Investigation of the Transport and Deposition of Fullerene (C60) Nanoparticles in Quartz Sands under Varying Flow Conditions
Bibliographic record
Abstract
A coupled experimental and mathematical modeling investigation was undertaken to explore nanoscale fullerene aggregate (nC60) transport and deposition in water-saturated porous media. Column experiments were conducted with four different size fractions of Ottawa sand at two pore-water velocities. A mathematical model that incorporates nonequilibrium attachment kinetics and a maximum retention capacity was used to simulate experimental nC60 effluent breakthrough curves and deposition profiles. Fitted maximum retention capacities (S(max)), which ranged from 0.44 to 13.99 microg/g, are found to be correlated to normalized mass flux. The developed correlation provides a means to estimate S(max) as a function of flow velocity, nanoparticle size, and mean grain size of the porous medium. Collision efficiency factors, estimated from fitted attachment rate coefficients, are relatively constant (approximately 0.14) over the range of conditions considered. These fitted values, however, are more than 1 order of magnitude larger than the theoretical collision efficiency factor computed from Derjaguin-Landau-Verwey-Overbeek (DLVO) theory (0.009). Data analyses suggest that neither physical straining nor attraction to the secondary minimum is responsible for this discrepancy. Patch-wise surface charge heterogeneity on the sand grains is shown to be the likely contributor to the observed deviations from classical DLVO theory. These findings indicate that modifications to clean-bed filtration theory and consideration of surface heterogeneity are necessary to accurately predict nC60 transport behavior in saturated 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 | 0.000 |
| 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".