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 (nCu 0 ), coupled with borohydride (hydrogen donor), has shown promising results. So far, the research on nCu 0 as a remedial treatment has focused mainly on contaminant removal efficiencies and degradation mechanisms. Our study has examined the effects of Cu 0 /Cu n+ ratio, surface poisoning (presence of chloride, sulfides, humic acid (HA)), and regeneration of Cu 0 sites on catalytic dechlorination of aqueous-phase 1,2-dichloroethane (1,2-DCA) via nCu 0 -borohydride. Scanning electron microscopy confirmed the nano size and quasi-spherical shape of nCu 0 particles. X-ray diffraction confirmed the presence of Cu 0 and Cu 2 O and x-ray photoelectron spectroscopy also provided the Cu 0 /Cu n+ ratios. Reactivity experiments showed that nCu 0 was incapable of utilizing H 2 from borohydride left over during nCu 0 synthesis and, hence, additional borohydride was essential for 1,2-DCA dechlorination. Washing the nCu 0 particles improved their Cu 0 /Cu n+ ratio (1.27) and 92% 1,2-DCA was removed in 7 h with k obs = 0.345 h −1 as compared to only 44% by unwashed nCu 0 (0.158 h −1 ) with Cu 0 /Cu n+ 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 Cu 0 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 nCu 0 catalytic activity by altering its physico-chemical properties. Thus, these factors should be considered to develop an efficient remedial design for practical applications of nCu 0 -borohydride.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".