Effect of ZnO layer thickness upon optoelectrical properties of NiO/ ZnO heterojunction prepared at room temperature

In this work, p-NiO/n-ZnO heterostructures were successfully prepared at room temperature using RF sputtering technique. The influence of ZnO layer thickness on the performance of the heterojunction was investigated. The deposited ZnO layers have a hexagonal Wurtzite structure with preferable growth orientations along (002) and (103) for thinner films. Increasing the thickness results in more crystallographic orientation randomness. The current–voltage measurements of the realized heterojunctions showed a clear rectifying behavior. The measured ideality factor varies from 2.5 to 1.6 according to the thickness of ZnO layer. The series resistance of the device is enlarged with increasing ZnO thickness. The deduced parameters from the I–V characteristics suggest that 200 nm is the optimal thickness of the ZnO layer according to our experimental conditions. We attribute the relatively better performance of this thickness to achieving reasonable compensation between serial resistance and ideality factor. The best heterojunction was tested and successfully used as a UV detector.     

Vicious cycle during chemical degradation of sulfonated aromatic proton exchange membranes in the fuel cell application

Weak phase separation and vulnerable linking groups between aromatic units are common setbacks of sulfonated aromatic proton exchange membranes (PEMs) from durability point of view. In this study, sulfonated poly(ether ether ketone) (SPEEK) membranes were exposed to Fenton's solution for a specific time, ranging from 10 to 60 minutes. Chemical structure and morphology evolution, decay in mechanical and thermal stability, and H₂ permeability of SPEEK membranes were evaluated during the chemical degradation. Less-entangled polymeric chains with lower average molecular weight of degraded SPEEK samples diminished mechanical rigidity. In addition, reduction of aromatic rings in each repeat unit led to higher thermal decomposition rate. Furthermore, randomly distributed micro-defects in the SPEEK morphology and an increase in water sorption can reduce the fatigue strength of membranes in the wet-dry cycles. Eventually, hydrogen cross-over rate was gradually increased, and henceforth, accelerated destructive radical formation and degradation can be predicted.     

GutSelf: Interindividual Variability in the Processing of Dietary Compounds by the Human Gastrointestinal Tract

Nutritional research is currently entering the field of personalized nutrition, to a large extent driven by major technological breakthroughs in analytical sciences and biocomputing. An efficient launching of the personalized approach depends on the ability of researchers to comprehensively monitor and characterize interindividual variability in the activity of the human gastrointestinal tract. This information is currently not available in such a form. This review therefore aims at identifying and discussing published data, providing evidence on interindividual variability in the processing of the major nutrients, i.e., protein, fat, carbohydrates, vitamins, and minerals, along the gastrointestinal tract, including oral processing, intestinal digestion, and absorption. Although interindividual variability is not a primary endpoint of most studies identified, a significant number of publications provides a wealth of information on this topic for each category of nutrients. This knowledge remains fragmented, however, and understanding the clinical relevance of most of the interindividual responses to food ingestion described in this review remains unclear. In that regard, this review has identified a gap and sets the base for future research addressing the issue of the interindividual variability in the response of the human organism to the ingestion of foods.     

Ceramics based on calcium phosphates substituted with magnesium ions for bone regeneration

In this study, chemical precipitation methods were used to obtain ceramic materials doped with magnesium ions in order to improve the regeneration properties of materials used for tissue engineering. Two different ratios of magnesium oxide were used to dope the ceramic powder, more precisely 5% and 10%. The synthesized materials were characterized to determine the calcination temperature of the precursor powder by means of thermal analysis; to determine the mineralogical composition, X-ray diffraction was employed and the scanning electron microscopy was used to determine the microstructure. To make use of these ceramics as biomaterials, viability and proliferation cell tests have been performed. Since synthetic materials have several limitations with regard to medical applications, the materials based on HAp substituted with Mg ions are a promising solution for the regeneration of bone defects because they have a similar bone structure. The presence of Mg in the material proves to be beneficial because this element plays an important role in bone cell regeneration, and more specifically, in stimulating osteoblast proliferation. The materials synthesized in this work present a suitable morphology for uses in bone regeneration because they offer to cells a friendly environment for growth and anchoring.     

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.