Influence of Pluronic coating formulation on iron oxide nanoparticle transport in natural and oil‐impacted sandy aquifer media
Bibliographic record
Abstract
Abstract Targeted delivery of nanoparticles has the potential to enhance remediation and characterization of sites contaminated with non‐aqueous phase liquids (NAPLs) by ensuring delivery of treatment or contrast agents to the NAPL/water interface. For a targeted delivery technique to be successful, nanoparticles must be capable of transporting through porous media and binding to NAPLs under relevant geological conditions. In this study, successful targeted delivery of nanoparticles to sandy aquifer material mixed with crude oil was achieved using an active targeting technique based on an amphiphilic polymer coating. It was determined that the molecular structure and concentration of the nanoparticle coating greatly influenced the recovery of nanoparticles injected into saturated columns. Coatings with longer polymer molecules and lower polymer concentrations reduced recovery, and the nanoparticle coating formulation could be adjusted to improve transport while maintaining targeted binding behaviour. This study demonstrated that nanoparticle retention in oil‐impacted sand exceeded that of clean sand in flow through experiments, indicating that a nanoparticle targeted delivery strategy for soil contaminated with LNAPLs such as crude oil is possible under the experimental conditions explored.
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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".