. The simulation results reported here can be used to propose appropriate means to reduce the extent of aggregation of asphaltene in the presence of water.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.234
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations134
Published2013
Admission routes2
Has abstractyes

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