Reduction of hexavalent chromium mediated by micron- and nano-scale zero-valent metallic particles
Bibliographic record
Abstract
A variety of zero-valent metals (Al(0), Cu(0), Fe(0), Mg(0), Ni(0), Si(0) and Zn(0)), and Cu(0) and Fe(0) nano-sized particles were evaluated for reactivity towards the reduction of Cr(VI) in a surfactant preparation (Tween 20) under a selection of reaction conditions. At circum neutral pHs, a rapid inactivation of the surface was observed for almost all of the tested metals and complete reduction of Cr(VI) was achieved at acidic pH only by using Cu(0), Fe(0), Mg(0) or Zn(0). A considerable increase in reactivity (up to 100-fold) was observed for reductive remediation with nano-sized particles. The use of a solid support as a dispersant and stabilizer circumvented the tendency of freshly prepared nano-particles to either react with surrounding media or agglomerate, resulting in the formation of much larger flocs and appreciable loss in reactivity. The formation of clusters by polymeric structures provided an extra protection of the nano-particle surface with a striking improvement in their reactivity mainly at more alkaline conditions. Therefore the incorporation of an innocuous stabilizer can substantially enhance the stability of nano-particles for environmental transformations.
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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".