CdS nanocrystallites sensitized ZnO nanosheets for visible light induced sonophotocatalytic/photocatalytic degradation of tetracycline: From experimental results to a generalized model based on machine learning methods

Machine learning model to predict rate constants for sonochemical degradation of organic pollutants

In this study, machine learning (ML) algorithms were employed to predict the pseudo-1st-order reaction rate constants for the sonochemical degradation of aqueous organic pollutants under various conditions. A total of 618 sets of data, including ultrasonic, solution, and pollutant characteristics, were collected from 89 previous studies. Considering the difference between the electrical power (Pele) and calorimetric power (Pcal), the collected data were divided into two groups: data with Pele and data with Pcal. Eight input variables, including frequency, power density, pH, temperature, initial concentration, solubility, vapor pressure, and octanol-water partition coefficient (Kow), and one target variable of the degradation rate constant, were selected for ML. Statistical analysis was conducted, and outliers were determined separately for the two groups. ML models, including random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were used to predict the pseudo-1st-order reaction rate constants for the removal of aqueous pollutants. The prediction performance of the ML models was evaluated using different metrics, including the root mean squared error (RMSE), mean absolute error (MAE), and R squared (R2). A significantly higher prediction performance was obtained using data without outliers and augmented data. Consequently, all the applied ML models could be used to predict the sonochemical degradation of aqueous pollutants, and the XGB model showed the highest accuracy in predicting the rate constants. In addition, the power density and frequency were the most influential factors among the eight input variables in prediction with the Shapley additive explanation (SHAP) values method. The degradation rate constants of the two pollutants over a wide frequency range (20-1,000 kHz) were predicted using the trained ML model (XGB) and the prediction results were analyzed.

Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots

To predict the behavior of aromatic contaminants (ACs) in complex soil-plant systems, this study developed machine learning (ML) models to estimate the root concentration factor (RCF) of both traditional (e.g., polycyclic aromatic hydrocarbons, polychlorinated biphenyls) and emerging ACs (e.g., phthalate acid esters, aryl organophosphate esters). Four ML algorithms were employed, trained on a unified RCF dataset comprising 878 data points, covering 6 features of soil-plant cultivation systems and 98 molecular descriptors of 55 chemicals, including 29 emerging ACs. The gradient-boosted regression tree (GBRT) model demonstrated strong predictive performance, with a coefficient of determination (R2) of 0.75, a mean absolute error (MAE) of 0.11, and a root mean square error (RMSE) of 0.22, as validated by five-fold cross-validation. Multiple explanatory analyses highlighted the significance of soil organic matter (SOM), plant protein and lipid content, exposure time, and molecular descriptors related to electronegativity distribution pattern (GATS8e) and double-ring structure (fr_bicyclic). An increase in SOM was found to decrease the overall RCF, while other variables showed strong correlations within specific ranges. This GBRT model provides an important tool for assessing the environmental behaviors of ACs in soil-plant systems, thereby supporting further investigations into their ecological and human exposure risks.

Solvent-thermal approach of MIL-100(Fe)/Cygnea/Fe3O4/TiO2 nanocomposite for the treatment of lead from oil refinery wastewater (ORW) under UVA light

The main purpose of this research endeavor is to reduce lead concentrations in the wastewater of an oil refinery through the utilization of a material composed of oyster shell waste (MIL-100(Fe)/Cygnea/Fe3O4/TiO2. Initially, iron oxide nanoparticles (Fe3O4) were synthesized via solvent-thermal synthesis. It was subsequently coated layer by layer with the organic-metallic framework MIL-100 (Fe) using the core-shell method. Additionally, the solvent-thermal method was utilized to integrate TiO2 nanoparticles into the magnetic organic-metallic framework's structure. Varieties of analytical analysis were utilized to investigate the physical and chemical properties of the synthetic final photocatalyst. Nitrogen adsorption and desorption technique (BET), scanning electron microscopy (SEM), scanning electron diffraction pattern (XRD), and transmission electron microscopy (TEM). Following the characterization of the final photocatalyst, the physical and chemical properties of the nanoparticles synthesized in each step, several primary factors that significantly affect the removal efficiency in the advanced oxidation system (AOPs) were examined. These variables consist of pH, photocatalyst dosage, lead concentration, and reaction temperature. The synthetic photocatalyst showed optimal performance in the removal of lead from petroleum wastewater under the following conditions: 35 °C temperature, pH of 3, 0.04 g/l photocatalyst dosage, and 100 mg/l wastewater concentration. Additionally, the photocatalyst maintained a significant level of reusability after undergoing five cycles. The findings of the study revealed that the photocatalyst dosage and pH were the most influential factors in the effectiveness of lead removal. According to optimal conditions, lead removal reached a maximum of 96%. The results of this investigation showed that the synthetic photocatalyst, when exposed to UVA light, exhibited an extraordinary capacity for lead removal.

Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment

This study focuses on the utilization of connectionist models, specifically Independent Component Analysis (ICA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Genetic Algorithm-Particle Swarm Optimization (GAPSO) integrated with a least-squares support vector machine (LSSVM) to forecast the degradation of tetracycline (TC) through photocatalysis using Metal-Organic Frameworks (MOFs). The primary objective of this study was to evaluate the viability and precision of these connectionist models in estimating the efficiency of TC degradation, particularly within the context of wastewater treatment. The input parameters for these models cover essential MOF characteristics, such as pore size and surface area, along with critical operational factors, such as pH, TC concentration, catalyst dosage, and illumination duration, all of which are linked to the photocatalytic performance of MOFs. Sensitivity analysis revealed that the illumination duration is the primary influencer of TC photodegradation with MOF photocatalysts, while the MOFs' surface area is the second crucial parameter shaping the efficiency and dynamics of the TC-MOF photocatalytic system. The developed LSSVM models display impressive predictive capabilities, effectively forecasting the experimental degradation of TC with high accuracy. Among these models, the GAPSO-LSSVM model excels as the top performer, achieving notable evaluation metrics, including STD, RMSE, MSE, MRE, and R2 at values of 3.09, 3.42, 11.71, 5.95, and 0.986, respectively. In comparison, the PSO-LSSVM, ICA-LSSVM, and GA-LSSVM models yield mean relative errors of 6.18%, 7.57%, and 11.37%, respectively. These outcomes highlight the exceptional predictive capabilities of the GAPSO-LSSVM model, solidifying its position as the most accurate and dependable model for predicting TC photodegradation in this study. This study contributes to advancing photocatalytic research and effectively reinforces the importance of leveraging machine learning methodologies for tackling environmental challenges.

Prediction of free radical reactions toward organic pollutants with easily accessible molecular descriptors

Machine learning (ML) is becoming an efficient tool for predicting the fate of aquatic contaminants owing to the preponderance of big data. However, whether ML can "learn" the differences in reactivity among different free radicals has not yet been tested. In this work, the effectiveness of combining ML algorithms with molecular fingerprints to predict the reactivity of three free radicals was evaluated. First, a dataset containing 211 organic pollutants and their respective rate constants with the carbonate radical (CO3•-) was used to develop predictive models using both linear regression and ML methods. The use of topological atomic alignment information, in the form of the molecular access system (MACCS) and Morgan Fingerprint, and the electronic structure features (energy levels of the lowest unoccupied and highest occupied molecular orbitals, ELUMO and EHOMO, and the energy gap between ELUMO and EHOMO) gave satisfactory predictive performances (ML model with Random Forest algorithm with MACCS: RMSEtest = 0.787; linear regression model with energy levels: RMSEtest = 0.641). Additionally, the model interpretation correctly described that the key reactivity features for CO3•- were relatively close to those for SO4•- rather than those for •OH. These results suggest that combination of ML algorithms with easily accessible molecular fingerprints would be a powerful tool to accurately predict the radical reactions towards organic compounds.

Highly efficient ultrasound-driven Cu-MOF/ZnWO4 heterostructure: An efficient visible-light photocatalyst with robust stability for complete degradation of tetracycline

Metal-organic frameworks (MOFs) are a significant class of porous, crystalline materials composed of metal ions (clusters) and organic ligands. The potential use of copper MOF (Cu-BTC) for the sonophotocatalytic degradation of Tetracycline (TC) antibiotic was investigated in this study. To enhance its catalytic efficiency, S-scheme heterojunction was created by combining Cu-BTC with Zinc tungstate (ZnWO4), employing an ultrasound-assisted hydrothermal method. The results demonstrated that the Cu-BTC/ZnWO4 heterojunction exhibited complete removal of TC within 60 min under simultaneous irradiation of visible light and ultrasound. Interestingly, the sonophotocatalytic degradation of TC using the Cu-BTC/ZnWO4 heterojunction showed superior efficiency (with a synergy index of ∼0.70) compared to individual sonocatalytic and photocatalytic degradation processes using the same heterojunction. This enhancement in sonophotocatalytic activity can be attributed to the formation of an S-scheme heterojunction between Cu-BTC and ZnWO4. Within this heterojunction, electrons migrated from Cu-BTC to ZnWO4, facilitated by the interface between the two materials. Under visible light irradiation, the built-in electric field, band edge bending, and coulomb interaction synergistically inhibited the recombination of electron-hole pairs. Consequently, the accumulated electrons in Cu-BTC and holes in ZnWO4 actively participated in the redox reactions, generating free radicals that effectively attacked the TC molecules. This study offers valuable perspectives on the application of a newly developed S-scheme heterojunction photocatalyst, demonstrating its effectiveness in efficiently eliminating diverse recalcitrant pollutants via sonophotocatalytic degradation.

Capability of TiO2 and Fe3O4 nanoparticles loaded onto Algae (Scendesmus sp.) as a novel bio-magnetic photocatalyst to degration of Red195 dye in the sonophotocatalytic treatment process under ultrasonic/UVA irradiation

In this study, the magnetic photocatalyst Scendesmus/Fe3O4/TiO2 was synthesized, and its sonophotocatalytic properties in relation to the degradation of the Red195 dye were evaluated. Particles were characterized using a scanning electron microscope (SEM), Fourier's transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), and a vibrating-sample magnetometer (VSM). At a pH of 5, a photocatalyst dosage of 100 mg, an initial R195 concentration of 100 mg/l, an ultrasound power of 38W, and an exposure time of 20 min, the maximum Red195 removal efficiency (100%) was achieved. After five cycles of recycling, the composite's sonophotocatalytic degradation stability for R195 remains above 95%. Experiments on scavenging indicate that electrons (h+) and hydroxyls (OH-) are indispensable decomposition agents. The removal of R195 by Scendesmus/Fe3O4/TiO2 is consistent with the pseudo-first-order kinetic, Freundlich, and Henderson's isotherm models, as determined by kinetic and isotherm investigations. The negative activation enthalpy of the standard (ΔH°) illuminates the exothermic adsorption mechanism. The increase in standard Gibbs activation free energy (ΔG°) with increasing temperature reveals the process is not spontaneous. As indicated by the negative value of the standard entropy of activation (ΔS°), activation of the reactants resulted in a loss of freedom.

Preparation of the self-accelerating photocatalytic self-cleaning carboxymethyl cellulose sodium-based hydrogel for removing cationic dyes

Hydrogels loaded with photocatalysts have shown great potential in effectively degrading dye wastewater. In this work, carboxymethyl cellulose sodium-based hydrogels loaded with nitrogen-doped graphene oxide-zinc oxide-zinc peroxide (NGO-ZnO-ZnO2) were synthesized using hydrothermal reaction and in-situ synthesis method. NGO acts as an electron mediator, suppressing the recombination of photoinduced electrons and holes. ZnO2 decomposes to generate hydrogen peroxide (H2O2), promoting a self-enhanced photocatalytic reaction. Carboxymethyl cellulose sodium (CMC) acts as a dispersant, improving the uniformity and stability of NGO-ZnO-ZnO2 within the hydrogel. The results demonstrate that NGO-ZnO-ZnO2 exhibits high photocatalytic degradation efficiency towards methyl orange (MO) (10 mg/L) and rhodamine B (RhB) (50 mg/L), with degradation rates of 99.99 % (200 min) and 99.26 % (160 min), respectively. The carboxymethyl cellulose sodium-based hydrogel achieves a photocatalytic degradation rate of 95.85 % (220 min) for RhB (10 mg/L). After 5 cycles of repeated photocatalytic tests, the degradation efficiency of the hydrogel towards RhB reaches 80.81 %. This work provides a low-cost and convenient method for constructing novel hydrogel carriers with high photocatalytic stability and efficiency.