Supercritical Water Oxidation vs Supercritical Water Gasification: Which Process Is Better for Explosive Wastewater Treatment?
Bibliographic record
Abstract
2,4,6-Trinitrotoluene (TNT), as a representative component of explosive wastewater, is treated in supercritical water gasification (SCWG) and supercritical water oxidation (SCWO) using molecular dynamic simulations based on ReaxFF reactive force field as well as density functional theory (DFT). The detailed reaction processes, important intermediates and products distribution, and kinetic behaviors of SCWG and SCWO systems have been analyzed at the atomistic level. For the SCWG system, TNT is activated by water cluster or H radical and the N atom is mainly converted into NH 3 more than N 2 through two significant intermediates NOH and C–N fragment. In addition to water cluster and H radical, the TNT is activated by O 2 in the SCWO system. Besides, the N atom is transferred into N 2 more than other N-containing products after 750 ps simulation. Combined with the calculated cracking energy of the bonds in TNT, SCWG can accelerate its degradation and is easier for C–N bond breaking or changing through other reactions because of its low cracking energy (69.6 kcal/mol in thermal decomposition and 59.0 kcal/mol in SCWG). In addition, a large amount of H 2 molecules is produced in SCWG, which is a meaningful way of transforming waste to assets. On TNT degradation, SCWO with inadequate O 2 that can be treated as partial oxidation reaction (SCWPO) can combine the advantages of SCWG and SCWO (with enough O 2 ) to convert TNT into CO 2, H 2 O, as well as H 2 and NH 3 with high economic value. Finally, a kinetic description is performed whose activation energies (17.6 and 18.4 kcal/mol) are theoretically consistent with experimental measurements.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".