Deposition of Carboxymethylcellulose-Coated Zero-Valent Iron Nanoparticles onto Silica: Roles of Solution Chemistry and Organic Molecules
Bibliographic record
Abstract
Zero-valent iron nanoparticles (nZVI) used in the remediation of contaminated subsurface environments are commonly stabilized using polymer coatings. A bottom-up synthesis approach was used to synthesize carboxymethylcellulose (CMC)-coated nZVI particles with increased colloidal stability. The influence of water chemistry and selected environmental molecules, namely, fulvic acids and rhamnolipids, on the aggregate size and surface charge of the bare and CMC-coated nZVI particles was systematically examined using dynamic light scattering (DLS), nanoparticle tracking analysis (NTA), and laser Doppler velocimetry. A quartz crystal microbalance with energy dissipation monitoring (QCM-D) was used to quantify the deposition rates of bare and CMC-coated nZVI particles onto a silica surface over a broad range of solution ionic strengths and in the presence of naturally occurring molecules. Nanoscale ZVI particle deposition was found to increase with IS for many of the conditions investigated. CMC acted as a better colloidal stabilizer when covalently bound to nZVI particles than when physisorbed onto the nanoparticle surface after particle synthesis. The lowest nanoparticle deposition rates were observed for CMC-coated nZVI in the presence of the rhamnolipid biosurfactant.
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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) | 0.001 | 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".