Neuro-computational simulation of blood flow loaded with gold and maghemite nanoparticles inside an electromagnetic microchannel under rapid and unexpected change in pressure gradient.

Abstract

In cardiovascular research, electromagnetic fields generated by Riga plates are utilized to study or manipulate blood flow dynamics, which is particularly crucial in developing treatments for conditions such as arterial plaque deposition and understanding blood behavior under varied flow conditions. This research predicts the flow patterns of blood enhanced with gold and maghemite nanoparticles (gold-maghemite/blood) in an electromagnetic microchannel influenced by Riga plates with a temperature gradient that decays exponentially, under sudden changes in pressure gradient. The flow modeling includes key physical influences like radiation heat emission and Darcy drag forces in porous media, with the flow mathematically represented through unsteady partial differential equations solved using the Laplace transform (LT) method. Results, including shear stress (SS) and rate of heat transfer (RHT), are graphically detailed, demonstrating changes in blood velocity profile with modifications in the Hartmann number and the width of electrodes, and differences in temperature and RHT between hybrid nano-blood (HNB) and nano-blood (NB). The key results indicate an increase in blood velocity distribution with higher modified Hartmann number, and a decrease with wider electrodes. Temperature is elevated in both hybrid nano-blood (HNB) and nano-blood (NB). Notably, HNB with gold and maghemite enhances heat transmission in the flow. Furthermore, an artificial intelligence-driven methodology employing an artificial neural network (ANN) has been incorporated to facilitate rapid and precise evaluations of SS and RHT, demonstrating remarkable predictive accuracy. The proposed algorithm exhibits outstanding accuracy, achieving 99.998% on the testing dataset and 96.843% during cross-validation for predicting SS, and 100% on the testing dataset, and 95.008% during cross-validation for predicting RHT. The implementation of nanotechnology with artificial intelligence promises new tools for doctors and surgeons, potentially transforming patient care in fields such as oncology, cardiology, and radiology. This model also facilitates the generation of precise electromagnetic fields to guide drug-loaded magnetic nanoparticles for applications in targeted drug delivery, hyperthermia treatment, MRI contrast enhancement, blood flow monitoring, cancer treatment, and controlled drug release.