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Contamination with polycyclic aromatic hydrocarbons (PAHs) is considered an important health issue due to the toxicity of these compounds. Photocatalytic degradation of anthracene, a representative molecule of PAHs, using the high quantum yield semiconductor ZnO, has been reported. The solubility of anthracene in water makes necessary to use mixtures with organic solvents in fundamental degradation studies. It is well known that some organic solvents participate in the photochemical transformation of this molecule. In the PAHs photocatalysis, the competition between a semiconductor and solvents has not reported. Therefore, in this work, we decided to study the photocatalytic degradation of anthracene with two common reaction media and nanostructured ZnO. The semiconductor was obtained by a one pot method which consists in an alkaline hydrolysis of Zn(CH 3 COO) 2 ⋅2H 2 O in ethanol. Nanoparticles size in colloidal dispersion was calculated using UV-Vis spectroscopy and High Resolution Transmission Electron Microcopy (HR-TEM). ZnO powder was isolated and characterized by X-ray diffraction to be used in photocatalytic experiments. Surface area determination and photocurrent spectroscopic experiments were also carried out. Linear sweep voltammetries under darkness and UV-Vis irradiation indicate a charge separation due to photoexcitation. Photocatalytic experiments in ethanol:water pH 12 (1:1) and acetone:water pH 12 (1:1), with and without ZnO was explored. The results demonstrated that ethanol:water and acetone:water promotes the photo-transformation of anthracene to 9,10-anthraquinone. Meanwhile, ZnO transformed anthracene to benzoic acid and to 9,10-anthraquinone in ethanol:water and acetone:water, respectively. A faster photochemical kinetic is observed when acetone was used as solvent in the presence and in the absence of ZnO.

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This research aims to compare, from a technical and financial perspective, the application of biological (methane-capture) and thermal (incineration) treatments of waste in Mexico City in order to generate clean energy. For each alternative, pessimist (50%), realistic (80%), and optimistic (100%) scenarios were considered in terms of the efficiency collection rates of methane and the efficiency of the capacity conversion factor for incineration. For the methane project, the LandGEM model was used to evaluate the potential generation of methane. In order to calculate the electricity output that could be generated through incineration, we relied on two key factors: the total amount of heat that could be generated by burning the waste and the average level of moisture in the waste material. The evaluation resulted in an annual energy generation of 206.09 GWh for methane and 4183.39 GWh for incineration, both in the realistic scenario. Both projects reported positive financial indicators with a discount rate of 12%. Incineration resulted in a net present value of USD 706,377,303 and an internal rate of return of 23% versus USD 4,975,369 and 24% for the methane project. However, the incineration project only became feasible by omitting financing. Incineration resulted in a payback period that was lower by a ratio of 2:1 compared to methane, but the levelized cost of energy resulted in higher figures (USD 216.92). The aim of these findings is to support the decision-making process for the creation and implementation of sustainable energy strategies based on circular economy principles in Mexico and other similar regions across the globe.

BACKGROUND: This study evaluates the effectiveness of an artificial intelligence (AI) model for predicting the best experimental conditions to reduce particle size during the synthesis of ZnO nanoparticles. Firstly, an artificial neural networks (ANN) was trained using 52 experimental data from the synthesis of ZnO nanoparticles. The selected input variables were temperature, experimental time, and NaOH concentration, and the output variable was nanoparticle size. The performance of the ANN was measured with the root mean square error and mean absolute percentage error, and the obtained values for the selected ANN were 0.67% and 9.87%, respectively. These values were calculated by using real and predicted values.RESULTS: A genetic algorithm (GA) model was coupled with the ANN to find the best operational conditions for the reduced size of ZnO nanoparticles. According to the AI model, a temperature of 59 °C, an experimental time of 56 min, and a concentration of NaOH of 0.08 should be tested to obtain ZnO nanoparticles with 5.67 nm of diameter. After applying the conditions predicted by the model, ZnO nanoparticles with a mean diameter of 5.3 ± 0.4 nm were obtained. The results were confirmed by using several characterization methods, such as approximation of effective masses (5.3 nm), equation Debye-Scherrer (5.2 nm), and high-resolution transmission electron microscope (HR-TEM) selected micrograph (5.5 nm). The photocatalytic activity of the synthesized nanoparticles was evaluated using the synthetic dye thymol blue. The best discoloration efficiency was reached by the synthesized ZnO nanoparticles at 74%, while the commercial ZnO only achieved 58%.CONCLUSION: According to the ANN-GA model, it was possible to predict the experimental conditions needed to obtain ZnO nanoparticles with reduced sizes and excellent photocatalytic activity.