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Record W4402580159 · doi:10.1039/d4ra05567e

Studies on the synergistic effect between corn straw and Canadian oil sands bitumen during the co-pyrolysis process

2024· article· en· W4402580159 on OpenAlexaboutno aff
Bing Wang, Cuiyu Zhao, Congxiu Guo

Bibliographic record

VenueRSC Advances · 2024
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPyrolysisStrawOil sandsAsphaltProcess (computing)Pulp and paper industryPyrolysis oilChemistryWaste managementEnvironmental scienceMaterials scienceOrganic chemistryEngineeringComposite materialComputer science

Abstract

fetched live from OpenAlex

To investigate the potential synergistic effect, the co-pyrolysis of corn straw (CS) and Canadian oil sands bitumen (CA-OB) was carried out in this work. Thermogravimetric and differential thermogravimetric curves of CA-OB, CS and their blends were recorded using a thermogravimetric analyser. The main co-pyrolysis regions of the CS/CA-OB blends partially overlapped with the individual pyrolysis curves of CS and CA-OB, and the apparent weight loss was detected between 250 °C and 500 °C. The comparison of the experimental curves with the calculated data indicated that the synergistic effect was present in the main reaction region of co-pyrolysis and was enhanced with increasing CS content. The effects of the interactions between CA-OB and CS on the distributions and yields of the pyrolyzed products were studied in a high-pressure autoclave. It can be concluded that the co-pyrolysis process promoted an increase in the coke yield, while the oil and gas yields decreased. The proportion of aromatics in the pyrolyzed oil products increased as the increasing CS content suppressed the decomposition and dehydrogenation-condensation reactions. In addition, the gasification activity of co-pyrolysis cokes was enhanced.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.998
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.289
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2024
Admission routes1
Has abstractyes

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