Influence of Residual Polymer on Nanoparticle Deposition in Porous Media
Bibliographic record
Abstract
Although surface coatings and free polymers are known to affect the mobility of nanoparticles in water-saturated porous media, the influence of these compounds on nanoparticle deposition behavior has received limited attention. A series of column experiments was conducted to evaluate the transport and retention of quantum dots (QDs) coated with a synthetic polymer, polyacrylic acid-octylamine (PAA-OA). Initial column studies, conducted with three size fractions of Ottawa sand, resulted in unusual solid-phase retention profiles, characterized by low QD deposition near the column inlet and increasing solid-phase concentrations along the column until a plateau or limiting capacity was reached near the column midpoint. Mathematical modeling studies indicated that the observed retention behavior could not be reproduced using one-dimensional simulators based on either clean-bed filtration theory or a modified filtration theory (MFT) model that incorporated a maximum retention capacity. Additional column studies demonstrated that changes in the inlet end plate configuration designed to ensure uniform flow did not alter the observed effluent breakthrough curves (BTCs) or shape of the retention profile. Subsequent QD transport experiments, pretreated by flushing with a pulse of PAA-OA solution, resulted in almost complete QD breakthrough with minimal retention. It is postulated that free polymer was preferentially adsorbed onto the solid surface near the column inlet, thereby preventing QD attachment, whereas in the down-gradient portion of the column, QDs attached to the solid phase without competition from the polymer. These findings reveal the importance of accounting for the influence of coconstituents on nanoparticle deposition and demonstrate the need to simulate both transport and retention data when assessing nanoparticle mobility in porous media.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 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".