Characterization of nZVI Mobility in a Field Scale Test
Bibliographic record
Abstract
Nanoscale zerovalent iron (nZVI) particles were injected into a contaminated sandy subsurface area in Sarnia, Ontario. The nZVI was synthesized on site, creating a slurry of 1 g/L nanoparticles using the chemical precipitation method with sodium borohydride (NaBH4) as the reductant in the presence of 0.8% wt. sodium carboxymethylcellulose (CMC) polymer to form a stable suspension. Individual nZVI particles formed during synthesis had a transmission electron microscopy (TEM) quantified particle size of 86.0 nm and dynamic light scattering (DLS) quantified hydrodynamic diameter for the CMC and nZVI of 624.8 nm. The nZVI was delivered to the subsurface via gravity injection. Peak normalized total Fe breakthrough of 71% was observed 1m from the injection well and remained above 50% for the 24 h injection period. Samples collected from a monitoring well 1 m from the injection contained nanoparticles with TEM-measured particle diameter of 80.2 nm and hydrodynamic diameter of 562.9 nm. No morphological changes were discernible between the injected nanoparticles and nanoparticles recovered from the monitoring well. Energy dispersive X-ray spectroscopy (EDS) was used to confirm the elemental composition of the iron nanoparticles sampled from the downstream monitoring well, verifying the successful transport of nZVI particles. This study suggests that CMC stabilized nZVI can be transported at least 1 m to the contaminated source zone at significant Fe(0) concentrations for reaction with target 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 | 0.000 | 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".