Selectivity of Nano Zerovalent Iron in <i>In Situ</i> Chemical Reduction: Challenges and Improvements
Bibliographic record
Abstract
Nano zerovalent iron (nZVI) is a promising remediation technology utilizing in situ chemical reduction (ISCR) to clean up contaminated groundwater at hazardous waste sites. The small particle size and large surface area of nZVI result in high reactivity and rapid destruction of contaminants. Over the past 20 years, a great deal of research has advanced the nZVI technology from bench-scale tests to field-scale applications. However, to date, the overall number of well-characterized nZVI field deployments is still small compared to other alternative remedies that are more widely applied. Apart from the relatively high material cost of nZVI and questions regarding possible nanotoxicological side effects, one of the major obstacles to the widespread utilization of nZVI in the field is its short persistence in the environment due to natural reductant demand (NRD). The NRD for nZVI is predominantly due to reduction of water, but other reactions with naturally present oxidants (e.g., oxygen) occur, resulting in situ conditions that are reducing (high in ferrous iron phases and H2) but with little or no Fe(0). This article reviews the main biogeochemical processes that determine the selectivity and longevity of nZVI, summarizes data from prior (laboratory and field) studies on the longevity of various common types of nZVI, and describes modifications of nZVI that could improve its selectivity and longevity for full-scale applications of ISCR. © 2016 Wiley Periodicals, Inc.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".