Atopic dermatitis is one of the most widespread chronic inflammatory skin conditions that can occur at any age, though the prevalence is highest in children. The purpose of the current study was to prepare and optimize the azelaic acid (AzA) loaded SNEDDS using Pseudo ternary phase diagram, which was subsequently incorporated into the Carbopol 940 hydrogel for the treatment of atopic dermatitis. The composition was evaluated for size, entrapment efficiency, in vitro, ex vivo, and in vivo studies. The polydispersity index of the optimized preparation was found to be less than 0.5, and the size of the distributed globules was found to be 151.20 ± 3.67 nm. The SNEDDS hydrogel was characterized for pH, viscosity, spreadability, and texture analysis. When compared to the marketed formulation, SNEDDS hydrogel was found to have a higher rate of permeation through the rat skin. In addition, a skin irritation test carried out on experimental animals showed that the SNEDDS formulation did not exhibit any erythematous symptoms after a 24-h exposure. In conclusion, the topical delivery of AzA through the skin using SNEDDS hydrogel could prove to be an effective approach for the treatment of atopic dermatitis.
In this work, silver (Ag) decorated reduced graphene oxide (rGO) coated with ultrafine CuO nanosheets (Ag-rGO@CuO) was prepared by the combination of a microwave-assisted hydrothermal route and a chemical methodology. The prepared Ag-rGO@CuO was characterized for its morphological features by field emission scanning electron microscopy and transmission electron microscopy while the structural characterization was performed by X-ray diffraction and Raman spectroscopy. Energy-dispersive X-ray analysis was undertaken to confirm the elemental composition. The electrochemical performance of prepared samples was studied by cyclic voltammetry and galvanostatic charge-discharge in a 2M KOH electrolyte solution. The CuO nanosheets provided excellent electrical conductivity and the rGO sheets provided a large surface area with good mesoporosity that increases electron and ion mobility during the redox process. Furthermore, the highly conductive Ag nanoparticles upon the rGO@CuO surface further enhanced electrochemical performance by providing extra channels for charge conduction. The ternary Ag-rGO@CuO nanocomposite shows a very high specific capacitance of 612.5 to 210 Fg-1 compared against rGO@CuO which has a specific capacitance of 375 to 87.5 Fg-1 and the CuO nanosheets with a specific capacitance of 113.75 to 87.5 Fg-1 at current densities 0.5 and 7 Ag-1, respectively.
In the last three decades, smart materials have become popular. The piezoelectric materials have shown key characteristics for engineering applications, such as in sensors and actuators for industrial use. Because of their excellent mechanical-to-electrical and vice versa energy conversion properties, piezoelectric materials with high piezoelectric charge and voltage coefficient have been tested in renewable energy applications. The fundamental component of the energy harvester is the piezoelectric material, which, when subjected to mechanical vibrations or applied stress, induces the displaced ions in the material and results in a net electric charge due to the dipole moment of the unit cell. This phenomenon builds an electric potential across the material. In this review article, a detailed study focused on the piezoelectric energy harvesters (PEH's) is reported. In addition, the fundamental idea about piezoelectric materials, along with their modeling for various applications, are detailed systematically. Then a summary of previous studies based on PEH's other applications is listed, considering the technical aspects and methodologies. A discussion has been provided as a critical review of current challenges in this field. As a result, this review can provide a guideline for the scholars who want to use PEH's for their research.
Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA's mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.
Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .