In-situ Decomposition of Trichloroethylene Using Electrochemical Treatment Method
Bibliographic record
Abstract
Trichloroethylene (TCE) has an excellent degreasing capacity, so it is often used as a solvent for dry cleaning, and is still used for removing grease from metallic parts and so on. However, its inappropriate handling caused contamination of soil. Recently, its toxicity and carcinogenicity to humans have been concerned. By these reasons, it is highly required to remediate the contaminated soils. In the present study, the possibility of application of electrochemical treatment method to the in-situ decomposition of TCE is examined because in-situ remediation is expected to be simple and inexpensive. The experiment in the aqueous systems was conducted as a basic examination. As a result of comparing experimental values under various stirring speeds with the theoretical value calculated from mass transfer coefficient, it turned out that TCE transferring from bulk to the electrode surface is accelerated by the radicals in the boundary film near the electrode surface. Hence the TCE decomposition rate is affected by the radical formation rate or radical concentration in the boundary film. In the experiment with the soils, the TCE decomposition rate was much smaller than that in the aqueous systems. Moreover, the influence of the voltage was not observed. Therefore, it turned out that the movement of TCE in the aqueous phase near the electrode surface was the rate-controlling step in the soils. Under the condition, the TCE decomposition rate was not affected by the particle size. Consequently, it turned out TCE is not transported by bulk flow but is mostly transfered by molecular diffusion in the soil.
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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 | 0.001 |
| 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".