Degradation of Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) Using Zerovalent Iron Nanoparticles
Bibliographic record
Abstract
Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is a common contaminant of soil and water at military facilities. The present study describes degradation of RDX with zerovalent iron nanoparticles (ZVINs) in water in the presence or absence of a stabilizer additive such as carboxymethyl cellulose (CMC) or poly(acrylic acid) (PAA). The rates of RDX degradation in solution followed this order CMC-ZVINs > PAA-ZVINs > ZVINs with k1 values of 0.816 +/- 0.067, 0.082 +/- 0.002, and 0.019 +/- 0.002 min(-1), respectively. The disappearance of RDX was accompanied by the formation of formaldehyde, nitrogen, nitrite, ammonium, nitrous oxide, and hydrazine by the intermediary formation of methylenedinitramine (MEDINA), MNX (hexahydro-1-nitroso-3,5-dinitro-1,3,5-triazine), DNX (hexahydro-1,3-dinitroso-5-nitro-1,3,5-triazine), TNX (hexahydro-1,3,5-trinitroso-1,3,5-triazine). When either of the reduced RDX products (MNX or TNX) was treated with ZVINs we observed nitrite (from MNX only), NO (from TNX only), N2O, NH4+, NH2NH2 and HCHO. In the case of TNX we observed a new key product that we tentatively identified as 1,3-dinitroso-5-hydro-1,3,5-triazacyclo-hexane. However, we were unable to detect the equivalent denitrohydrogenated product of RDX and MNX degradation. Finally, during MNX degradation we detected a new intermediate identified as N-nitroso-methylenenitramine (ONNHCH2NHNO2), the equivalentof methylenedinitramine formed upon denitration of RDX. Experimental evidence gathered thus far suggested that ZVINs degraded RDX and MNX via initial denitration and sequential reduction to the corresponding nitroso derivatives prior to completed decomposition but degraded TNX exclusively via initial cleavage of the N-NO bond(s).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".