Hydrophobic Interactions Drive the Attachment of a Model Nanoplastic on Porous Media Surfaces
Bibliographic record
Abstract
Abstract Predicting the transport and fate of nanoparticles in the subsurface requires understanding of their interactions with collector surfaces. We report here on the effect of the less‐studied hydrophobic interactions, which are relevant to the fate of hydrophobic nano‐colloids. Using a model nanoplastic (charge‐stabilized, ethyl cellulose nanoparticles) and a model porous medium (regular array of collectors in a pore network etched on glass) we demonstrate the dominance of hydrophobic attraction over electrostatic repulsion when an otherwise hydrophilic glass surface is rendered hydrophobic via coating with octadecyltrichlorosilane (OTS). An empirical model of hydrophobic interactions between dissimilar surfaces (Yoon et al., 1997, https://doi.org/10.1006/jcis.1996.4583 ), informed by contact angle measurements, explains the irreversible attachment of ethyl cellulose nanoparticles on OTS‐coated glass surfaces confirmed by atomic force microscopy. Transport of ethyl cellulose nanoparticles in OTS‐coated glass micromodels is characterized by favorable irreversible attachment. These findings provide novel insights into the mechanisms affecting the transport and fate of nano‐colloids in subsurface aquatic environments and lend further support to the conclusion that contact angle can serve to quantify the magnitude of hydrophobic interactions between nanoparticles and collector surfaces.
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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) | 0.000 | 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".