Aggregation and Deposition of Engineered Nanomaterials in Aquatic Environments: Role of Physicochemical Interactions
Bibliographic record
Abstract
The ever-increasing use of engineered nanomaterials will lead to heightened levels of these materials in the environment. The present review aims to provide a comprehensive overview of current knowledge regarding nanoparticle transport and aggregation in aquatic environments. Nanoparticle aggregation and deposition behavior will dictate particle transport potential and thus the environmental fate and potential ecotoxicological impacts of these materials. In this review, colloidal forces governing nanoparticle deposition and aggregation are outlined. Essential equations used to assess particle-particle and particle-surface interactions, along with Hamaker constants for specific nanoparticles and the attributes exclusive to nanoscale particle interactions, are described. Theoretical and experimental approaches for evaluating nanoparticle aggregation and deposition are presented, and the major findings of laboratory studies examining these processes are also summarized. Finally, we describe some of the challenges encountered when attempting to quantify the transport of nanoparticles in aquatic environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".