Oxidation of aqueous organic pollutants using a stable copper nanoparticle suspension
Bibliographic record
Abstract
Abstract Many applications of copper nanoparticles (Cu‐NPs) have been suggested in recent years, although the potential for use of Cu‐NPs in water treatment processes has received relatively little attention. This work highlights the preparation, characterization, and application of polyethylenimine capped copper nanoparticles for use in oxidative degradation of organic pollutants in aqueous solutions; atrazine was selected as a representative pollutant. A stable aqueous Cu‐NP suspension was prepared, with polyethylenimine (PEI) as capping agent, under ambient conditions. The Cu:PEI ratio during Cu‐NP synthesis has a significant influence on nanoparticle properties as well as on the degradation of atrazine. The synthesized Cu‐NPs, which comprised a mixture of Cu0 and Cu2O, induced rapid atrazine degradation (> 99 % in 1 h) and significantly superior performance over commercial nano‐copper oxide powder. Mechanistic insight into the atrazine degradation, via electron spin resonance (ESR) measurements, demonstrated (i) that significant hydroxyl radicals were generated only in the presence of Cu‐NPs, (ii) longevity of radical generation, and (iii) regeneration of hydroxide radicals. The efficiency of the Cu‐NPs applied to oxidative degradation was further demonstrated on eight other representative organic water pollutants.
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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".