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Accurate flood forecasting and efficient emergency response operations are vital, especially in the case of urban flash floods. The dense distribution of power lines in urban areas significantly impacts search and rescue operations during extreme flood events. However, no existing emergency response frameworks have incorporated the impacts of overhead power lines on lifeboat rescue operations. This study aims to determine the necessity and feasibility of incorporating overhead power line information into an emergency response framework using Manville, New Jersey during Hurricane Ida as a test bed. We propose an integrated framework, which includes a building-scale flood model, urban point cloud data, a human vulnerability model, and network analysis, to simulate rescue operation feasibility during Hurricane Ida. Results reveal that during the most severe point of the flood event, 46% of impacted buildings became nonrescuable due to complete isolation from the road network, and a significant 67.7% of the municipality’s areas that became dangerous for pedestrians also became inaccessible to rescue boats due to overhead power line obstruction. Additionally, we identify a continuous 10-hour period during which an average of 43.4% of the 991 impacted buildings faced complete isolation. For these structures, early evacuation emerges as the sole means to prevent isolation. This research highlights the pressing need to consider overhead power lines in emergency response planning to ensure more effective and targeted flood resilience measures for urban areas facing increasingly frequent extreme precipitation events. 

GaN-on-GaN vertical diode is a promising device for next-generation power electronics. Its breakdown voltage (BV) is limited by edge termination designs such as guard rings. The design space of guard rings is huge and it is difficult to optimize manually. In this paper, we propose an effective inverse design strategy to co-optimize BV and (V F Q) −1 , where BV, V F , and Q are the breakdown voltage, forward voltage, and reserve capacitive charge of the diode, respectively. Using rapid Technology Computer-Aided-Design (TCAD) simulations, neural network (NN), and Pareto front generation, a GaN-on-GaN diode is optimized within 24 hours. We can obtain structures with 200V higher BV at medium (V F Q) −1 or find a nearly ideal BV structure with 25% higher BV 2 /R on compared to the best randomly generated TCAD data. 

Edge termination is the enabling building block of power devices to exploit the high breakdown field of wide bandgap (WBG) and ultra-wide bandgap (UWBG) semiconductors. This work presents a heterogeneous junction termination extension (JTE) based on p-type nickel oxide (NiO) for gallium oxide (Ga2O3) devices. Distinct from prior JTEs usually made by implantation or etch, this NiO JTE is deposited on the surface of Ga2O3 by magnetron sputtering. The JTE consists of multiple NiO layers with various lengths to allow for a graded decrease in effective charge density away from the device active region. Moreover, this surface JTE has broad design window and process latitude, and its efficiency is drift-layer agnostic. The physics of this NiO JTE is validated by experimental applications into NiO/Ga2O3 p–n diodes fabricated on two Ga2O3 wafers with different doping concentrations. The JTE enables a breakdown voltage over 3.2 kV and a consistent parallel-plate junction field of 4.2 MV/cm in both devices, rendering a power figure of merit of 2.5–2.7 GW/cm2. These results show the great promise of the deposited JTE as a flexible, near ideal edge termination for WBG and UWBG devices, particularly those lacking high-quality homojunctions. 

This work demonstrates a novel junction termination extension (JTE) with a graded charge profile for vertical GaN p-n diodes. The fabrication of this JTE obviates GaN etch and requires only a single-step implantation. A bi-layer photoresist is used to produce an ultra-small bevel angle (~0.1°) at the sidewall of a dielectric layer. This tapered dielectric layer is then used as the implantation mask to produce a graded charge profile in p-GaN. The fabricated GaN p-n diodes show a breakdown voltage ( BV ) of 1.7 kV (83% of the parallel-plane limit) with positive temperature coefficient, as well as a high avalanche current density over 1100 A/cm 2 at BV in the unclamped inductive switching test. This robust avalanche is ascribed to the migration of the major impact ionization location from the JTE edge to the main junction. This single-implant, efficient, avalanche-capable JTE can potentially become a building block of many vertical GaN devices, and its fabrication technique has wide device and material applicability. 

We present a multi-scale mathematical model and a novel numerical solver to study blood plasma flow and oxygen concentration in a prototype model of an implantable Bioartificial Pancreas (iBAP) that operates under arteriovenous pressure differential without the need for immunosuppressive therapy. The iBAP design consists of a poroelastic cell scaffold containing the healthy transplanted cells, encapsulated between two semi-permeable nano-pore size membranes to prevent the patient’s own immune cells from attacking the transplant. The device is connected to the patient’s vascular system via an anastomosis graft bringing oxygen and nutrients to the transplanted cells of which oxygen is the limiting factor for long-term viability. Mathematically, we propose a (nolinear) fluid–poroelastic structure interaction model to describe the flow of blood plasma through the scaffold containing the cells, and a set of (nonlinear) advection–reaction–diffusion equations defined on moving domains to study oxygen supply to the cells. These macro-scale models are solved using finite element method based solvers. One of the novelties of this work is the design of a novel second-order accurate fluid–poroelastic structure interaction solver, for which we prove that it is unconditionally stable. At the micro/nano-scale, Smoothed Particle Hydrodynamics (SPH) simulations are used to capture the micro/nano-structure (architecture) of cell scaffolds and obtain macro-scale parameters, such as hydraulic conductivity/permeability, from the micro-scale scaffold-specific architecture. To avoid expensive micro-scale simulations based on SPH simulations for every new scaffold architecture, we use Encoder–Decoder Convolution Neural Networks. Based on our numerical simulations, we propose improvements in the current prototype design. For example, we show that highly elastic scaffolds have a higher capacity for oxygen transfer, which is an important finding considering that scaffold elasticity can be controlled during their fabrication, and that elastic scaffolds improve cell viability. The mathematical and computational approaches developed in this work provide a benchmark tool for computational analysis of not only iBAP, but also, more generally, of cell encapsulation strategies used in the design of devices for cell therapy and bio-artificial organs. 

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

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