Fabrication of Low Cost Composite Spargers and Their Performance in Polar and Non-Polar Liquids
Bibliographic record
Abstract
Spargers are porous devices used for the continuous injection of gas bubbles into liquids.They have many applications like effective aeration in bio reactors, enhanced oil recovery, flotation, filtration and water treatment.In this study low cost spargers are fabricated, utilizing a new method.The bubble sizes and distributions are determined in an experimental setup comprising a bubble column equipped with a semi-professional camera to record the sizes of the bubbles in the column and resulting bubbles are photographed at different gas flow rates.First substrate of glass-bead spargers are fabricated.They are then covered by a layer of copper.The effect of reaction temperature and fluid properties are investigated on the size and the distribution of the produced bubbles.The results showed that the pore size of flat composite sample is decreased to 100 nm by coating by plasma focus deposition device; consequently, all bubbles produced by this sample are less than 0.1 mm inside kerosene.Comparison of BSD for all samples indicated that the smallest bubbles are produced in kerosene.By controlling the sintering conditions and through our innovative reaction and sintering method, we fabricated flat and conical composite spargers that produce 100% bubbles of less than 0.1mm diameter, and in kerosene a foamy bubble column is formed.
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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".