Alain Ricardo Picos Benítez

Driven by the need to develop innovative solutions for wastewater treatment and to reduce waste generation, this study proposes the valorization of agricultural residues, specifically peanut shells, as a precursor for the synthesis of surface-area activated carbon (AC) through chemical activation with KOH (ratio 1:3). AC derived from peanut shells showed a microporous structure with a surface area of 1171 m²/g and a highly heterogeneous surface. Physicochemical analysis confirmed that the presence of oxygen-containing functional groups may contribute to contaminant adsorption. Equilibrium data were best fitted by the Sips isotherm model (R² = 0.997), indicating surface heterogeneity and yielding an estimated adsorption capacity (q_s) of 67.83 mg/g for methyl orange (MO). Kinetic analysis indicated rapid adsorption during the initial stage, favored by the porous structure of the AC, followed by a slower stage associated with mass transfer within the material’s internal pores. The adsorption kinetics were best described by the pseudo-second-order model (R² = 0.992, q_e = 25.78 mg/g). Overall, the results establish a relationship between the porous structure of the adsorbent and its adsorption behavior, demonstrating the potential of peanut shell valorization for the development of sustainable and efficient adsorbent materials for wastewater treatment within a circular economy framework.

This study focused on the generation and characterization of sludge produced by electrocoagulation (EC) combined with Moringa oleifera seed extract (MOSE) to remove water hardness. First, an experimental data set was generated and used as the baseline data for mathematical modeling to identify the effects of different parameters on Ca²⁺ and Mg²⁺ ion hardness removal. Then, using the generated data set, operational conditions were optimized using neural networks integrated with a genetic algorithm, resulting in the selection of Fe electrodes, 12.5 mL of MOSE per 100 mL of water, a current density (j) of 49.16 mA cm⁻², and a reaction time of 5.3 min, considering Ca²⁺ ions as the sample contaminant. Additionally, machine learning analysis identified contaminant type, reaction time, and cathode material as the most influential variables affecting sludge formation, with optimal conditions identified for both Ca²⁺ and Mg²⁺ ion systems. For all the mathematical models, experimental validation was performed. The MOSE extract was characterized for the presence of proteins, polyphenols, flavonoids, and polysaccharides, which provide functional groups that promote aggregation and floc development. Sludge characterization by FT-IR, TGA, and TEM revealed the formation of organic–inorganic hybrid matrices composed of biomolecules interacting with electrochemically generated Fe³⁺ and Al³⁺ species, as well as Ca²⁺ and Mg²⁺ ions. These results highlight the role of plant-derived biomolecules in modulating the sludge structure and composition, providing insight into the mechanisms of sludge formation and the implications for handling and valorization of EC-based water treatment systems.

Functionalized carbon-based materials have recently attracted attention for the efficient removal of complex pollutants, including dyes. In this study, agave bagasse, an abundant by-product of the Mexican tequila industry, was used as biomass to produce H₂SO₄-modified hydrochar (HC) for the removal of Reactive Orange 84 (RO84). FTIR-ATR analysis revealed characteristic signals of –SO₃H groups in the functionalized HC. BET characterization showed a broad range of surface areas and pore volumes, with pore radio indicating nano-, micro-, and mesoporous structures, depending on the acid concentration used during synthesis. TGA and XRD analyses indicated that higher acid concentration promoted the depolymerization of biomass components. Adsorption assays demonstrated that increasing H₂SO₄ concentrations enhanced dye removal. Post-adsorption FTIR-ATR analysis revealed signal shifts consistent with interactions between sulfonated groups and dye RO84. Statistical and mathematical analyses showed that optimal results were achieved by combining high acid concentrations during HC synthesis with high HC dosages during adsorption.

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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. © 2023 Society of Chemical Industry (SCI).

In this study, electro-oxidation (EOx) and in situ generation of ferrate ions [Fe(VI)] were tested to treat water contaminated with Blue BR dye (BBR) using a boron-doped diamond (BDD) anode. Two electrolytic media (0.1 M HClO₄ and 0.05 M Na₂SO₄) were evaluated for the BDD, which simultaneously produced oxygen radicals (^•OH) and [Fe(VI)]. The generation of [Fe(VI)] was characterized by cyclic voltammetry (CV) and the effect of different current intensity values (e.g., 7 mA cm⁻², 15 mA cm⁻², and 30 mA cm⁻²) was assessed during BBR degradation tests. The discoloration of BBR was followed by UV-Vis spectrophotometry. When the EOx process was used alone, only 78% BBR discoloration was achieved. The best electrochemical discoloration conditions were found using 0.05 M Na₂SO₄ and 30 mA cm⁻². Using these conditions, overall BBR discoloration values up to 98%, 95%, and 87% with 12 mM, 6 mM, and 1 mM of FeSO₄, respectively, were achieved. In the case of chemical oxygen demand (COD) reduction, the EOx process showed only a 37% COD reduction, whereas combining [Fe(VI)] generation using 12 mM of FeSO₄ achieved an up to 61% COD reduction after 90 min. The evolution of reaction byproducts (oxalic acid) was performed using liquid chromatography analysis.