Characterization of nanoscale zero valent iron modified by nonionic surfactant for trichloroethylene removal in the presence of humic acid: A research note
Bibliographic record
Abstract
Trichloroethylene (TCE) is a common contaminant in water and groundwater, known as suspected carcinogens, and its presence in the environment is of significant concern. Nano-scale zero-valent iron (NZVI) has emerged as an excellent reduction catalyst due to fast degradation of chlorinated solvents. However aggregation of NZVI is a serious limitation. In this study, NZVI was coated with nonionic surfactant to overcome its aggregation and to enhance its dispersion. The synthesized NZVI using water-based solution method produced nanowire-like structures mostly and a few portion of NZVI showed a spherical shape. For the modification of NZVI by surfactant, the amount of Tween 80® used to be adsorbed onto NZVI was ca. 8 mmol/kg of NZVI. The volume size distribution of the obtained surfactant modified NZVI (SNZVI) increased to d90% (cumulative size at 90%) = 2.03 µm and at 0.79 µm of mean size, while that of bare NZVI had diameter with d90% (cumulative size at 90%) = 14.12 µm and at 4.54 µm of mean size. The maximum adsorption amount of humic acid (HA) onto SNZVI was 18.70 mg/g and that of NZVI was 20.05 mg/g. For TCE removal in the presence of HA, SNZVI was not enhanced for TCE removal efficiency, compared with NZVI removal. And TCE removal using SNZVI was inhibited as HA concentration increased, but TCE degradation efficiency increased at high concentration of HA. Based on reduction of size and the reactivity of SNZVI, the present results can be applied for in situ groundwater remediation.
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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".