The rapid synthesis of nanostructured orthorhombic KNbO3 particles by a microwave-assisted hydrothermal method and their characterization

We studied the molar ratio effects of niobium and potassium precursors on the structure and morphology of potassium niobate powders prepared via microwave-assisted hydrothermal synthesis (MaHS). KNbO₃ nanostructures in the form of nanotowers and nanocubes were obtained at reduced synthesis times (30–240 min). The products were characterized via XRD, Raman spectroscopy, SEM, and TEM; band gap calculations used diffuse reflectance data. The results indicate that KNbO₃ nanostructures were obtained with crystallite sizes ranging from 33 to 52 nm. An orthorhombic crystalline structure was formed from the increase of KOH at a molar ratio Nb₂O₅:KOH (1:8 to 1:16 M). The band-gap of 3.1–3.3 eV has potential use in photodegradation applications.     

Analysis of Brazilian SHP policy and its regulation scenario

This article presents the main regulatory changes that occurred in the Brazilian power sector in 2009, along with the impacts these changes caused on the market, especially related to small hydropower (<30 MW). This study addresses regulatory issues based on inventory studies and records of basic projects, changes related to the compensation of the assured energy of SHPs in the Brazilian energy reallocation market, the socio-economic impact resulting from the construction of SHPs, SHPs in alternative resource auctions and finally the general outlook for the growth scenario for SHPs in Brazil according to the ten-year plan (2010–2019). The overall conclusions of this investigation were that the 2008/2009 biennium was a period of great changes in the regulation of small hydropower plants in Brazil, and the SHP market has shown maturity. Additionally, despite SHP being a type of technology that is completely dominated by domestic industry, in recent years, they have experienced policy disincentives caused by changes to rules that inhibit their growth.     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

3D simulation of nano/micro cellulose delivery systems for dermic and respiratory applications: Case study of eucalyptus essential oil incorporation

The purpose of this work was to design, develop and optimize different combinations of drug delivery systems (DDS) with nanofibrillated (NFC) and microfibrillated (MFC) cellulose-based materials, as 3D networks, encapsulating eucalyptus essential oil molecules, for dermic and respiratory applications. Experimental and computational approaches were implemented for characterization, modeling studies, and structure porosity optimization. The optimized porous structures were able to retain the desired molecules, resulting in more controlled and uniform release kinetics over time. This methodology allowed for optimizing new bio-based drug delivery systems with controlled porosity, pore dimension and distribution, retention of therapeutic molecules, and uniformity of the 3D network. The results showed that different drug delivery systems with this 3D matrix released the essential oil molecules, improving its stability and prolonging the exposure time, factors that are important to develop promising drug delivery systems in biomaterials fields. Overall, cellulose-based structured biomaterials capable of transporting and releasing essential oil components are representatives of innovative materials that will be used in novel applications, in which the exposure time to the therapeutic molecules constitutes a competitive benefit.     

Extracellular Vesicles in CNS Developmental Disorders

The central nervous system (CNS) is the most complex structure in the body, consisting of multiple cell types with distinct morphology and function. Development of the neuronal circuit and its function rely on a continuous crosstalk between neurons and non-neural cells. It has been widely accepted that extracellular vesicles (EVs), mainly exosomes, are effective entities responsible for intercellular CNS communication. They contain membrane and cytoplasmic proteins, lipids, non-coding RNAs, microRNAs and mRNAs. Their cargo modulates gene and protein expression in recipient cells. Several lines of evidence indicate that EVs play a role in modifying signal transduction with subsequent physiological changes in neurogenesis, gliogenesis, synaptogenesis and network circuit formation and activity, as well as synaptic pruning and myelination. Several studies demonstrate that neural and non-neural EVs play an important role in physiological and pathological neurodevelopment. The present review discusses the role of EVs in various neurodevelopmental disorders and the prospects of using EVs as disease biomarkers and therapeutics.