Impacts of Select Organic Ligands on the Colloidal Stability, Dissolution Dynamics, and Toxicity of Silver Nanoparticles
Bibliographic record
Abstract
Key understanding of potential transformations that may occur on silver nanoparticle (AgNP) surface upon interaction with naturally ubiquitous organic ligands (e.g., -SH (thoil), humic acid, or -COO (carboxylate)) is limited. Herein we investigated how dissolved organic carbon (DOC), -SH (in cysteine, a well-known Ag(+) chelating agent), and -COO (in trolox, a well-known antioxidant) could alter the colloidal stability, dissolution rate, and toxicity of citrate-functionalized AgNPs (citrate-AgNPs) against a keystone crustacean Daphnia magna. Cysteine, DOC, or trolox amendment of citrate-AgNPs differentially modified particle size, surface properties (charge, plasmonic spectra), and ion release dynamics, thereby attenuating (with cysteine or trolox) or promoting (with DOC) AgNP toxicity. Except with DOC amendment, the combined toxicity of AgNPs and released Ag under cysteine or trolox amendment was lower than of AgNO3 alone. The results of this study show that citrate-AgNP toxicity can be associated with oxidative stress, ion release, and the organism biology. Our evidence suggests that specific organic ligands available in the receiving waters can differentially surface modify AgNPs and alter their environmental persistence (changing dissolution dynamics) and subsequently the toxicity; hence, we caveat to generalize that surface modified nanoparticles upon environmental release may not be toxic to receptor organisms.
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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".