This paper presents an innovative approach to fabricating controllable n-type doping graphene transistors with extended air stability by using self-encapsulated doping layers of titanium suboxide (TiOx) thin films, which are an amorphous phase of crystalline TiO(2) and can be solution processed. The nonstoichiometry TiOx thin films consisting of a large number of oxygen vacancies exhibit several unique functions simultaneously in the n-type doping of graphene as an efficient electron-donating agent, an effective dielectric screening medium, and also an encapsulated layer. A novel device structure consisting of both top and bottom coverage of TiOx thin layers on a graphene transistor exhibited strong n-type transport characteristics with its Dirac point shifted up to -80 V and an enhanced electron mobility with doping. Most interestingly, an extended stability of the device without rapid degradation after doping was observed when it was exposed to ambient air for several days, which is not usually observed in other n-type doping methods in graphene. Density functional theory calculations were also employed to explain the observed unique n-type doping characteristics of graphene using TiOx thin films. The technique of using an "active" encapsulated layer with controllable and substantial electron doping on graphene provides a new route to modulate electronic transport behavior of graphene and has considerable potential for the future development of air-stable and large-area graphene-based nanoelectronics.

A novel organic/graphene/inorganic -heterostructure, consisting of a graphene layer encapsulated by n- and p-type photoactive materials with complementary absorptions, enables the control of dual n- and p-typed transport behaviors of a graphene transistor under selective UV or visible light illumination. A graphene-based p-n junction created by spatially patterned wavelength-selective illumination using the organic/graphene/inorganic heterostructure is also demonstrated.

This paper uses differing objective functions under the Longstaff model (1990) to discuss the strategic choices faced by the creditor when deciding whether to grant maturity extension on a defaulted loan. The results reveal that: (1) it ensures that the return per unit of risk is higher after maturity extension than before; (2) it recognizes that the risk profile of the firm substantially affects the strategic behavior of the creditor; and (3) it demonstrates that the higher the profit sharing percentage the creditor get, the more willing it will be to extend maturity.

This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake pedals, and an Nvidia drive PX2 with a robot operating system (ROS). The vehicle dynamics model, sensors, controller, and test field map are virtually built with the PreScan simulation platform. Both manual and autonomous driving modes can be simulated, and a graphic user interface allows a test driver to select a target parking space on a display screen. Three scenarios are demonstrated: forward parking, reverse parking, and obstacle avoidance. When the vehicle perceives an obstacle, the map is updated and the route is adaptively planned. The effectiveness of the proposed MPC is verified in experiments and proved to be superior to a traditional proportional–integral–derivative controller with regards to safety, energy-saving, comfort, and agility.