Transformation of Engineered Copper Oxide Nanoparticles in Surface Waters
Bibliographic record
Abstract
Copper oxide nanoparticles (CuO-NPs) are widely used for their catalytic properties, conductive capacity, and innovations in the fields of superconductors, alloys, and solar energy sensors. To better understand the impact of water chemistry on the stability of CuO nanoparticles, a series of measurements were carried out on nanoparticles suspended in pure water, natural water, and water enriched with natural organic matter fulvic acid (FA). ICP-MS characterization in single-particle mode (SP-ICP-MS) was performed to determine the stability or transformation of nanoparticles in contrasting water conditions. We first observed that particle sedimentation was very fast in pure Milli-Q water. The addition of FA favored the dissolution of CuO-NPs with an increase in the dissolved copper concentration, for both Milli-Q water and natural water. The presence of FA also reduced the size of CuO-NPs (i.e., less aggregation) measured in natural water. By comparing signals of single particles, FA decreased nanoparticle numbers as well, confirming the increase in dissolution of CuO-NPs over time. The transformation products of CuO-NPs are important in the ecological context since the uptake and toxicity of parent nanoparticles differ from those of the chemical species in solution. Further considerations are needed on the fate of released NPs to better assess their exposure pathways to aquatic organisms and potential environmental risks.
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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".