Preparation and Regeneration of Nickel-Iron for Reduction of Organic Contaminants
Bibliographic record
Abstract
Abstract: Factors affecting nickel-iron (Ni-Fe) performance for TCE reduction were examined in column experiments. The Ni-Fe materials were prepared by plating 0.25 wt% nickel onto acid-washed (clean) and unwashed (oxide-covered) granular iron using both displacement and electroless methods. For a short, initial period (300 PV), acid-washed Ni-Fe degraded TCE faster than unwashed Ni-Fe. As deactivation proceeded, there was no significant difference in TCE reduction rates in acid-washed and unwashed Ni-Fe columns. Both methods plated nickel onto acid-washed iron effectively. Displacement plating, however, could not deposit nickel onto unwashed iron. Particle size was another important factor affecting Ni-Fe performance. The source of iron (e.g., Fisher vs. Connelly), however, had only limited effect. Methods for regenerating Ni-Fe were tested using N-nitrosodimethylamine. A partial recovery of the Ni-Fe reactivity was achieved by (1) flushing the column with water, (2) flushing the column with 0.01 NH 2 SO 4, and (3) stopping the flow to the column periodically. Of the methods tested, flushing with acid proved to be the most effective in restoring the Ni-Fe activity.
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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) | 0.002 | 0.002 |
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".