Do particle size and surface functionality affect uptake and depuration of gold nanoparticles by aquatic invertebrates?
Bibliographic record
Abstract
Because of the widespread use of engineered nanoparticles (ENPs) in consumer and industrial products, it is inevitable that these materials will enter the environment. It is often stated that the uptake of ENPs into organisms in the environment is related to the particle size and surface functionality. To test this assumption, the present study investigated the uptake and depuration of gold nanoparticle (Au NPs) coated with either citrate (Au-citrate NPs), mercaptoundecanoic acid (Au-MUDA NPs), amino polyethylene glycol (PEG) thiol (Au-NH2 NPs), or PEG (Au-PEG NP) by the aquatic invertebrate Gammarus pulex. The studies were performed using a range of standard ecotoxicity media and natural waters, resulting in varying degrees of aggregation of the different NPs. Uptake of gold by G. pulex varied depending on the surface coatings, with Au-MUDA and Au-citrate NPs being taken up to a greater extent than Au-NH2 and Au-PEG NPs in all test media and natural waters. In all test media evaluated, higher amounts of amino and PEG-coated ENPs were eliminated compared with MUDA- and citrate-coated ENPs. No obvious relationships were seen between the aggregation state of the different Au NPs in treatment and uptake, suggesting that the widely accepted assumption that Au NP uptake is related to particle size does not hold for the range of aggregation states studied (67.1-178.8 nm). Positive correlations between particle number concentration in the media and uptake were observed, indicating that this factor might partly explain the differences in uptake of a particle from different media types.
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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 | 0.001 |
| 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 | 0.001 | 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".