Flexible poly(styrene-ethylene-butadiene-styrene) hybrid nanofibers for bioengineering and water filtration applications

Among the thermoplastic elastomers that play important roles in the polymer industry due to their superior properties, styrene-based species and polyurethane block copolymers are of great interest. Poly(styrene-ethylene-butadiene-styrene) (SEBS) as a triblock copolymer seems to have the potential to meet many demands in different applications due to various industrial requirements where durability, biocompatibility, breaking elongation, and interfacial adhesion are important. In this study, the SEBS triblock copolymer was functionalized with natural (Satureja hortensis, SH) and synthetic (nanopowder, TiO₂) agents to obtain composite nanofibers by electrospinning and electrospraying methods for use in biomedical and water filtration applications. The results were compared with thermoplastic polyurethane (TPU) composite nanofibers, which are commonly used in these fields. Here, functionalized SEBS nanofibers exhibited antibacterial effect while at the same time improving cell viability. In addition, because of successful water filtration by using the SEBS composite nanofibers, the material may have a good potential to be used comparably to TPU for the application.     

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.     

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

     

Mechanical and viscoelastic properties of polyethylene-based microfibrillated composites from 100% recycled resources

In this study, recycled polyethylene (rPE) based microfibrillated composites (MFCs) were developed while incorporating recycled poly(ethylene terephthalate) (rPET) and recycled polyamide 6 (rPA) as the reinforcing fibrillar phases at a given weight ratio of 80 wt% (rPE)/20 wt% (rPET or rPA). The blends were first melt processed using a twin-screw extruder. The extrudates were then cold stretched at a drawing ratio of 2.5 to form rPET and rPA fibrillar structures. Next, the pelletized drawn samples were injection molded at the barrel temperatures below the melting temperatures of rPET and rPA. The tensile, three-point bending, impact strength, dynamic thermomechanical, and rheological properties of the fabricated MFCs were analyzed. The effects of injection molding barrel temperature (i.e., 150°C and 190°C) and extrusion melt processing temperature (i.e., 250°C and 275°C) on the generated fibrillar structure and the resultant properties were explored. A strong correlation between the fibrillar morphology and the mechanical properties with the extrusion and injection molding temperatures was observed. Moreover, the ethylene/n-butyl acrylate/glycidyl methacrylate (EnBAGMA) terpolymer and maleic anhydride grafted PE (MAH-g-PE) were, respectively, melt processed with rPE/rPET and rPE/rPA6 blends as compatibilizers. The compatibilizers refined the fibrillar structure and remarkably influenced mechanical properties, specifically the impact strength.     

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.