Nanoparticle transport in partially saturated porous media: Attachment at fluid interfaces
Bibliographic record
Abstract
• The interface between water and a trapped non-wetting phase in porous media acts as a nanoparticle collector in a manner vastly different from the solid-water interface. • Interplay between kinetically-limited irreversible attachment and nanoparticle exchange via diffusion between advective and non-advective parts of the pore space determines macroscopic behavior in the presence of a trapped nonwetting phase. • Pore network modeling is an effective tool for upscaling pore-level physics of nanoparticle attachment at fluid interfaces. Like the solid-water interface (SWI), air-water and oil-water interfaces (AWI and OWI) also act as collectors for nano-sized particles in porous media. The attachment of hydrophobic nanoparticles, which is often favorable and irreversible, is of particular interest because the transport and retention of such particles is closely linked to the fate of nanoplastics in unsaturated subsurface environments and the success of nanoremediation practices. Here, we show how a pore-network model (PNM) can be used to upscale the kinetics and extent of irreversible nanoparticle attachment at a single fluid-fluid interface under conditions of advection and dispersion in a sphere packing. By focusing on a trapped (immobile) non-wetting phase, we highlight a fundamental difference between the single-collector contact efficiency of AWI/OWI and SWI. Namely, AWI/OWI collectors, which are largely by-passed by the flowing aqueous phase, are exposed to a hydrodynamic environment dominated by diffusion. This difference has profound implications for the modelling of nanoparticle transport in porous media at the continuum (Darcy) scale. This study reveals the potential of pore network modelling as an essential complement to continuum models for upscaling the behavior of nanocolloids in porous media.
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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 | 0.001 |
| 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".