Micro‐nanobubbles assisted fouling reduction in membrane distillation for desalination
Bibliographic record
Abstract
Abstract The present study describes the use of micro‐nanobubbles (MNBs) as an easy‐to‐use method for delaying scaling in membrane distillation (MD) while treating highly concentrated saline feed (10 wt.%). The hydrodynamic flow conditions with MNBs are enhanced compared to the traditional gas bubbling in MD. Specifically, an air cushion is formed between the membrane surface and bulk, limiting the deposition of solid particles on the surface. Also, turbulence created by the MNBs reduces the membrane fouling. The findings of the MD performance analysis showed that, in the absence of nanobubbles, considerable membrane scaling occurred during the treatment of high‐salinity feed, which significantly decreased the distillate flux to 70% after 20 h. The one‐time incorporation of air NBs into the saline feed significantly reduced salt precipitation or fouling on the polyvinylidene fluoride (PVDF) membrane surface by delaying the start of flux drop and preventing membrane wetting, thus improving MD performance. For nanobubble‐assisted MD, the only measured flux drop after 20 h of operation was 57% under similar input concentration and operating parameters.
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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".