In this paper, data-driven and physics-embedded deep neural networks (DNN) are analyzed as potential candidates for flow field reconstruction in fluid mechanics applications. The data-driven and physics-based algorithms are employed to model the canonical case of flow past a circular cylinder. It is shown that both types of DNNs model the flow fields well, with the physics-based alternative having a higher reconstruction accuracy. Moreover, it is seen that the data-driven model provides a reasonably accurate solution with a lower computational expense as compared to the physics-embedded model. Finally a discussion on the strengths and drawbacks of each model is presented compared to mainstream numerical methods.

A polymer-assisted graphene transfer method is used to transfer sheets of monolayer and multilayer graphene onto the passivation layer of ion-sensitive field effect transistor arrays. The arrays are fabricated using commercial 0.35 μ m complementary metal-oxide-semiconductor (CMOS) technology and contain 3874 pixels sensitive to pH changes on the top silicon nitride surface. By inhibiting dispersive ion transport and hydration of this underlying nitride layer, the transferred graphene sheets help address non-idealities in the sensor response while retaining some pH sensitivity due to the presence of ion adsorption sites. Improvements in hydrophilicity and electrical conductivity of the sensing surface after graphene transfer, as well as in-plane molecular diffusion along the graphene-nitride interface, also greatly improve spatial consistency across an array, allowing for ∼20% more pixels to remain within operating range and enhancing sensor reliability. Multilayer graphene offers a better performance trade-off than monolayer graphene, reducing drift rate by ∼25% and drift amplitude by ∼59% with minimal reduction in pH sensitivity. Monolayer graphene offers slightly better temporal and spatial uniformity in performance of a sensing array, which is associated with the consistency in layer thickness and a lower defect density.

The current electronics industry has used the aggressive miniaturization of solid-state devices to meet future technological demands. The downscaling of characteristic device dimensions into the sub-10 nm regime causes them to fall below the electron–phonon scattering length, thereby resulting in a transition from quasi-ballistic to ballistic carrier transport. In this study, a well-established Monte Carlo model is employed to systematically investigate the effects of various parameters such as applied voltage, channel length, electrode lengths, electrode doping and initial temperature on the performance of nanoscale silicon devices. Interestingly, from the obtained results, the short channel devices are found to exhibit smaller heat generation, with a 2 nm channel device having roughly two-thirds the heat generation rate observed in an 8 nm channel device, which is attributed to reduced carrier scattering in the ballistic transport regime. Furthermore, the drain contacts of the devices are identified as critical design areas to ensure safe and efficient performance. The heat generation rate is observed to increase linearly with an increase in the applied electric field strength but does not change significantly with an increase in the initial temperature, despite a marked reduction in the electric current flowing through the device.