Cavitation-enhanced carbonation for nano-ZnO synthesis via an ultrasonic-jet coupled reactor: Machine learning prediction and multi-objective optimization using a genetic algorithm.

Nano-sized zinc oxide (Nano-ZnO) is of significant interest in catalysis, adsorption, rubber reinforcement, and electronic devices due to its high specific surface area, tunable crystal structure, and superior interfacial properties. However, the traditional carbonization process is often constrained by inefficient gas-liquid-solid mass transfer, the formation of a dense "self-passivation layer" on particle surfaces, and severe agglomeration during crystal growth. Consequently, achieving a synergistic enhancement in reaction yield, specific surface area, and crystallite size reduction remains a significant challenge. In this study, a 20 kHz clamp-mounted horn-type ultrasonic vibration unit coupled with an ultrasonic-jet cavitation reactor was developed to address these issues. Through multi-scale synergistic intensification involving macroscopic turbulent shear and microscopic cavitation effects, the reactor significantly enhances the interfacial renewal rate, mass transfer efficiency, and nucleation density, thereby modulating carbonization kinetics and improving product microstructure. Based on the Box-Behnken Design (BBD), the effects of four key operating parameters-ultrasonic axial distance, solid-liquid ratio, incident pressure, and jet outlet height-on reaction yield, BET specific surface area, and crystallite size were systematically investigated. Four machine learning models-BP-ANN, SVR, RF, and XGBoost-were constructed and evaluated using the BBD experimental dataset. Comparative analysis revealed that the XGBoost model exhibited superior predictive performance (R