In situ chemical oxidation processes: 4D quantitative visualization of byproduct formation and deposition via micro-CT imaging
Bibliographic record
Abstract
Abstract In Canada alone, petroleum hydrocarbons have been found in groundwater and soil at approximately 1400 and 4000 sites, respectively. In situ chemical oxidation (ISCO) is a remediation technology that delivers oxidants to the subsurface to mineralize the contaminants. A typical oxidant is permanganate, which generates carbon dioxide (CO2) as gas and manganese oxides (MnO2) as precipitates. In this study, microcomputed tomography (micro-CT) imaging has been used successfully to visualize the oxidation of diesel fuel with permanganate in a 1D column packed with silica sand with respect to time (4D imaging). The byproducts of diesel fuel oxidation with permanganate have been visualized with micro-CT image analysis and subsequently qualitatively and quantitatively assessed via image processing. This is the first study to visualize the distribution of the byproducts in the pores in a noninvasive manner and to quantify both the gaseous CO2 and MnO2. Flushing water through the sample to remove the byproducts was also investigated. Imaging results showed a reduction of the gas phase by approximately 6% from water flushing, but the MnO2 deposits were not removed. CO2 and MnO2 generation from permanganate addition for contaminant remediation may result in preferential pathways, and potential permanganate bypassing of the target treatment zone may occur, reducing the efficiency of the remediation process. Using 4D micro-CT imaging offers an opportunity to further elucidate the fundamental understanding of all underlying processes and potentially help in improving the design of ISCO schemes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".