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Record W3014749259 · doi:10.1021/acsomega.0c00096

Oxidative-Extractive Desulfurization of Model Fuels Using a Pyridinium Ionic Liquid

2020· article· en· W3014749259 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandCompute Canada
KeywordsDibenzothiopheneIonic liquidFlue-gas desulfurizationOrganosulfur compoundsOxidizing agentPyridiniumChemistryExtraction (chemistry)Inorganic chemistrySulfurOrganic chemistryNuclear chemistryCatalysis

Abstract

fetched live from OpenAlex

In this research, N-butyl-pyridinium tetrafluoroborate ([BPy][BF4]) ionic liquid (IL) was synthesized and characterized by 1H-NMR, 13C-NMR, and FT-IR spectroscopy. The synthesized ionic liquid was employed as an extractive agent for the removal of dibenzothiophene (DBT), a typical organosulfur pollutant from the organic medium. The effect of extractive desulfurization on a model fuel with DBT was investigated. The impact of operating parameters, i.e., temperature, extraction time, IL-to-fuel ratio, and fuel to oxidizing agent ratio, on the extraction efficiency was investigated. It was observed that the IL to model fuel ratio and the H2O2 dosage have significant effects on desulfurization efficiency. An 89.49% DBT removal efficiency was obtained at a temperature of 30 °C after 35 min of extraction when the volume ratio of IL:model fuel:H2O2 was 1:1:0.04. A density functional theory (DFT) study was carried out to investigate the interactions between the IL and the sulfur-containing compounds DBT/DBTO2.

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.

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 score0.312
Threshold uncertainty score0.444

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 head0.054
GPT teacher head0.271
Teacher spread0.217 · 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