Treatment of Dissolved Perchlorate, Nitrate, and Sulfate Using Zero-Valent Iron and Organic Carbon
Bibliographic record
Abstract
Waters containing ClO and dissolved NO, derived from detonated explosives and solid propellants, often also contain elevated concentrations of other dissolved constituents, including SO. Four column experiments, containing mixtures of silica sand, zero-valent Fe (ZVI) and organic C (OC) were conducted to evaluate the potential for simultaneous removal of NO, SO and ClO. Initially, the flow rate was maintained at 0.5 pore volumes (PV) d and then decreased to 0.1 PV d after 100 PV of flow. Nitrate concentrations decreased from 10.8 mg L (NO-N) to trace levels through NO reduction to NH using ZVI alone and through denitrification using OC. Observations from the mixture of ZVI and OC suggest a combination of NO reduction and denitrification. Up to 71% of input SO (24.5 ± 3.5 mg L) was removed in the column containing OC, and >99.7% of the input ClO (857 ± 63 μg L) was removed by the OC- and (ZVI + OC)-containing columns as the flow rate was maintained at 0.1 PV d. Nitrate and ClO removal followed first-order and zero-order rates, respectively. Nitrate >2 mg L (NO-N) inhibited ClO removal in the OC-containing column but not in the (ZVI + OC)-containing column. Sulfate did not inhibit ClO degradation within any of the columns.
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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) | 0.001 | 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 | 0.001 | 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".