Self Assembly of Polymer Structures Induced by Electric Field

A method has been developed for fabricating polymer microstructures based on electric field induced self assembly and pattern formation. A dielectric fluid placed in between two conductive plates experience a force in an applied electric field gradient across the plates, which can induce a diffusive surface instability and self construction of the fluid surface. This process is exploited for the fabrication of self assembled polymer microstructures as well as replicated patterns through the use of pre-patterned plates or electrodes. FEM simulation is used to decide the minimum wavelength and electric gradient distribution of polymer structures. A variety of structures in the micron and nanometer scales including bio-fluidic MEMS, polymer optoelectronic devices can be fabricated using this method.     

The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability

The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes.     

Investigation of bio-ethanol steam reforming over cobalt-based catalysts

The catalytic performance of cobalt catalysts supported on γ-Al₂O₃, TiO₂, ZrO₂ were studied for bio-ethanol steam reforming (BESR) reaction. The supported catalysts (10 wt%Co) were prepared by impregnation and characterized through Thermogravimetric analysis (TGA), H₂ chemisorption, laser Raman Spectroscopy, Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), and temperature-programmed reaction (TPRxn). The metallic cobalt sites were found to correlate with the BESR reaction activity. The reaction and H₂ chemisorption showed that ZrO₂ supported catalyst showed the best dispersion and best catalytic activity. Over the 10% Co/ZrO₂ catalyst, using a H₂O:EtOH:inert molar ratio of 10:1:75 and a GHSV = 5000 h⁻¹, 100% ethanol conversion and a yield of 5.5 mol H₂/mol EtOH were obtained at 550 °C and atmospheric pressure.     

Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study

This work presents an investigation of the potential of artificial neural networks for classification of registered magnetic resonance and X-ray computer tomography images of the human brain. First, topological and learning parameters are established experimentally. Second, the learning and generalization properties of the neural networks are compared to those of a classical maximum likelihood classifier and the superiority of the neural network approach is demonstrated when small training sets are utilized. Third, the generalization properties of the neural networks are utilized to develop an adaptive learning scheme able to overcome interslice intensity variations typical of MR images. This approach permits the segmentation of image volumes based on training sets selected on a single slice. Finally, the segmentation results obtained both with the artificial neural network and the maximum likelihood classifiers are compared to contours drawn manually.     

Investigation of the Reduction/Oxidation Behavior of Cobalt Supported on Nano-ceria

The oxidation and reduction behavior of cobalt catalysts supported on nano-ceria (5–8 nm) was investigated under hydrogen, oxygen and water atmospheres. A novel quantitative isothermal reduction (QIR) technique was introduced to analyze the kinetics and activation barrier of Co reduction. CoO to Co reduction was found to be of first order in the 250–350 °C temperature range. Temperature programmed reduction and oxidation experiments were conducted and the Kissinger method was used to obtain apparent activation energies for reduction and oxidation with O₂. The apparent activation energy for CoO reduction was found to agree with that obtained from the QIR technique. Re-oxidation of Co metal was found to have a slightly lower activation energy barrier than the reduction. The reduction and oxidation behavior was further investigated with in situ XANES where the reaction order for reduction was observed to change at 450 °C. The pre-reduced samples were seen to re-oxidize under a water atmosphere, where the oxidation followed first order kinetics. Re-oxidation by water yielded a higher activation energy when compared to re-oxidation under oxygen.