The Staged Photocatalytic Reactor in the Removal of Acetaminophen: Aspects of Adsorption and Photocatalysis.

The efficiency of a staged photocatalytic reactor prototype was evaluated on a semi-pilot scale with the removal of acetaminophen, for which anatase particles were synthesized by Sol-Gel and impregnated on rectangular plates of clay. X-ray diffraction and Energy Dispersive X-ray Fluorescence patterns show that the final composite is made up of Al2O3 (14 %), SiO2 (41 %), CaO (3 %) TiO2 (34 %), and Fe2O3 (7 %). The impregnation method favors the dispersion of Anatase on the surface of the adsorbent. TiO2-Anatase/Clay, classified as a macro-porous solid with H3-type hysteresis loops by N2 physisorption. Adsorption processes are improved when using TiO2-Anatase/Clay compared to using TiO2-Anatase. The external mass transfer has a greater influence on the removal rate. The dimensionless parameters of the Biot number indicate there are no limitations due to the diffusive effect on the interior of the particle. The evaluation of the kinetic data under the Langmuir-Hinshelwood equation shows a decrease in efficiency as the initial concentration increases. The acetaminophen molecule shows destabilization in the structure of the aromatic ring with a visible decrease in the signals of this functional group evaluated by High-Performance Liquid Chromatography and Raman Spectroscopy.

Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste.

The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe health issues and ecosystem damage; education plays a crucial role in mitigating these effects by enhancing awareness, promoting sustainable practices, and integrating environmental science into curricula to empower individuals to address and advocate for effective solutions to water pollution. However, the educational transformation should be accompanied with a technical process which can be eventually transferred to society to empower environmental education. In this study, carbonaceous material derived from Haematoxylum campechianum (CM-HC) was utilized for removing 3-nitrophenol (3-Nph) from aqueous solutions. The novelty of this research utilizes Haematoxylum campechianum bark and coconut shell, abundant agricultural wastes in Campeche, Mexico, for toxin removal, enhancing the adsorption process through artificial neural networks and genetic algorithms to optimize conditions and maximize the absorption efficiency. CM-HC's surface morphology was analyzed using scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and pHpzc. Kinetic models including pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich were applied to fit the data. Adsorption isotherms were determined at varying pH (3-8), adsorbent dosages (2-10 g/L), and temperatures (300.15-330.15 K), employing Langmuir, Freundlich, Temkin, and Redlich-Peterson models. PSO kinetics demonstrated a good fit (R2 > 0.98) for Ci = 50-100 mg/L, indicating a chemical adsorption mechanism. The Langmuir isotherm model exhibited the best fit, confirming chemical adsorption, with a maximum adsorption capacity (Qm) of 236.156 mg/g at T = 300.15 K, pH = 6, contact time = 3 h, and 2 g/L adsorbent dosage. Lower temperatures favored exothermic adsorption. Artificial neural networks (ANNs) were employed for deep learning, optimizing the predictive model for removal percentage. Correlation heat maps highlighted positive correlations between time, dosage, and removal percentage, emphasizing the impact of initial concentration on efficiency. ANN modeling, incorporating iterative optimization, yielded highly accurate predictions, aligned closely with experimental results. The study showcases the success of deep learning in optimizing adsorption processes, emphasizing the importance of diverse correlation algorithms for comprehensive insights into competitive adsorption dynamics. The 5-14-14-1 deep learning architecture, fine-tuned over 228 epochs, demonstrated strong performance with mean squared error (MSE) values of 4.07, 18.406, and 6.2122 for training, testing, and total datasets, respectively, and high R-squared values. Graphical analysis showed a solid linear correlation between experimental and simulated removal percentages, emphasizing the need to consider more than just testing data for optimization. Experimental validation confirmed a 98.77% removal efficiency, illustrating the effectiveness of combining deep learning with genetic algorithms, and highlighting the necessity of experimental trials to verify computational predictions. It is concluded that the carbonaceous material from Haematoxylum campechianum waste (CM-HC) is an effective, low-cost adsorbent for removing 3-nitrophenol from aqueous solutions, achieving optimal removal at pH 6 and 300.15 K with a maximum adsorption capacity of 236.156 mg/g, following Langmuir model and pseudo-second order kinetics. The validated ANN model offers a reliable tool for practical applications in environmental remediation, advancing both environmental science and educational innovation by integrating artificial neural networks and data science methodologies into student learning experiences.

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.

This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from Haematoxylum campechianum waste (ABHC). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar. The surface morphology of the adsorbent (before and after dye adsorption) was characterized by scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) and, lastly, pHpzc was also determined. Batch studies were carried out in the following intervals of pH = 4-10, temperature = 300.15-330.15 K, the dose of adsorbent = 1-10 g/L, and isotherms evaluated the adsorption process to determine the maximum adsorption capacity (Qmax, mg/g). Kinetic studies were performed starting from two different initial concentrations (25 and 50 mg/L) and at a maximum contact time of 48 h. The reusability potential of activated biochar was evaluated by adsorption-desorption cycles. The maximum adsorption capacity obtained with the Langmuir adsorption isotherm model was 114.8 mg/g at 300.15 K, pH = 5.4, and a dose of activated biochar of 1.0 g/L. This study also highlights the application of advanced machine learning techniques to optimize a chemical removal process. Leveraging a comprehensive dataset, a Gradient Boosting regression model was developed and fine-tuned using Bayesian optimization within a Python programming environment. The optimization algorithm efficiently navigated the input space to maximize the removal percentage, resulting in a predicted efficiency of approximately 90.47% under optimal conditions. These findings offer promising insights for enhancing efficiency in similar removal processes, showcasing the potential of machine learning in process optimization and environmental remediation.

Determination of the Concentration of Heavy Metals in Artisanal Cheeses Produced in the Mexican States of Tabasco and Chiapas.

Cheese consumption provides humans with minerals, proteins, carbohydrates, and vitamins. In Mexico, several cheese varieties are produced, each with its texture, scent, and flavor. The artisanal cheeses made in the states of Tabasco and Chiapas-including, among others, the varieties named crema (cream), doble crema (double cream), oaxaca, panela, fresco, bola, poro, cotija, and asadero-have a high demand in the domestic and foreign markets. The intensification of anthropic activity in these states causes an increased emission to the environment of contaminants like heavy metals, which could reach human foodstuffs through the food chains. In particular, heavy metal contents in cheeses consumed daily by these states' local populations might represent a public health risk. Because of that, our objectives in this work were to determine the concentrations of lead, cadmium, nickel, copper, zinc, and iron in artisanal cheeses produced in the states of Tabasco and Chiapas and to determine the values of the hazard quotient (HQ), total hazard quotient (THQ), and cancer risk total (CRT) for adult and young men and women. The results of our analyses of cheese samples from the states of Tabasco and Chiapas showed that the average concentrations (mg kg-1) of cadmium (0.0023 ± 0.002, 0.0023 ± 0.002 mg kg-1, respectively, for each state), lead (0.0047 ± 0.00, 0.0051 ± 0.002), nickel (0.0039 ± 0.0046, 0.0031 ± 0.0039), copper (0.0199 ± 0.021, 0.0202 ± 0.022), zinc (0.1611 ± 0.18, 0.194 ± 0.21), and iron (61.84 ± 4.23, 65.76 ± 6.61 mg kg-1), the first three values lower than the limits established by the FAO/WHO and Codex Alimentarius. The value of THQ that we obtained was less than one, and that of CRT was within the limits established by the US-EPA, which means that the consumption of artisanal cheeses from Tabasco and Chiapas by humans does not imply a risk of disease or cancer.

Biosorption of Pb(II) Using Natural and Treated Ardisia compressa K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm.

This study explored the effects of solution pH, biosorbent dose, contact time, and temperature on the Pb(II) biosorption process of natural and chemically treated leaves of A. compressa K. (Raw-AC and AC-OH, respectively). The results show that the surface characteristics of Raw-AC changed following alkali treatment. FT-IR analysis showed the presence of various functional groups on the surface of the biosorbent, which were binding sites for the Pb(II) biosorption. The nonlinear pseudo-second-order kinetic model was found to be the best fitted to the experimental kinetic data. Adsorption equilibrium data at pH = 2-6, biosorbents dose from 5 to 20 mg/L, and temperature from 300.15 to 333.15 K were adjusted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherm models. The results show that the adsorption capacity was enhanced with the increase in the solution pH and diminished with the increase in the temperature and biosorbent dose. It was also found that AC-OH is more effective than Raw-AC in removing Pb(II) from aqueous solutions. This was also confirmed using artificial neural networks and genetic algorithms, where it was demonstrated that the improvement was around 57.7%. The nonlinear Langmuir isotherm model was the best fitted, and the maximum adsorption capacities of Raw-AC and AC-OH were 96 mg/g and 170 mg/g, respectively. The removal efficiency of Pb(II) was maintained approximately after three adsorption and desorption cycles using 0.5 M HCl as an eluent. This research delved into the impact of solution pH, biosorbent characteristics, and operational parameters on Pb(II) biosorption, offering valuable insights for engineering education by illustrating the practical application of fundamental chemical and kinetic principles to enhance the design and optimization of sustainable water treatment systems.

Synthesis and Characterization of Thiol-Functionalized Polynorbornene Dicarboximides for Heavy Metal Adsorption from Aqueous Solution.

The contamination of water resources with heavy metals is a very serious concern that demands prompt and effective attention due to the serious health risks caused by these contaminants. The synthesis and ring-opening metathesis polymerization (ROMP) of norbornene dicarboximides bearing thiol pendant groups, specifically, N-4-thiophenyl-exo-norbornene-5,6-dicarboximide (1a), N-4-(methylthio)phenyl-exo-norbornene-5,6-dicarboximide (1b) and N-4-(trifluoromethylthio)phenyl-exo-norbornene-5,6-dicarboximide (1c), as well as their assessment for the removal of heavy metals from aqueous systems, is addressed in this work. The polymers were characterized by NMR, SEM and TGA, among others. Single and multicomponent aqueous solutions of Pb2+, Cd2+ and Ni2+ were employed to perform both kinetic and isothermal adsorption studies taking into account several experimental parameters, for instance, the initial metal concentration, the contact time and the mass of the polymer. In general, the adsorption kinetic data fit the pseudo-second-order model more efficiently, while the adsorption isotherms fit the Freundlich and Langmuir models. The maximum metal uptakes were 53.7 mg/g for Pb2+, 43.8 mg/g for Cd2+ and 29.1 mg/g for Ni2+ in the SH-bearing polymer 2a, 46.4 mg/g for Pb2+, 32.9 mg/g for Cd2+ and 27.1 mg/g for Ni2+ in the SCH3-bearing polymer 2b and 40.3 mg/g for Pb2+, 35.9 mg/g for Cd2+ and 27.8 mg/g for Ni2+ in the SCF3-bearing polymer 2c, correspondingly. The better performance of polymer 2a for the metal uptake was ascribed to the lower steric hindrance and higher hydrophilicity imparted by -SH groups to the polymer. The results show that these thiol-functionalized polymers are effective adsorbents of heavy metal ions from aqueous media.

Composite of methyl polysiloxane and avocado biochar as adsorbent for removal of ciprofloxacin from waters.

Two carbon composite materials were prepared by mixing avocado biochar and methyl polysiloxane (MK). Firstly, MK was dissolved in ethanol, and then the biochar was added at different times. In sample 1 (R1), the time of adding biochar was immediately after dissolving MK in ethanol, and in sample 2 (R2), after 48 h of MK dissolved in ethanol. The samples were characterized by nitrogen adsorption/desorption measurements obtaining specific surface areas (SBET) of 115 m2 g-1 (R1) and 580 m2 g-1 (R2). The adsorbents were further characterized using scanning electron microscopy, FTIR and Raman spectroscopy, adsorption of vapors of n-heptane and water, thermal analysis, Bohem titration, pHpzc, and C H N elemental analysis. R1 and R2 adsorbents were employed as adsorbents to remove the antibiotic ciprofloxacin from the waters. The t1/2 and t0.95 based on the interpolation of Avrami fractional-order were 20.52 and 246.4 min (R1) and 14.00 and 157.6 min (R2), respectively. Maximum adsorption capacities (Qmax) based on the Liu isotherm were 10.77 (R1) and 63.80 mg g-1 (R2) for ciprofloxacin. The thermodynamic studies showed a spontaneous and exothermic process for both samples, and the value of ΔH° is compatible with physical adsorption.

Synthesis, characterization, and assessment of novel sulfonated polynorbornene dicarboximides as adsorbents for the removal of heavy metals from water.

The occurrence of heavy metals in the natural aquatic systems arising from anthropogenic sources is an issue of global and environmental concern because of their extremely harmful effects to living beings even in rather low concentrations. The synthesis and ring-opening metathesis polymerization (ROMP) of novel norbornene dicarboximides bearing highly aromatic pendant groups, specifically, N-4-tritylphenyl-norbornene-5,6-dicarboximide (2a) and N-2,4,6-(triphenyl)phenyl-norbornene-5,6-dicarboximide (2b), their hydrogenation and further polymer sulfonation to render them adsorbents for the uptake of heavy metal ions from water is reported in this study. The macromolecules were characterized by means of FT-IR, 1H NMR, and thermal analysis, among others. A thoroughly kinetic and isothermal study of adsorption in single and ternary aqueous solutions of Pb2+, Cd2+, and Ni2+ was performed considering several experimental variables for instance initial metal concentration, contact time and solution pH. In general, the experimental data were adjusted more efficiently to the pseudo-second order kinetic model and to the Freundlich isotherm model, respectively. The maximum removal amounts were found to be 55.7 mg/g for Pb2+, 33.9 mg/g for Cd2+, and 10.2 mg/g for Ni2+ in the sulfonated trityl-bearing polymer 5a while those found for the sulfonated triphenyl-bearing polymer 5b were 31.5 mg/g for Pb2+, 26.6 mg/g for Cd2+, and 7.0 mg/g for Ni2+, respectively. The higher heavy metal removal capacity of polymer 5a was attributed to its also higher degree of sulfonation. The outcomes indicate that these novel sulfonic acid containing polymer-based adsorbents are effective for the uptake of heavy metallic elements from water.

Eco-Efficient Biosorbent Based on Leucaena leucocephala Residues for the Simultaneous Removal of Pb(II) and Cd(II) Ions from Water System: Sorption and Mechanism.

Leucaena leucocephala is a potential source of polyphenols widely available in southern Mexico. This work highlights the extraction of polyphenols from Leucaena leucocephala leaves waste (LLEPs) and the evaluation of their efficiency to remove the single and multicomponent Pb(II) and Cd(II) metal ions from aqueous solutions. Batch test conditions were carried out to examine the effects of contact time, initial metal ion concentration, and adsorbent dosage on the biosorption process. The surface textures and the composition of the LLEP biosorbent was characterized using pH of point of zero charge (pHPZC), attenuated total reflectance Fourier transform infrared (ATR-FTIR), and matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry, respectively. Further analysis using ATR-FTIR after adsorption contact of biosorbent was also investigated. The highest Langmuir saturation monolayer adsorption capacity, q m, for the removal of Pb(II) by LLEPs was obtained as 25.51 and 21.55 mg/g in mono- and bimetal solutions, respectively. The pseudo-second-order model provided the best fit for the kinetic data obtained for the removal of Pb(II), Cd(II), and their mixture, and the k2 values depend on the adsorbent mass. This implied that the chemisorption process might be the mechanism of the solute ions-LLEPs interaction in this study. Furthermore, nearly 100% removal of lead and cadmium individually and 95% of their mixture was found using 0.9 g of LLEPs.

Application of Multivariable Analysis and FTIR-ATR Spectroscopy to the Prediction of Properties in Campeche Honey.

Attenuated total reflectance-Fourier transform infrared spectrometry and chemometrics model was used for determination of physicochemical properties (pH, redox potential, free acidity, electrical conductivity, moisture, total soluble solids (TSS), ash, and HMF) in honey samples. The reference values of 189 honey samples of different botanical origin were determined using Association Official Analytical Chemists, (AOAC), 1990; Codex Alimentarius, 2001, International Honey Commission, 2002, methods. Multivariate calibration models were built using partial least squares (PLS) for the measurands studied. The developed models were validated using cross-validation and external validation; several statistical parameters were obtained to determine the robustness of the calibration models: (PCs) optimum number of components principal, (SECV) standard error of cross-validation, (R2cal) coefficient of determination of cross-validation, (SEP) standard error of validation, and (R2val) coefficient of determination for external validation and coefficient of variation (CV). The prediction accuracy for pH, redox potential, electrical conductivity, moisture, TSS, and ash was good, while for free acidity and HMF it was poor. The results demonstrate that attenuated total reflectance-Fourier transform infrared spectrometry is a valuable, rapid, and nondestructive tool for the quantification of physicochemical properties of honey.