Degradation of hexahydro‐1,3,5‐trinitro‐1,3, 5‐triazine (RDX) by anaerobic mesophilic granular sludge from a UASB reactor
Bibliographic record
Abstract
Abstract BACKGROUND: This study investigated the performance of anaerobic mesophilic granular sludge for the degradation of hexahydro‐1,3,5‐trinitro‐1,3,5‐triazine (RDX). Batch tests were conducted to investigate the effects of different supplements on the RDX degradation ability of anaerobic granular sludge, as well as the contributions of both physicochemical and biological processes involved in RDX removal from aqueous solution. RESULTS: Anaerobic granular sludge exhibited good performance in treating RDX as the sole substrate. Biodegradation was the main mechanism responsible for RDX removal. Ammonium had no significant promoting effect on the degradation process. The presence of glucose was found to enhance the degradation of RDX by anaerobic granular sludge, while the addition of sulfate and nitrate had adverse effects on the reductive transformation of RDX. CONCLUSIONS: Anaerobic granular sludge is capable of removing RDX from aqueous solution with high efficiency. This study showed good prospects for high‐rate anaerobic processes in the treatment of munition wastewater. The results can be used for the design and optimization of high rate anaerobic systems for the elimination of RDX. Copyright © 2010 Society of Chemical Industry
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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) | 0.000 | 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".