Non-equilibrium Green's function predictions of band tails and band gap narrowing in III-V semiconductors and nanodevices

High-doping induced Urbach tails and band gap narrowing play a significant role in determining the performance of tunneling devices and optoelectronic devices such as tunnel field-effect transistors (TFETs), Esaki diodes and light-emitting diodes. In this work, Urbach tails and band gap narrowing values are calculated explicitly for GaAs, InAs, GaSb and GaN as well as ultra-thin bodies and nanowires of the same. Electrons are solved in the non-equilibrium Green's function method in multi-band atomistic tight binding. Scattering on polar optical phonons and charged impurities is solved in the self-consistent Born approximation. The corresponding nonlocal scattering self-energies as well as their numerically efficient formulations are introduced for ultra-thin bodies and nanowires. Predicted Urbach band tails and conduction band gap narrowing agree well with experimental literature for a range of temperatures and doping concentrations. Polynomial fits of the Urbach tail and band gap narrowing as a function of doping are tabulated for quick reference

Decentralized Discrete-Time Neural Network Controller for a Class of Nonlinear Systems with Unknown Interconnections

A novel decentralized neural network (NN) controller in discrete-time is designed for a class of uncertain nonlinear discrete-time systems with unknown interconnections. Neural networks are used to approximate both the uncertain dynamics of the nonlinear systems and the unknown interconnections. Only local signals are needed for the decentralized controller design and the stability of the overall system can be guaranteed using the Lyapunov analysis. Further, controller redesign for the original subsystems is not required when additional subsystems are appended. Simulation results demonstrate the effectiveness of the proposed controller. The NN does not require an offline learning phase and the weights can be initialized at zero or randomly. Simulation results verify the theoretical conclusions

Non-noble electrocatalysts for alkaline fuel cells

The doping of solid phase precursors followed by pyrolysis or the copyrolysis of gas phase precursors has allowed us to produce catalysts with good activity toward oxygen reduction. Efforts are currently underway to better understand the reasons for the catalytic activity of the bulk doped catalysts with a view toward further improving their activity

Advanced double layer capacitors

There is a need for large amounts of power to be delivered rapidly in a number of airborne and space systems. Conventional, portable power sources, such as batteries, are not suited to delivering high peak power pulses. The charge stored at the electrode-electrolyte double layer is, however, much more assessible on a short time scale. Devices exploiting this concept were fabricated using carbon and metal oxides (Pinnacle Research) as the electrodes and sulfuric acid as the electrolyte. The approach reported, replaces the liquid sulfuric acid electrolyte with a solid ionomer electrolyte. The challenge is to form a solid electrode-solid ionomer electrolyte composite which has a high capacitance per geometric area. The approach to maximize contact between the electrode particles and the ionomer was to impregnate the electrode particles using a liquid ionomer solution and to bond the solvent-free structure to a solid ionomer membrane. Ruthenium dioxide is the electrode material used. Three strategies are being pursued to provide for a high area electrode-ionomer contact: mixing of the RuOx with a small volume of ionomer solution followed by filtration to remove the solvent, and impregnation of the ionomer into an already formed RuOx electrode. RuOx powder and electrodes were examined by non-electrochemical techniques. X-ray diffraction has shown that the material is almost pure RuO2. The electrode structure depends on the processing technique used to introduce the Nafion. Impregnated electrodes have Nafion concentrated near the surface. Electrodes prepared by the evaporation method show large aggregates of crystals surrounded by Nafion

Knowledge, curricula, and teaching methods: the case of India

In this short note I have developed the outline of what I regard as the major phases in India’s curriculum and teaching, from the pre-independence period to the present day. It is a quick sketch of a complex country with a complex history. I have not developed the idea of regionalism in this sketch, nor have I explored the question of tribal peoples within India, which presents a new set of problems and issues that are also worthy of exploration. I have also not dealt with the questions of te..

Near Optimal Neural Network-Based Output Feedback Control of Affine Nonlinear Discrete-Time Systems

In this paper, a novel online reinforcement learning neural network (NN)-based optimal output feedback controller, referred to as adaptive critic controller, is proposed for affine nonlinear discrete-time systems, to deliver a desired tracking performance. The adaptive critic design consist of three entities, an observer to estimate the system states, an action network that produces optimal control input and a critic that evaluates the performance of the action network. The critic is termed adaptive as it adapts itself to output the optimal cost-to-go function which is based on the standard Bellman equation. By using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the estimation and tracking errors and weight estimates is demonstrated. The effectiveness of the controller is evaluated for the task of nanomanipulation in a simulation environment

Reinforcement Learning Neural-Network-Based Controller for Nonlinear Discrete-Time Systems with Input Constraints

A novel adaptive-critic-based neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of actuator constraints. The constraints of the actuator are treated in the controller design as the saturation nonlinearity. The adaptive critic NN controller architecture based on state feedback includes two NNs: the critic NN is used to approximate the strategic utility function, whereas the action NN is employed to minimize both the strategic utility function and the unknown nonlinear dynamic estimation errors. The critic and action NN weight updates are derived by minimizing certain quadratic performance indexes. Using the Lyapunov approach and with novel weight updates, the uniformly ultimate boundedness of the closed-loop tracking error and weight estimates is shown in the presence of NN approximation errors and bounded unknown disturbances. The proposed NN controller works in the presence of multiple nonlinearities, unlike other schemes that normally approximate one nonlinearity. Moreover, the adaptive critic NN controller does not require an explicit offline training phase, and the NN weights can be initialized at zero or random. Simulation results justify the theoretical analysi