Conclusion
The optimum tip immersion depth which facilitates mixing is estimated from the relationship between fluid flow pattern and tip position using a simplified acoustic streaming model. A time dependent heat transfer model is also presented which can estimate temperature rise in laboratory scale sonication given process inputs. Assumptions were made and validated by experiments which enabled us to decouple heat transfer model from acoustic pressure and velocity distribution. Using the model and previous experimental data, effect of temperature on yield of BL21 DE3 Star strain is calculated. The model offers us process insight on how input parameters like power, sample volume and pulse time affects temperature rise. This model can also be used to tune the parameters to control temperature in other lab scale sonication procedures like extraction of biological and food samples where temperature rise is a concern; this model allows one to narrow in on a range of suitable power and pulse setting without the need for lengthy iterative laboratory experiments. Use of this model can be extended to any tip sonicator, irrespective of vendors, by empirically determining input power as a function of amplitude setting and volume. Although this model is used for laboratory scale tubes, in future work, it could also be applied for scale up in larger vessels. Finally, the central finding of an optimal temperature range for cell extract preparation can be used in design of a closed loop sonication system with real time temperature control.