Effect of size and surface chemistry of gold nanoparticles on their retention in a sediment-water system and <i>Lumbriculus variegatus</i>
Bibliographic record
Abstract
With the increased production, usage, and disposal of engineered nanoparticles (ENPs), there is growing concern over the fate of ENPs in the environment, their potential bioavailability and ecotoxicity. It is assumed that bioavailability and uptake into organisms depend on the environmental conditions as well as the physicochemical properties of ENPs, such as particle size or surface coating. A major sink for nanoparticles is expected to be sediments due to sorption and agglomeration processes. Accordingly, this study, investigated how different sizes (5 and 30 nm) and surface coatings of three different AuENPs based on citrate (AuCIT), mercaptoundecanoic acid (AuMUDA), and bovine serum albumin (AuBSA) affected the retention of ENPs in a sediment-water system and subsequent uptake into sediment-dwelling organism Lumbriculus variegatus (L. variegatus). Surface charge was found to be one of the factors affecting retention of the AuENPs in the sediment-water system. More negatively charged AuENPs had a higher mass fraction in the supernatant after 24 h exposure. Furthermore, the stability of AuENPs in the supernatant depended more on their zeta potential than particle size (5 nm vs. 30 nm). The surface coating was found to play an important role in the uptake (after depuration) of Au in L. variegatus, that is, AuBSA > AuCIT > AuMUDA.
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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".