Cavitation-enhanced carbonation for nano-ZnO synthesis via an ultrasonic-jet coupled reactor: Machine learning...

Area of Science:

• Materials Science
• Chemical Engineering
• Nanotechnology

Background:

• Nano-sized zinc oxide (Nano-ZnO) is crucial for catalysis, adsorption, and electronics.
• Traditional carbonization methods face challenges like poor mass transfer and particle agglomeration.
• Achieving high yield, surface area, and small crystallite size simultaneously is difficult.

Purpose of the Study:

• To develop an ultrasonic-jet cavitation reactor for enhanced Nano-ZnO synthesis.
• To optimize process parameters using Box-Behnken Design (BBD) and machine learning.
• To provide a green, controllable, and efficient route for high-performance Nano-ZnO.

Main Methods:

• Utilized a 20 kHz ultrasonic vibration unit with a jet cavitation reactor.
• Employed multi-scale synergistic intensification (turbulent shear and cavitation effects).
• Systematically investigated operating parameters (axial distance, solid-liquid ratio, pressure, jet height) using BBD.
• Constructed and evaluated four machine learning models (BP-ANN, SVR, RF, XGBoost).
• Integrated XGBoost with a genetic algorithm for multi-objective optimization.

Main Results:

• The XGBoost model demonstrated superior predictive performance (R² = 0.956).
• Optimal parameters yielded a reaction yield of 94.92%, specific surface area of 62.12 m²/g, and crystallite size of 18.89 nm.
• Synergistic cavitation effectively disrupted passivation layers, enhanced CO₂ mass transfer, and reduced agglomeration.
• Synthesized Nano-ZnO exhibited higher crystallinity and a well-developed mesoporous structure.

Conclusions:

• The ultrasonic-jet cavitation reactor significantly improves Nano-ZnO production efficiency and quality.
• Machine learning-assisted optimization provides a robust strategy for complex chemical processes.
• This approach offers a novel, green, and efficient method for preparing high-performance Nano-ZnO.