Theoretical model for micro-nano-bubbles mass transfer during contaminant treatment
Bibliographic record
Abstract
Micro-nano-bubbles (MNBs), in particular ozone and oxygen MNBs, represent an innovative method for wastewater treatment and groundwater remediation. Although several models have been developed to describe the fate of MNBs in water, a theoretical model describing the mass transfer processes of MNBs during treatment of contaminants has not been adequately developed. In this study, a theoretical model considering the decomposition and reaction of dissolved gas is proposed based on the Epstein and Plesset theory, aiming to describe mass transfer processes of oxygen and ozone MNBs during treatment of contaminants. The life of MNBs, volume-weighted average dissolved gas concentration and utilisation efficiency of gas, which are of significant importance in MNB applications, are further studied. The life of MNBs increases with the half-life of dissolved gas, but the impact diminishes. For gases which present a slow consumption rate and a long half-life, the effect of half-life on the life of MNBs is negligible. With controlled total mass of gas, smaller-sized bubbles result in a significantly higher dissolved gas concentration and more efficient treatment of contaminants. The newly developed model can describe the fate of MNBs during treatment of contaminated water and theoretically proves the advantages of MNBs over large-sized bubbles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".