Hydrophobic Interactions Drive the Attachment of a Model Nanoplastic on Hydrophobic Collector Surfaces
Bibliographic record
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 (i.e., nanoplastics) and their attachment onto solid-water (SWI) and air-water interfaces (AWI) in groundwater. 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), informed by contact angle measurements, explains the irreversible attachment of ethyl cellulose nanoparticles on OTS-coated glass surfaces, which is confirmed by atomic force microscopy. The same model explains the irreversible attachment of the model nanoplastic on AWIs, which is revealed by fluorescence microscopy. Transport experiments in microfluidic pore networks etched on glass further demonstrate the irreversible attachment of ethyl cellulose nanoparticles on hydrophobic collector surfaces (SWI or AWI) even in the absence of salt. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".