Effects of Rhamnolipid and Carboxymethylcellulose Coatings on Reactivity of Palladium-Doped Nanoscale Zerovalent Iron Particles
Bibliographic record
Abstract
Nanoscale zerovalent iron (NZVI) particles are often coated with polymeric surface modifiers for improved colloidal stability and transport during remediation of contaminated aquifers. Doping the NZVI surface with palladium (Pd-NZVI) increases its reactivity to pollutants such as trichloroethylene (TCE). In this study, we investigate the effects of coating Pd-NZVI with two surface modifiers of very different molecular size: rhamnolipid (RL, anionic biosurfactant, M.W. 600 g mol(-1)) and carboxymethylcellulose (CMC, anionic polyelectrolyte, M.W. 700 000 g mol(-1)) on TCE degradation. RL loadings of 13-133 mg TOC/g NZVI inhibited deposition of Pd in a concentration-dependent manner, thus limiting the number of available Pd sites and decreasing the TCE degradation reaction rate constant from 0.191 h(-1) to 0.027 h(-1). Furthermore, the presence of RL in solution had an additional inhibitory effect on the reactivity of Pd-NZVI by interacting with the exposed Pd deposits after they were formed. In contrast, CMC had no effect on reactivity at loadings up to 167 mg TOC/g NZVI. There was a lack of correlation between Pd-NZVI aggregate sizes and TCE reaction rates, and is explained by cryo-transmission electron microscopy images that show open, porous aggregate structures where TCE would be able to easily access Pd sites.
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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) | 0.001 | 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".