An extended computational model of the circulatory system for designing ventricular assist devices.

An extended computational model of the circulatory system has been developed to predict blood flow in the presence of ventricular assist devices (VADs). A novel VAD, placed in the descending aorta, intended to offload the left ventricle (LV) and augment renal perfusion is being studied. For this application, a better understanding of the global hemodynamic response of the VAD, in essence an electrically driven pump, and the cardiovascular system is necessary. To meet this need, a model has been established as a nonlinear, lumped-parameter electrical analog, and simulated results under different states [healthy, congestive heart failure (CHF), and postinsertion of VAD] are presented. The systemic circulation is separated into five compartments and the descending aorta is composed of three components to accurately yield the system response of each section before and after the insertion of the VAD. Delays in valve closing time and blood inertia in the aorta were introduced to deliver a more realistic model. Pump governing equations and optimization are based on fundamental theories of turbomachines and can serve as a practical initial design point for rotary blood pumps. The model's results closely mimic established parameters for the circulatory system and confirm the feasibility of the intra-aortic VAD concept. This computational model can be linked with models of the pump motor to provide a valuable tool for innovative VAD design.

Nonvolatile memory functionality of ZnO nanowire transistors controlled by mobile protons.

We demonstrated the nonvolatile memory functionality of ZnO nanowire field effect transistors (FETs) using mobile protons that are generated by high-pressure hydrogen annealing (HPHA) at relatively low temperature (400 °C). These ZnO nanowire devices exhibited reproducible hysteresis, reversible switching, and nonvolatile memory behaviors in comparison with those of the conventional FET devices. We show that the memory characteristics are attributed to the movement of protons between the Si/SiO(2) interface and the SiO(2)/ZnO nanowire interface by the applied gate electric field. The memory mechanism is explained in terms of the tuning of interface properties, such as effective electric field, surface charge density, and surface barrier potential due to the movement of protons in the SiO(2) layer, consistent with the UV photoresponse characteristics of nanowire memory devices. Our study will further provide a useful route of creating memory functionality and incorporating proton-based storage elements onto a modified CMOS platform for FET memory devices using nanomaterials.

First-principles prediction of doped graphane as a high-temperature electron-phonon superconductor.

We predict by first-principles calculations that p-doped graphane is an electron-phonon superconductor with a critical temperature above the boiling point of liquid nitrogen. The unique strength of the chemical bonds between carbon atoms and the large density of electronic states at the Fermi energy arising from the reduced dimensionality give rise to a giant Kohn anomaly in the optical phonon dispersions and push the superconducting critical temperature above 90 K. As evidence of graphane was recently reported, and doping of related materials such as graphene, diamond, and carbon nanostructures is well established, superconducting graphane may be feasible.