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Record W2316927382 · doi:10.1021/ef500712r

Halogenation of Oilsands Bitumen, Maltenes, and Asphaltenes

2014· article· en· W2316927382 on OpenAlexaff
Glaucia H. C. Prado, Arno de Klerk

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsphalteneHalogenationChemistryOrganic chemistryResidue (chemistry)SolventAromaticityChemical engineeringMolecule

Abstract

fetched live from OpenAlex

Bromination of oilsands-derived materials was investigated as part of a search to find conversion pathways other than traditional hydroprocessing and thermal processing for the upgrading of asphaltenes. The working hypothesis was that the insertion of a bulky halogen substituent on an aromatic carbon of a multinuclear aromatic might sterically disrupt π–π ring stacking. Mild bromination (1–3 wt % Br incorporation) caused observable changes in the physical appearance of bitumen, maltenes, and asphaltenes. The hardness was increased and the asphaltenes gained solvent resistance, suggesting potential application as pretreatment for road paving asphalt. UV–vis spectrometry indicated that metalloporphyrin structures were disrupted, suggesting possible demetalation. There was also an increased association of oxygenate-rich asphaltenes with iron pyrite particulates already present in this fraction. Bromination was deleterious in its effect on bitumen and the maltenes, resulting in a decrease in straight run vacuum gas oil yield and increase in microcarbon residue. Conversely, bromination of asphaltenes resulted in an increase in straight run vacuum gas oil yield from 2.0 ± 0.3 wt % to 7.5 ± 1.8 wt % without affecting the microcarbon residue. It was not possible to unequivocally attribute the observed changes in the asphaltenes to the disruption of π–π ring stacking.

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 score0.116
Threshold uncertainty score0.370

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 head0.214
Teacher spread0.207 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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