A New Look at Cavitation and the Applications of Its Liquid-Phase Effects in the Processing of Food and Fuel
Bibliographic record
Abstract
Ultrasound-induced acoustic cavitation has been studied in detail due to its chemical effects (sonochemistry) and light emission (sonoluminescence). However, the physical effects such as shear, shockwaves, etc., can also be used for useful applications despite their harmful effects, such as cavitation erosion. It has been suggested that the physical forces generated during cavitation may alter the 3-dimensional network of water molecules and hence a better hydration of macromolecules can be achieved using the cavitation process. In recent times, the physical effects of acoustic cavitation are used in food processing applications. The possibility of using hydrodynamic cavitation for food processing as an alternative approach to ultrasonic processing has been discussed in this manuscript. A special rotary disintegrator, developed by Dr. Hint in the last century is used for the generation of efficient hydrodynamic cavitation. A mathematical model has been developed for the optimization of rotary disintegrators. The suitability of the model for evaluating the process efficiency has also been tested using experimental data obtained for the production of emulsions used as fuels. The development and testing of a new mathematical model for optimizing rotary disintegrators pave new pathways to the use of hydrodynamic cavitation for processing large volumes of liquid ingredients making them suitable for fuel and food industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".