Decoding of the isotopic fingerprint of tequila 100% agave silver class and image analysis to evaluate differences between spirits.

This communication shows the decoding of Isotopic Fingerprint of Tequila 100% agave silver class (IF) in three areas corresponding to isotopic variations due to: plant used as raw material, fermentation and distillation process, and hydrolysis process. Isotopic tracers that make them up correspond to the δC-δC-δC, δC-δC-δC and δC-δC-δC, respectively. Once the IF has been decoded, an image comparison was performed against isotopic fingerprints of spirits (Tequila, Bacanora, Raicilla, Sotol, and Mezcal).

Development of an artificial neural network (ANN) for the prediction of a pilot scale mobile wastewater treatment plant performance.

Productive activities such as pig farming are a fundamental part of the economy in Mexico. Unfortunately, because of this activity, large quantities of wastewater are generated that have a negative impact in the environment. This work shows an alternative for treating piggery wastewater based on advanced oxidation processes (Fenton and solar photo Fenton, SPF) that have been probed successfully in previous works.

Development of a sustainability-oriented university laboratory: Insight into adsorption kinetics models for the removal of pollutants from aqueous solution.

The aim of the present research is to show the development of a sustainability-oriented lab that teaches adsorption concepts in a virtual environment based on the premise "learning-through-play". Kinetic results in the virtual environment are contrasted to those obtained experimentally when diverse adsorbents prepared from Agave Bagasse (Raw Fibers, Hydrothermal Fibers, and Activated Fibers) were synthesized. Comparison between virtual and real-life experiments involving removal of methylene blue in solution showed that a pseudo-first-order model could describe adsorption kinetics satisfactorily.

Artificial intelligence application in adsorption of uremic toxins: Towards the eco-friendly design of highly efficient with potential applications as hemodialysis membranes.

Six Functionalized Activated Carbon Cloths (FACCs) were designed to obtain fundamental information for training a Bayesian Regularized Artificial Neural Network (BRANN) capable of predicting adsorption capacity of the FACCs to synthesize tailor-made materials with potential application as dialysis membranes. Characterization studies showed that FACCs have a high surface area (1354-2073 m g) associated with increased microporosity (W: 0.57 cm g).