Preparation and Regeneration of Nickel-Iron for Reduction of Organic Contaminants
Bibliographic record
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 NH2SO4, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".