Effects of ultrasonic cavitation on neutralization process of low molecular weight polyethylene glycol
Bibliographic record
Abstract
In recent decades, ultrasound has been broadly employed in different applications, particularly in the chemical industry. Ultrasound enhances chemical reactions in a solution via generation of cavitation microbubbles. It improves mass transfer rate and kinetic rate of targeted systems under various process conditions. In polyethylene glycol (PEG) production, the neutralization reaction is generally carried out by employing a mechanical stirring operation. However, this process produces a soluble salt containing potassium and acetate ions, which are known to appreciably manipulate/alter the properties of PEG. In this paper, ultrasound influence is initially analyzed through the neutralization of deionized water. This study then focuses on the neutralization of PEG using ultrasonic cavitation and its impact on the properties of PEG. The ultrasonic cavitation unit employed in this research is a COLE‐Parmer® 500 W & 20 kHz with an ultrasonic probe. To investigate the efficiency of the ultrasonic cavitation reactor and analyze the important aspects of the implemented method, the concentration of soluble salts and conductivity of PEG neutralized by the ultrasonic cavitation method are measured and compared with those of the commercial PEG prepared by the mechanical stirring methodology. The results reveal that the neutralization reaction via the ultrasonic cavitation lowers the conductivity and concentrations of potassium and acetate ions, compared to the traditional stirring methods. It is also concluded that the minimum conductivity and the minimum content of potassium and acetate ions are achieved at 30 % power and a reaction time of 5 min. This study promises the efficient utilization of the ultrasonic cavitation in the industrial sectors, particularly in pharmaceutical 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 | 0.000 | 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".