Dissolution Rate of BTEX Contaminants in Water
Bibliographic record
Abstract
Abstract The BTEX group of contaminants consists of benzene, ethylbenzene, toluene, and three isomers of xylene. The dissolution rate, solubility, slick area and mass transfer coefficient were examined for the BTEX. The release of BTEXs into the environment is influenced by their fate and transport mechanisms. Thus, the fate and transport mechanisms are affected by the contaminant characteristics, which vary with the different BTEX compounds. A comprehensive model has been developed to simulate the molecular dissolution rate of BTEX contaminants in a natural water stream. The developed model modifies the work of Cohen et al. (1980) by considering the physicochemical properties of the BTEX compounds and physical processes relevant to the spreading of contaminants in the sea. The model shows that Benzene with greater solubility in water and dissolution coefficient has the largest dissolution rate while o‐xylene with the biggest density has the lowest dissolution rate because of its low fraction. The benzene dissolution rate is about 2.6, 20.6 times that of Toluene, ethylbenzene, respectively, but with a varying proportion with the xylenes. The model has been validated against the theories of mass transfer rate at the surface at appropriate surface area. The developed model can be found useful in prediction and monitoring the dissolution rate of contaminants in soil and water systems.
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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 | 0.001 |
| 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 | 0.000 | 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".