Effect of Surfactants on Zero‐Valent Iron Nanoparticles (NZVI) Reactivity
Bibliographic record
Abstract
Abstract Zero‐valent iron nanoparticles (NZVI) were synthesized and dispersed in solutions of sodium oleate (SO), sodium laurate (SL), sodium dodecyl phosphonate (SDP), and sodium dodecyl sulfate (SDS). The reactivity of these dispersions was evaluated to assess the impact of surfactants on the reduction rate of hydrophilic reactive black 5 (RB5) and hydrophobic carbon tetrachloride (CT) model contaminants. SO and SL, used at their critical micelle concentration (CMC), lowered the reduction rate of RB5 by two and three orders of magnitude, respectively. SO and SL also decreased the reduction rate of CT by up to one order of magnitude. SDS and SDP, at their CMC, decreased the reduction rate of RB5 by approximately 50‐fold, but increased the reduction rate of CT. The decrease in RB5 reduction rate might be explained by the formation of adsorbed surfactant species on the surface of NZVI that could hinder the transport of RB5 and other hydrophilic species. For SO and SL, the inhibition of RB5 and CT reduction might also be explained by the binding of carboxylates to NZVI. The increase in CT reduction rate with SDS and SDP suggests that providing a non‐binding lipophilic environment on the surface of NZVI would improve the reduction rate and selectivity towards the reduction of hydrophobic contaminants.
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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 | 0.001 | 0.001 |
| 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".