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Mechanistic Understanding of the Effect of Temperature and Salinity on the Water/Toluene Interfacial Tension

2016· article· en· W2544771192 on OpenAlexafffund
Cuiying Jian, Mohammad Reza Poopari, Qingxia Liu, Nestor Zerpa, Hongbo Zeng, Tian Tang

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsNexen (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Centre for Clean Coal/Carbon and Mineral Processing TechnologiesAlberta InnovatesWestern Canada Research Grid
KeywordsTolueneAsphalteneChemistrySurface tensionSalinityTemperature salinity diagramsDrop (telecommunication)ThermodynamicsWork (physics)Chemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

In this work, a series of pendant drop measurements and molecular dynamics (MD) simulations were performed to investigate the effects of temperature and salinity on the interfacial tension (IFT) of water/toluene binary systems. Both experimental measurements and theoretical simulations demonstrated that elevating temperature decreased the IFT, while adding salts resulted in an increment of IFT. Furthermore, it was found that the presence of model asphaltene compound could alleviate the effects of temperature and salinity on the IFTs. That is, in the presence of the model asphaltene compound, the decrement effect of elevating temperature as well as the increment effect of adding salts was reduced. Through detailed analysis of the simulated systems, the underlying mechanisms for the effects of temperature and salinity on the IFTs were clarified for cases with and without the presence of the model asphaltene. The results reported here can help to modulate the IFT values of oil/water interfaces in petroleum processing.

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 score0.001
Threshold uncertainty score0.136

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 head0.218
Teacher spread0.206 · 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

Citations44
Published2016
Admission routes2
Has abstractyes

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