Unsaturated Transport and Targeted Binding of Pluronic-Coated Nanoparticles: Lysimeter Experiments
Bibliographic record
Abstract
Efficient delivery of engineered nanoparticles (NPs) to a non-aqueous-phase liquid (NAPL) target zone located above the water table requires an understanding of their transport and binding characteristics. In this investigation, NPs coated with a tunable amphiphilic copolymer were employed in a series of experiments using a 1.4-m-long lysimeter. A crude oil zone was emplaced in the lysimeter to evaluate NP binding capabilities to a representative NAPL. Experimental observations were supported by a reactive transport model. NPs coated with a polymer concentration that promotes enhanced binding to crude oil were successfully delivered and retained in the NAPL zone at concentrations about three times higher than elsewhere in the lysimeter and consistent with the distribution of total petroleum hydrocarbons. Model simulations were able to reproduce the observed asymmetrical NP breakthrough curves and retention profile. The estimated attachment rate coefficient was two orders of magnitude higher for the NAPL zone than elsewhere, supporting the observed preferential binding to the crude oil. Depth-dependent straining was used in the model to capture the NP retention observed near the top of the lysimeter, presumably due to film straining caused by the increased capillary pressure. In addition to the reversible attachment and straining mechanisms, model simulations also indicated that loss of aqueous NP mass was required deeper in the lysimeter to provide a reasonable fit to the observed NP data. Due to increased contact with sediments, the polymer structure that coats the NPs may be slowly removed, leading to aggregation and reduced mobility because of physical trapping. Findings of this study revealed that despite the demonstrated targeted binding capability of these NPs in unsaturated systems, their delivery to a target NAPL zone distant from an injection location may be a challenge due to possible aggregation over longer travel distances and thus an important design consideration.
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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 | 0.000 | 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 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".