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Record W2931814280 · doi:10.3390/nano9040504

Novel Synthesis of Choline-Based Amino Acid Ionic Liquids and Their Applications for Separating Asphalt from Carbonate Rocks

2019· article· en· W2931814280 on OpenAlexaff
Zisheng Zhang, Ning Kang, Jingjing Zhou, Xingang Li, Lin He, Hong Sui

Bibliographic record

VenueNanomaterials · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsUniversity of Ottawa
FundersNational Natural Science Foundation of China
KeywordsIonic liquidChemical engineeringSolventMaterials scienceCarbonateAsphaltIonic bondingSurface energyPhase (matter)ImidazoleOrganic chemistryChemistryInorganic chemistryIonComposite material

Abstract

fetched live from OpenAlex

In this study, a series of choline-based amino acid ionic liquids have been synthesized in an economic method and are used to assist solvents with extracting asphalt from carbonate rocks. All of the ionic liquids perform well in extracting asphalt, especially choline histidine, by which the single-step recovery of asphalt is up to 91%. Furthermore, oil product with higher quality (fewer solids entrained) is also obtained. Molecular dynamics simulation and thermodynamic equilibrium method are applied to investigate the role of amino acid ionic liquids via interaction energy calculation and surface free energy calculations. The simulation results suggest that the ionic liquid phase is beneficial for the transfer of oil fraction from the carbonate surface to the organic solvent phase. Moreover, the results of simulated calculation show that the introduction of a functional group with conjugated structures into ionic liquid, such as an imidazole ring and a benzene ring, is beneficial for enhancing oil recovery, which are in accordance with the results of experimental tests.

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 score0.024
Threshold uncertainty score0.571

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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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

Citations46
Published2019
Admission routes1
Has abstractyes

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