Calcium doping in double perovskite SmBa1-xCaxCo2O5,delta to enhance the electrochemical activity of solid oxide cell reversible oxygen electrode

Despite the remarkable reduction of carbon emissions due to COVID-19 pandemic during 2020, a reckless energy demand has compromised the 2050 agenda. In this scenario, reversible Solid Oxide Cells (rSOCs) could play a key-role due to their high fuel flexibility and versatility but there is still room for electrode stability and performance improvement. Lately, double perovskite materials have been widely studied due to their extraordinary fast oxygen diffusion rates and high conductivity mainly related to their layered ordering structure. In this work, the authors present the synthesis route and the electrochemical char-acterization of the A-site layered double perovskite structure SmBa1-xCaxCo2O5,delta in which calcium co -doping demonstrates a remarkable effect in the oxygen electrode activity. The best calcium doping corre-sponding to SmBa0.8Ca0.2Co2O5,delta displays a Rp reduction from 0.082 for the undoped material to 0.019 ohm middotcm2 at 700 degrees C. Moreover, under anodic and cathodic operating conditions the electrocatalytic ac-tivity was further increased to 0.007 and 0.006 ohm middotcm2 at eta = +/- 0.3 V, respectively. This behaviour de-monstrates the suitability of the material to work as reversible oxygen electrode. Finally, the electrode material was subjected to a switching current aging test for over 100 h to prove the electrode stability under operating conditions.(c) 2022 Elsevier B.V. All rights reserved.

Health misinformation detection in web content: A structural-, content-based, and context-aware approach based on Web2Vec

In recent years, we have witnessed the proliferation of large amounts of online content generated directly by users with virtually no form of external control, leading to the possible spread of misinformation. The search for effective solutions to this problem is still ongoing, and covers different areas of application, from opinion spam to fake news detection. A more recently investigated scenario, despite the serious risks that incurring disinformation could entail, is that of the online dissemination of health information. Early approaches in this area focused primarily on user-based studies applied to Web page content. More recently, automated approaches have been developed for both Web pages and social media content, particularly with the advent of the COVID-19 pandemic. These approaches are primarily based on handcrafted features extracted from online content in association with Machine Learning. In this scenario, we focus on Web page content, where there is still room for research to study structural-, content- and context-based features to assess the credibility of Web pages. Therefore, this work aims to study the effectiveness of such features in association with a deep learning model, starting from an embedded representation of Web pages that has been recently proposed in the context of phishing Web page detection, i.e., Web2Vec. © 2021 ACM.