Fate of Zinc Oxide Nanoparticles in a Biosolid Slurry Characterized for Metal Complexation Characteristics
Bibliographic record
Abstract
Zinc oxide nanoparticles (NPs) present in domestic wastewaters may accumulate in biosolids used as fertilizer. In this paper, metal complexation by typical biosolids is explored using methods from the humics literature. Uptake of Zn from NPs in the biosolids is evaluated. Finally, the kinetics of release of Zn species are reported as a function of (i) pH and (ii) the presence of strong binding ligands (e.g., ion exchange resin promoting release). The investigation revealed that (i) metal binding sites of biosolids are analogs of humic substances, (ii) ZnO NPs do not survive in the digestion environment, and (iii) any ZnO NPs dissolve to aqueous Zn2+ in <10 d. Kinetics of Zn in biosolids revealed that Zn release is a function of biosolid protonation. At pH 8, Zn is retained in the biosolids, whereas at pH 4.5, 10% of Zn is released from the biosolids. Adding a chelating resin to the system at pH 5.0 led to Zn release from the biosolids as per Noyes–Whitney kinetics, releasing 85% of the bound Zn in 360 h. Fifteen percent of Zn appeared to be irreversibly bound. Core Ideas Thorough chemical characterization of biosolids was performed. Metal binding sites of biosolids are analogs of humic substances. Release of Zn species by biosolids is a function of biosolids protonation. Added chelating agent drove Zn release from biosolids according Noyes–Whitney kinetics.
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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".