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Quantification of the Free Radical Content of Oilsands Bitumen Fractions

2019· article· en· W2961603695 on OpenAlexafffund
Joy H. Tannous, Arno de Klerk

Bibliographic record

VenueEnergy & Fuels · 2019
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesChina National Offshore Oil Corporation
KeywordsAsphalteneRadicalChemistryElectron paramagnetic resonanceAnalytical Chemistry (journal)SpectroscopyFree fractionLight crude oilThermal decompositionAtmospheric temperature rangeOrganic chemistryThermodynamicsNuclear magnetic resonance

Abstract

fetched live from OpenAlex

Electron spin resonance (ESR) spectroscopy was employed to perform quantitative analysis of the free radical content of oilsands bitumen, asphaltenes, deasphalted oil, vacuum residue, and vacuum gas oil fractions, as well as thermally converted product fractions. Calibration standards for ESR were compared, and 2,2-diphenyl-1-picrylhydrazyl was selected. The heaviest fractions, including asphaltenes, had free radical concentrations in the range 1017–1018 spins/g, whereas lighter fractions such as the lighter gas oil fractions had free radical concentrations in the range 1016–1017 spins/g. It was found that the bulk liquid properties affected the measured free radical concentration even after compensating for effects that could affect the spectroscopy. These differences were not analytical artifacts and could be explained with reference to the literature in terms of the “equilibrium” composition resulting from dimerization and decomposition of free radical pairs. Reported free radical concentrations must consequently be interpreted by considering the nature of the bulk liquid that was analyzed. Practically, the results have implications for thermal conversion of bitumen. It appears that the free radical concentration and availability of reactive free radicals can be independently manipulated through temperature and the bulk liquid properties.

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 score0.012
Threshold uncertainty score0.548

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)0.0010.000

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 head0.020
GPT teacher head0.237
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

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

Citations47
Published2019
Admission routes2
Has abstractyes

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