Resistive organic memory devices based on nitrogen-doped CNTs/PSS composites

Journal of Materials Science: Materials in Electronics - Non-volatile organic memory devices were fabricated using polystyrene sulfonate (PSS)?+?nitrogen-doped multi-walled carbon...     

Reflections on the institutionalization process of scientific research in Latin America: the case of Cinvestav

Scientometrics - Consolidation of the scientific activity in Latin American research centers has been a matter of serious concern in recent years. In the present study we use a data set of the WoS...     

Gluten-free cakes with walnut flour: a technological,sensory, and microstructural approach

Walnut flour (WF), a by-product of walnut oil production, is characterised by high polyunsaturated fatty acids, proteins, and fibre contents and presents suitability for bakery products. However, when using non-traditional ingredients, it is essential to evaluate the effect on the quality properties of the final product. So, this work aimed to assess the impact of WF on the technological, physicochemical, and sensory properties of gluten-free (GF) cakes. WF was added at a flour blend (cassava (CS) and maize (MS) starches and rice flour) at 0, 10%, 15%, and 20%. The results showed that WF modified starch gelatinisation, increased amylose–lipid complex (ALC) content, and made crumbs easier to chew. Besides, the total dietary fibre (TDF) and protein content significantly increased. Cakes with 15% WF presented the highest specific volume (SV) and no differences in overall acceptability with respect to control. Hence, WF is a suitable ingredient for gluten-free bakery products.     

Facile preparation of glycine-based mesoporous graphitic carbons with embedded cobalt nanoparticles

Journal of Materials Science - A simple route has been developed for the preparation of mesoporous graphitic carbons with embedded cobalt nanoparticles just using glycine as a nitrogen source,...     

Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

In this article, an adaptive denoising method is suggested to accurate investigate the optical and structural features of polymeric fibers from noisy phase shifting microinterferograms. The mixed class of noise that may produce in the phase-shifting interferometric techniques is established. To our knowledge, this is an early study considered the mixing noises that may occur in microinterferograms. The suggested method utilized the convolution neural networks to detect the noise class and then denoising, it according to its class. Four convolution neural networks (Googlenet, VGG-19, Alexnet, and Alexnet–SVM) are refined to perform the automatic classification process for the noise class in the established data set. The network with the highest validation and testing accuracy of these networks is considered to apply the suggested method on realistic noisy microinterferograms for polymeric fibers, polypropylene and antimicrobial polyethylene terephthalate)/titanium dioxide, recoded using interference microscope. Also, the suggested method is applied on noisy microinterferograms include crazing and nanocomposite material. The demodulated phase maps and the three-dimensional birefringence profiles are calculated for tested fibers according to the suggested method. The obtained results are compared with the published data for these fibers and found to be in good agreements.     

Neutronic study on the effect of first wall material thickness on tritium production and material damage in a fusion reactor

In this study, the effects of changing first wall materials and their thicknesses on a reactor were investigated to determine the displacement per atom（DPA） and gas production（helium and hydrogen） in the first wall, as well as the tritium breeding ratio（TBR） in the coolant and tritium breeding zones. Therefore, the modeling of the magnetic fusion reactor was determined based on the blanket parameters of the International Thermonuclear Experimental Reactor（ITER）. Stainless steel（SS 316 LNIG）, Oxi...