Structural model of Longkou oil shale kerogen and the evolution process under steam pyrolysis based on ReaxFF molecular dynamics simulation
Bibliographic record
Abstract
The product distribution and reaction mechanism of steam pyrolysis of Longkou oil shale kerogen was researched by molecular dynamics simulation. Molecule structural model used in the simulation was constructed according to the analysis results of a series of detection about kerogen extracted from Longkou oil shale. Reactive force field molecular dynamics (ReaxFF MD) was used to simulate both steam pyrolysis and direct pyrolysis process of the kerogen at the temperature of 1600, 2000, 2400 and 2800 K. The results show that temperature is a critical factor affecting product distribution in steam pyrolysis, and 2000 K is a proper set temperature for studying steam pyrolysis via molecular simulation method. Besides that, adding the H2O molecules during steam pyrolysis can form complexes with heterogeneous atoms, thus destroying the intermolecular interactions in kerogen. Moreover, as the hydrogen radicals come from H2O molecules can inhibit cross-linking reactions between small fractions, it can reduce the average molecular weight of organic molecules product. These conclusions could be helpful for rational use of oil shale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".