Intelligent micro-electro-mechanical system for predictive nano-drug synthesis: Acousto-microfluidic heat and mass transportation

• Comparative statistical-ML approach to designing an acoustofluidic system is presented. • Mathematical surrogate predictive modeling is used to model the system. • DE outperformed NSGA-II in Multi-objective optimization method. • Curvature radius chamber significantly influences heat and mass transportation in high voltages. • Controllability offered by the intelligent framework, enabling manipulation of heat and mass transfer. Bio-nanoparticle synthesis, essential for advanced drug delivery systems, relies on precise control over particle size, composition, and transport behavior. This study highlights micro-acoustofluidic heat and mass transport modeling as a powerful tool for designing controllable nano-drugs, including nanocarriers for respiratory systems and targeted cancer therapies, where precise control over the physical properties of nanoparticles is critical for successfully accomplishing the intended biological task. The intelligent acousto-microfluidic chamber is designed to overcome the unpredictability of heat and mass transport affecting nanoparticle properties, caused by the complex physics of acoustic streaming phenomena, thereby enabling controlled microfluidic nanoparticle synthesis. The study demonstrates how slight adjustments to the voltage settings and the arrangement of sharp edges can profoundly affect the acoustic streaming, thereby facilitating precise micro reaction phenomena. The optimal models were identified, showcasing varying efficiencies in three defined objective functions mixing, temperature fluctuation, and power consumption of proposed microsystem based on the configuration of sharp edge in the baffle and the actuation voltage. Results indicate that increasing voltage to 15 V significantly elevates the objective function by 131%. Systems with higher alpha exhibit greater temperature variations due to less intense vortices. Configurations with lower alpha and smaller curvature radii promote stable mechanical energy consumption (MEC), mitigating pressure drops. However, increasing alpha alongside higher voltage and curvature radius values leads to a substantial 115% increase in Mechanical Energy Consumption MEC. The outcomes and the intelligent framework presented here serve as a guide for creating AI-integrated micro-electro-mechanical devices, aiming controlled heat and mass transfer for precise nanoparticle synthesis. Precision is crucial in advanced biomedical fields requiring reproducible synthesis of smart nanoparticles responsive to biological and physical stimuli, as well as targeted treatments for tumors and respiratory infections. Integrated microfluidic designs enhance nanoparticle reproducibility and therapeutic effectiveness by precisely controlling heat and mass transfer in microfluidic design process. In these contexts, even minor changes in nanoparticle properties due to slight variations in synthesis conditions can significantly influence therapeutic outcomes, addressing current limitations and promoting successful clinical translation.