A ReaxFF molecular dynamics study on the mechanism and the typical pyrolysis gases in the pyrolysis process of Longkou oil shale kerogen
Bibliographic record
Abstract
The combination of Reactive molecular dynamics (RMD) simulations and a reactive force field (ReaxFF) was employed to investigate the chemical mechanisms and product distribution in the process of oil shale kerogen pyrolysis. A large-scale reactive system based on five structural models used in the simulation was constructed according to the analysis results of a series of detection about kerogen extracted from Longkou oil shale to investigate the reaction processes of oil shale. The characteristics observed in the simulation agree well with the known characteristics of the oil shale structure and reactions. The simulation results proved the importance of temperature exert on the product distributions, intermolecular interactions and elementary reactions in the process of pyrolysis. A conclusion was made about the suitable temperature range for producing useful organic gases and the highest yield of shale oil. The detailed chemical reaction process of Longkou oil shale pyrolysis was described in this work as well. This work is an intensive study on the pyrolysis mechanism and the formative path of the typical products especially shale gases at different temperatures at the atomic level and will be of great significance for the development and utilisation of oil shale mineral resources.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".