A novel, direct-push approach for detecting sulfidated nanoparticulate zero valent iron (S-nZVI) in sediments using reactive and non-reactive fluorophores
Bibliographic record
Abstract
Injection of microparticulate and nanoparticulate zero valent iron has become a regularly used method for groundwater remediation. Because of subsurface inhomogeneities, however, it is complicated to predict the ZVI transport in the subsurface, meaning that tools capable of determining its distribution after injection are highly useful. Here, we have developed a new direct-push based technique, which combines fluorescent and visible imaging, for detection of sulfidized nanoparticulate zero valent iron (S-nZVI) in the subsurface. Laboratory experiments show that the redox sensitive fluorophore riboflavin is rapidly reduced by S-nZVI within 200 s. Because the reduced riboflavin losses its green fluorescence, it can be used as S-nZVI sensitive indicator. Secondly, S-nZVI is black and tints light coloured sediment to a degree that allows detection in images. For quartz sand, 70 mg/kg of S-nZVI can be detected by visible imaging. Based on these results, a new direct-push probe (Dye-OIP) was designed based on Geoprobe's Optical Image Profiler (OIP), which was equipped with a fluorophore injection port below the OIP-unit. The injectant consisted of the redox active riboflavin mixed with the redox inactive fluorophore rhodamine WT, which fluoresces red and was used to verify that the mixture was indeed injected and detectable. Small scale experiments show that the fluorescence of this mixture in S-nZVI amended sand changes within 150 s from green with a hue of ~50 to red with a hue of ~30 when imaged with Dye-OIP. Tests of the Dye-OIP after a S-nZVI injection in a 1 m3 sized tank show that the tool could detect S-nZVI via fluorescence and visible imaging, when S-nZVI concentration was >0.2 mg per g dry sediment. Thus, these novel methods should be able to detect S-nZVI in the subsurface, without relying on infrastructure such as wells. Based on our results, the Dye-OIP could be further improved to make it suitable for regular use in the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".