MétaCan
Menu
Back to cohort
Record W2333151384 · doi:10.1021/ef501694s

Hydrocarbon Addition Reactions during Low-Temperature Autoxidation of Oilsands Bitumen

2014· article· en· W2333151384 on OpenAlexafffund
Muhammad N. Siddiquee, Arno de Klerk

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutoxidationChemistryHydrocarbonAlkylbenzenesSelectivityDisproportionationRadicalOrganic chemistryAsphaltAlkylPhotochemistryHydrogenCatalysisMaterials science

Abstract

fetched live from OpenAlex

Low-temperature oxidation of bitumen with air in the temperature range of 130–160 °C was investigated. Of particular interest were the addition reactions taking place during oxidation, which contributed to the observed increase in viscosity of oxidized bitumen. During the autoxidation of bitumen, the relative aliphatic to aromatic loss-ratio of hydrogen increased from 18:1 to 30:1 when the temperature was increased from 140 °C to 150 °C and then remained almost the same at 160 °C. It coincided with a bitumen oxidation selectivity change reported in the literature. The hydrocarbon class responsible for most addition reactions during bitumen oxidation is the naphthenic-aromatic class. A model compound oxidation study at 130 °C found no addition products during paraffin oxidation, low addition product selectivity for naphthenic and alkylaromatic compounds, and no measurable oxidation of aromatics without alkyl groups. It was proposed that the dominant pathway for addition reactions of hydrocarbons is hydrogen disproportionation of free radicals to produce olefins. Free-radical addition to olefins through the formation of C–C bonds explained all of the oxidation selectivity observations from the model compound studies, as well as the addition products identified from their mass spectra. It could also be applied to explain the bitumen oxidation results in these and other studies.

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 score0.027
Threshold uncertainty score0.497

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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

Citations37
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicPetroleum Processing and AnalysisFrench-language works237,207