Rhamnolipid Biosurfactant and Soy Protein Act as Effective Stabilizers in the Aggregation and Transport of Palladium-Doped Zerovalent Iron Nanoparticles in Saturated Porous Media
Bibliographic record
Abstract
Palladium-doped nanosized zerovalent iron (Pd-NZVI) particles can contribute to the transformation of chlorinated solvents and various other contaminants into innocuous products. To make Pd-NZVI an effective in situ subsurface remediation agent, these particles need to migrate through a targeted contaminated area. However, previous studies have reported very limited mobility of these particles in the groundwater environment and attributed it to rapid aggregation and subsequent pore plugging. In this study, we systematically investigated the influence of selected natural and nontoxic organic macromolecules (carboxymethyl cellulose, rhamnolipid biosurfactants, and soy protein) on the aggregation and transport behavior of bare and coated Pd-NZVI. Aggregation behavior was investigated using dynamic light scattering by monitoring the evolution of hydrodynamic diameter as a function of time, whereas transport behavior was investigated by conducting water-saturated sand-packed column experiments. While bare Pd-NZVI is prone to rapid aggregation, we observed good colloidal stability and concurrent enhanced transport of Pd-NZVI coated with carboxymethyl cellulose, rhamnolipid biosurfactants, and soy protein. Each surface modifier performed well at lower ionic strength (IS) (10 mM NaHCO3), and one of the rhamnolipid surface modifiers (JBR215) significantly enhanced transport of 150 mg/L Pd-NZVI at concentrations as low as 10 mg/L total organic carbon. However, an increase in the solution IS induced significant Pd-NZVI aggregation with a simultaneous decrease in the transport potential in accordance with the DLVO (Derjaguin, Landau, Verwey, and Overbeek) theory of colloidal stability. Nonetheless, at the highest IS (300 mM NaHCO3) investigated, the mobility of rhamnolipid-coated Pd-NZVI is significantly higher than that of Pd-NZVI coated with the other surface modifiers, suggesting that biosurfactants may be the most suitable surface modifiers in field application. Overall, this study emphasizes how stabilization of Pd-NZVI with natural macromolecules such as rhamnolipids can improve the transport potential of these reactive nanoparticles in subsurface remediation applications at concentrations significantly lower than those of other commonly used polymers.
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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".