Quantified Pore-Scale Nanoparticle Transport in Porous Media and the Implications for Colloid Filtration Theory
Bibliographic record
Abstract
This study evaluates the pore-scale distribution of silver nanoparticles during transport through a sandy porous medium via quantitative synchrotron X-ray computed microtomography (qSXCMT). The associated distributions of nanoparticle flow velocities and mass flow rates were obtained by coupling these images with computational fluid dynamic (CFD) simulations. This allowed, for the first time, the comparison of nanoparticle mass flow with that assumed by the standard colloid filtration theory (CFT) modeling approach. It was found that (i) 25% of the pore space was further from the grain than assumed by the CFT model; (ii) the average pore velocity agreed well between results of the coupled qSXCMT/CFD approach and the CFT model within the model fluid envelope, although the former were 2 times larger than the latter in the centers of the larger pores and individual velocities were upwards of 20 times those in the CFT model at identical distances from grain surfaces ; and (iii) approximately 30% of all nanoparticle mass and 38% of all nanoparticle mass flow occurred further away from the grain surface than expected by the CFT model. This work suggests that a significantly smaller fraction of nanoparticles than expected will contact a grain surface by diffusion via CFT models, likely contributing to inadequate CFT model nanoparticle transport predictions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".