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Record W2092851980 · doi:10.1021/ef400533t

Adsorption Kinetics and Thermodynamics of Vanadyl Etioporphyrin on Asphaltene in Pentane

2013· article· en· W2092851980 on OpenAlexaboutno aff
Feifei Chen, Qingjing Liu, Zhiming Xu, Xuewen Sun, Quan Shi, Suoqi Zhao

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionChemistryAsphaltenePentaneFreundlich equationKineticsSolventChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

The interaction between vanadyl porphyrins and asphaltene in alkane solvents is important to increase the removal rate of metals during the solvent deasphalting process. Thus, the adsorption kinetics and thermodynamics of vanadyl etioporphyrins on a Canadian oil sands bitumen vacuum tower bottom (VTB) asphaltene in n -pentane were investigated. After adsorption, asphaltene was analyzed via transmission electron microscopy (TEM), Brunauer–Emmett–Teller (BET), and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). A certain amount of vanadyl porphyrins was adsorbed by the VTB asphaltene. This adsorption process was affected by the asphaltene dosage (0.01 and 0.02 g), the concentration of the n -pentane solution containing vanadyl porphyrins (10 and 15 μg/mL), and the temperature (288, 293, and 298 K). The adsorption rate was initially distinctly high. However, this rate became much slower after around 300 min, until equilibrium was reached after 1800 min. A comparison of four kinetic models of the overall adsorption rate showed that the adsorption process can be well-described by a pseudo-first-order equation. Furthermore, the adsorption equilibrium fit the Freundlich isotherm. The Δ G ° and Δ H ° values of the adsorption process between vanadyl porphyrins and asphaltenes had been regressed at different temperatures. The absolute value of Δ G ° was less than 20 kJ/mol, whereas that of Δ H ° was greater than 40 kJ/mol.

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 score0.042
Threshold uncertainty score0.532

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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

Citations22
Published2013
Admission routes1
Has abstractyes

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