Catalytic dechlorination of 1,2-DCA in nano Cu0-borohydride system: effects of Cu0/Cun+ ratio, surface poisoning, and regeneration of Cu0 sites
Bibliographic record
Abstract
Abstract Aqueous-phase catalyzed reduction of organic contaminants via zerovalent copper nanoparticles (nCu0), coupled with borohydride (hydrogen donor), has shown promising results. So far, the research on nCu0 as a remedial treatment has focused mainly on contaminant removal efficiencies and degradation mechanisms. Our study has examined the effects of Cu0/Cun+ ratio, surface poisoning (presence of chloride, sulfides, humic acid (HA)), and regeneration of Cu0 sites on catalytic dechlorination of aqueous-phase 1,2-dichloroethane (1,2-DCA) via nCu0-borohydride. Scanning electron microscopy confirmed the nano size and quasi-spherical shape of nCu0 particles. X-ray diffraction confirmed the presence of Cu0 and Cu2O and x-ray photoelectron spectroscopy also provided the Cu0/Cun+ ratios. Reactivity experiments showed that nCu0 was incapable of utilizing H2 from borohydride left over during nCu0 synthesis and, hence, additional borohydride was essential for 1,2-DCA dechlorination. Washing the nCu0 particles improved their Cu0/Cun+ ratio (1.27) and 92% 1,2-DCA was removed in 7 h with kobs = 0.345 h−1 as compared to only 44% by unwashed nCu0 (0.158 h−1) with Cu0/Cun+ ratio of 0.59, in the presence of borohydride. The presence of chloride (1000–2000 mg L−1), sulfides (0.4–4 mg L−1), and HA (10–30 mg L−1) suppressed 1,2-DCA dechlorination; which was improved by additional borohydride probably via regeneration of Cu0 sites. Coating the particles decreased their catalytic dechlorination efficiency. 85–90% of the removed 1,2-DCA was recovered as chloride. Chloroethane and ethane were main dechlorination products indicating hydrogenolysis as the major pathway. Our results imply that synthesis parameters and groundwater solutes control nCu0 catalytic activity by altering its physico-chemical properties. Thus, these factors should be considered to develop an efficient remedial design for practical applications of nCu0-borohydride.
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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) | 0.001 | 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".