Degradation of Pentaerythritol Tetranitrate (PETN) by Granular Iron
Bibliographic record
Abstract
Pentaerythritol tetranitrate (PETN), a nitrate ester, is used primarily as an explosive. It is of environmental concern, posing a threat to aquatic organisms with an estimated EC50 five times greater than that of RDX. This study evaluated the kinetics and products of PETN degradation in the presence of granular iron. PETN transformation in columns containing 100% granular iron and 30% iron mixed with 70% silica sand followed pseudo first-order kinetics, with average half-lives of 0.26 and 1.58 min, respectively. The reduction pathway was proposed to be sequential denitration, in which PETN was stepwise reduced to pentaerythritol with the formation of pentaerythritol trinitrate and pentaerythritol dinitrate as intermediates. The intermediate of pentaerythritol mononitrate was not detected; however, the nearly 100% nitrogen mass recovery supported complete denitration. Nitrite was released in each denitration step and was subsequently reduced to ammonium by iron. Nitrate was not detected during the experiment suggesting that hydrolysis was not involved in PETN degradation. Batch experiments showed that when solid-phase PETN is present, dissolution is the rate-limiting factorfor PETN mass removal. Using 50% methanol as a cosolvent PETN solubility was enhanced and thus the removal efficiency was improved. The results demonstrate excellent potential of using iron to remediate PETN-contaminated water.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".