Application of Artificial Intelligence in the Development of Nanoreactors for Precise Chemical Synthesis: An Integrated Approach Based on Deep Learning, Advanced Statistical Analysis, and Multiscale Optimization

Nanoreactors represent cutting-edge platforms for precise chemical synthesis, offering unprecedented control over reaction dynamics at the nanoscale. However, their design, optimization, and scalability pose significant challenges that demand innovative computational tools. This study explores the application of artificial intelligence (AI), encompassing deep learning, reinforcement learning, and evolutionary optimization algorithms, to advance nanoreactor development. Leveraging a hybrid dataset (combining simulated and experimental data) from microfluidic nanoreactors and porous nanostructures, we developed advanced models, including deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), random forests (RF), and genetic algorithms (GA). Statistical analyses, such as nonlinear multivariate regression, Bayesian sensitivity analysis, multilevel statistical modeling, uncertainty quantification, and spatiotemporal analysis, were employed to evaluate the models' accuracy, stability, and generalizability. The results demonstrate that the hybrid DNN-CNN-RNN model outperformed others, achieving a coefficient of determination (R²) of ۰.۹۷, a root mean square error (RMSE) of ۰.۰۱۲, and a Bayesian information criterion (BIC) of -۱۶۰۰. Bayesian analysis identified temperature, pressure, nanoreactor geometry, flow rate, and surface properties of nanostructures as the most critical factors influencing yield, purity, and energy efficiency. This study proposes an integrated framework for designing intelligent nanoreactors equipped with real-time sensors, poised to revolutionize applications in pharmaceuticals, nanomaterials, clean energy, and advanced catalysis. Furthermore, we introduce an innovative approach to integrate AI with self-regulating nanoreactors, enabling dynamic optimization during reactions.

کلیدواژه ها:

Artificial Intelligence ، Deep Learning ، Reinforcement Learning ، Nanoreactor ، Chemical Synthesis ، Advanced Statistical Analysis ، Multiscale Optimization ، Intelligent Nanoreactors