Hydrophobic Interaction Effects on the Transport of a Model Nanoplastic in 2D and 3D Porous Media
Bibliographic record
Abstract
The attraction between a hydrophobic particle and a hydrophobic surface may be strong enough for irreversible attachment to take place, even under conditions of strong electrostatic repulsion (so called “unfavorable” attachment conditions). This fact has fundamental implications for the transport and retention of hydrophobic nano-colloids (i.e., nanoplastics) in subsurface aquatic environments, where hydrophobic surfaces and interfaces are ubiquitous. Inclusion of hydrophobic attraction in extended DLVO calculations of the total interaction potential between hydrophobic negatively charged ethyl cellulose nanoparticles (a model nanoplastic) and (i) glass surfaces rendered hydrophobic via treatment with octadecyltrichlorosilane (OTS) or (ii) naturally hydrophobic air-water interfaces, indicate the absence of a barrier to attachment and support an expectation of irreversible attachment. We present here a series of experiments in saturated and unsaturated 2D (pore networks etched on glass) and 3D (columns packed with glass beads) porous media which confirm this expectation. The ability of a continuum model accounting for advection, dispersion and irreversible attachment to describe the breakthrough curves is also tested. The results advance the ability to describe the fate of hydrophobic nano-colloids in porous media for a variety of applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".