Dataset of methane pyrolysis products in a batch reactor as a function of time at high temperatures and pressures
Bibliographic record
Abstract
Methane pyrolysis is a process used to generate hydrogen gas and carbon black without the creation of carbon dioxide. Methane pyrolysis in a constant volume batch reactor was investigated at temperatures of 892, 1093, and 1292 K with reaction times of 15, 30, 60, 180, and 300 s at an initial pressure of 399 kPa. A quartz vessel (32 mL) was placed inside an oven where it was heated to high temperatures. At the beginning of the process, the quartz vessel was vacuumed, then flushed with nitrogen before being vacuumed again prior to every experiment. Pressurized methane was then injected into the vessel for an allocated reaction time and collected in a sample bag post reaction for analysis. The molar concentration of the product gas was analyzed using gas chromatography. Hydrogen molar concentration increased as temperature and reaction time increased. For experiments completed at 892 K the hydrogen molar concentration varied from 10.0 ± 5.9% with a 15 s reaction time to 26.5 ± 0.8% for a 300 s reaction time. For experiments completed at 1093 K the hydrogen molar concentration varied from 21.8 ± 3.7% for a 15 s reaction time to 53.0 ± 2.9% for a 300 s reaction time. For experiments completed at 1292 K the hydrogen molar concentration varied from 31.5 ± 1.7% for a 15 s reaction time to 53.0 ± 2.4% for a 300 s reaction time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".