Using Single Atom Catalysts as Nanozymes in FET Sensors FET

The restrictions of enzymes necessitate looking for new materials to be used as their alternatives. Nanomaterials which can mimic enzymatic reactions are among the most promising materials. Single- atom catalysts (SACs) function at the extreme length scale of nanomaterial catalysts and show significant catalytic activity. The development of heterogeneous catalysts with cooperativity between metal centers, keeping all the salient features of SACs, can offer a platform for the development of the next generation of single-atom nanozymes (SANs). The catalytic interactions are generally dynamic and all known catalysts are based on electronic interactions. The application of in-situ surface techniques such as XAS, XPS, XRD, and TEM along with electrochemical methods, e.g., nano-FETs that bring the opportunity to follow bio-catalyst phenomena during the reaction time can be most convenient and useful. In this research, we will develop a method to examine the biocatalytic activity of SAN by the combination of nano-FETs and simulation data. We will synthesize the new SANs with special features, e.g. as enzyme comparable bio-catalytic activity and selectivity. We will optimize the biocatalytic activity/selectivity of SANs and examine the applicability of SANs in biological applications. We provide a framework to model the device and determine the experimental parameters. We present using SACs as biocatalysts in nano- FET systems. A broad family of various metals with the ability to be applied as heterogeneous SACs is selected to be converted to SANs functionalized by different carbon-based surrounding environments. We monitor their bio-catalytic behavior specifically for bio-catalytic reactions, e.g. peroxidase reaction, CO2 reduction, hydrogen or oxygen evolution reaction, and oxygen reduction. The SANs embedded inside the nano-FET semiconductor layer will eliminate the insulating membrane to enhance bioFET sensitivity. We investigate the capability of SANs as nanozymes in the biological enzyme-based reaction and examine the sensor performance using simulation and experimental data. We develop a self-consistent computational model, discuss its novel numerical methods, and use Bayesian inversion to estimate experimental important parameters. A machine learning setting based on rough neuralnetworks will be developed to predict the sensor behavior.