AI-based testing of urine containing penta hybrid nanoparticles within a charged bioactive rotational channel under strong magnetic fields: Implications for bioengineering

Abstract

The complexities of electro-osmotically induced flow are increasingly recognized for their broad applications in bioengineering. This could lead to innovative applications that use electromagnetic fields and urine infused with nanoparticles, customized to address the dynamics of various diagnostic kits and specific medical conditions. This study explores the dynamic behaviors of immiscible urine containing penta hybrid nanoparticles-engineered with five distinct functional components-within a specially designed rotational channel that both charges and activates them biologically under strong magnetic fields, employing artificial intelligence (AI) computing for analysis. This model subsumes various physical factors like Hall and ion-slip currents, Joule heating, heat generation, and interfacial nanolayers, simplifying the complexities through Debye-Hückel linearization strategies to analytically solve the dimensionless equations. Detailed graphs and tables elucidate the impact of these factors on flow dynamics and physical metrics. For instance, findings indicate that the Lorentz force acts as an inhibitory factor, reducing urine fluidity, while an increased thickness of the interfacial nanolayer correlates with lower thermal distribution levels. Importantly, the Nusselt number (NN) without a nanolayer (WNL) exceeds that with a nanolayer (NL). This study employs an AI-powered artificial neural network (ANN) for rapid and precise evaluations of the skin friction coefficient (SFC) and Nusselt number (NN), demonstrating strong predictive accuracy with minimal error rates of 0.02 % for SFC and 0.03 % for NN. This research also highlights the implications of these findings for designing future bioengineering solutions, emphasizing the role of AI in improving the precision and efficiency of biomedically relevant technologies.