Reduction of<i>N</i>-Nitrosodimethylamine with Granular Iron and Nickel-Enhanced Iron. 1. Pathways and Kinetics
Bibliographic record
Abstract
Laboratory batch and column tests were conducted to examine the reduction pathways and kinetics of N -nitrosodimethylamine (NDMA) by iron (Fe) and nickel-enhanced iron (Ni/Fe). A decrease in NDMA concentration and increases in dimethylamine (DMA) and ammonium were observed in both Fe and Ni/Fe columns. In the Fe column, the transformation process of NDMA appeared to follow pseudo-first-order kinetics with respect to NDMA, with an average half-life of 13±2 h. A small amount of nickel (0.25%) plated onto the iron greatly enhanced NDMA transformation rates. At early time the NDMA half-life in the Ni/Fe column was 2 min but as time progressed the half-life increased to 4 min, and departures from first-order kinetics were observed. The mass balances of carbon in DMA and nitrogen in DMA and ammonium improved over time and reached 100% and 90%, respectively, after NDMA had passed through the column for more than 50 pore volumes (PV). No 1,1-dimethylhydrazine, nitrous oxide, or methane were detected. Based on the electrochemical properties of NDMA, the transformation mechanism of NDMA with Fe and Ni/Fe is postulated to be catalytic hydrogenation, resulting in N−N bond breakdown to form DMA and ammonium as final products. Nickel, being a much stronger catalyst than Fe for catalytic hydrogenation, resulted in a much faster reduction rate of NDMA. Of several methods tested, flushing the Ni/Fe column with 0.01 N sulfuric acid proved to be the most effective in restoring the Ni/Fe activity. The rapid transformation rate on Ni/Fe and the formation of nontoxic products indicate that this material may be applicable for treating NDMA contaminated water, both in-situ and above ground.
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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".