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In this work, three models based on Artificial Neural Network (ANN) were developed to describe the behavior for the inhibition corrosion of bronze in 3.5% NaCl + 0.1 M Na2SO4, using the experimental data of Electrochemical Impedance Spectroscopy (EIS). The database was divided into training, validation, and test sets randomly. The parameters process used as the inputs of the ANN models were frequency, temperature, and inhibitor concentration. The outputs for each ANN model and the components in the EIS spectrum (Zre, Zim, and Zmod) were predicted. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and linear in the output layer, while the Levenberg–Marquardt algorithm was applied to determine the optimum values of the weights and biases. The statistical analysis of the results revealed that ANN models for Zre, Zim, and Zmod can successfully predict the inhibition corrosion behavior of bronze in different conditions, where what was considered included variability in temperature, frequency, and inhibitor concentration. In addition, these three input parameters were keys to describe the behavior according to a sensitivity analysis. [ABSTRACT FROM AUTHOR]

The alarming levels of carbon dioxide (CO 2 ) are an environmental problem that affects the economic growth of the world. CO 2 emissions represent penalties and restrictions due to the high carbon footprint. Therefore, sustainable strategies are required to reduce the negative impact that occurs. Among the potential systems for CO 2 capture are microalgae. These are defined as photosynthetic microorganisms that use CO 2 and sunlight to obtain oxygen (O 2 ) and generate value-added products such as biofuels, among others. Despite the advantages that microalgae may present, there are still technical-economic challenges that limit industrial-scale commercialization and the use of biomass in the production of added-value compounds. Therefore, this study reviews the current state of research on CO 2 capture with microalgae, for which bibliometric analysis was used to establish the trends of the subject in terms of scientometric parameters. Technological advances in the use of microalgal biomass were also identified. Additionally, it was possible to establish the different cooperation networks between countries, which showed interactions in the search to reduce CO 2 concentrations through microalgae.

Castilleja tenuiflora (Orobanchaceae) has been used in Mexican traditional medicine as a treatment for cough, dysentery, anxiety, nausea and vomiting as well as hepatic and gastrointestinal diseases. The ethanolic extract of the aerial parts of Castilleja tenuiflora was separated by silica gel column chromatography. The fractions were evaluated using the induced edema acetate 12-O-tetradecanoylphorbol (TPA) anti-inflammatory activity model. The most active fraction was subjected to medium-pressure liquid chromatography (MPLC) with UV detection at 206 and 240 nm. The following iridoids were isolated: geniposidic acid, aucubin, bartioside, 8-epi-loganin, mussaenoside, and the phenylpropanoid verbascoside. The most active iridoid was geniposidic acid, which was more active than the control (indomethacin), and the least active iridoid was mussaenoside. 8-epi-Loganin, and mussaenoside have not been previously reported to be anti-inflammatory compounds. The results of these investigations confirm the potential of Mexican plants for the production of bioactive compounds and validate the ethnomedical use of Castilleja tenuiflora-like anti-inflammatory plants.