Decomposition of chloroform and trichloroethylene in deionized water with the use of low voltage electron beam
Bibliographic record
Abstract
Paper presents the experimental setup and the results of the laboratory scale experiment concerning a water purification by electron beam irradiation. The setup was designed to use the circulation water flow in order to control (by the time of circulation) the absorbed dose of radiation. The electron beam was generated in vacuum p=10/sup -5/ Torr, and accelerated using the voltage within the range of V=100-185 kV. Electrons were injected into the water through titanium foil with thickness d=25 /spl mu/m. The electron beam current was within the range of I=0.5-1.2 mA. Trichloroethylene (TCE) and chloroform water solutions were used in the experiment. Although the initial contents of the compounds were higher than those occurring in real water sources and during drinking water treatment, it has been found that it is possible to decompose both of the chemicals with high efficiency L total decomposition in the case of TCE, and up to 90% of reduction for chloroform- using a relatively low accelerating voltage. The dependencies of the relative contaminant concentration (c=C/C/sub 0/, where C is a weight content of compound after electron irradiation, C/sub 0/ is an initial concentration of a contaminant) on the time of water circulation, and on the absorbed dose are presented. The results have indicated that the relative removal of TCE and chloroform mainly depends on the absorbed dose of electron radiation.
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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".