Surface attachment of protein fibrils via covalent modification strategies.

Chemical control of surface functionality and topography is an essential requirement for many technological purposes. In particular, the covalent attachment of monomeric proteins to surfaces has been the object of intense studies in recent years, for applications as varied as electrochemistry, immuno-sensing, and the production of biocompatible coatings. Little is known, however, about the characteristics and requirements underlying surface attachment of supramolecular protein nanostructures. Amyloid fibrils formed by the self-assembly of peptide and protein molecules represent one important class of such structures. These highly organized beta-sheet-rich assemblies are a hallmark of a range of neurodegenerative disorders, including Alzheimer's disease and type II diabetes, but recent findings suggest that they have much broader significance, potentially representing the global free energy minima of the energy landscapes of proteins and having potential applications in material science. In this paper, we describe strategies for attaching amyloid fibrils formed from different proteins to gold surfaces under different solution conditions. Our methods involve the reaction of sulfur containing small molecules (cystamine and 2-iminothiolane) with the amyloid fibrils, enabling their covalent linkage to gold surfaces. We demonstrate that irreversible attachment using these approaches makes possible quantitative analysis of experiments using biosensor techniques, such as quartz crystal microbalance (QCM) assays that are revolutionizing our understanding of the mechanisms of amyloid growth and the factors that determine its kinetic behavior. Moreover, our results shed light on the nature and relative importance of covalent versus noncovalent forces acting on protein superstructures at metal surfaces.

III-V semiconductor nanowires for optoelectronic device applications

Semiconductor nanowires have recently emerged as a new class of materials with significant potential to reveal new fundamental physics and to propel new applications in quantum electronic and optoelectronic devices. Semiconductor nanowires show exceptional promise as nanostructured materials for exploring physics in reduced dimensions and in complex geometries, as well as in one-dimensional nanowire devices. They are compatible with existing semiconductor technologies and can be tailored into unique axial and radial heterostructures. In this contribution we review the recent efforts of our international collaboration which have resulted in significant advances in the growth of exceptionally high quality IIIV nanowires and nanowire heterostructures, and major developments in understanding the electronic energy landscapes of these nanowires and the dynamics of carriers in these nanowires using photoluminescence, time-resolved photoluminescence and terahertz conductivity spectroscopy. © 2011 Elsevier Ltd. All rights reserved.

Motor control optimization of compliant one-legged locomotion in rough terrain

While underactuated robotic systems are capable of energy efficient and rapid dynamic behavior, we still do not fully understand how body dynamics can be actively used for adaptive behavior in complex unstructured environment. In particular, we can expect that the robotic systems could achieve high maneuverability by flexibly storing and releasing energy through the motor control of the physical interaction between the body and the environment. This paper presents a minimalistic optimization strategy of motor control policy for underactuated legged robotic systems. Based on a reinforcement learning algorithm, we propose an optimization scheme, with which the robot can exploit passive elasticity for hopping forward while maintaining the stability of locomotion process in the environment with a series of large changes of ground surface. We show a case study of a simple one-legged robot which consists of a servomotor and a passive elastic joint. The dynamics and learning performance of the robot model are tested in simulation, and then transferred the results to the real-world robot. ©2007 IEEE.