Digital imaging-in-flow (FlowCAM) and probabilistic machine learning to assess the sonolytic disinfection of cyanobacteria in sewage wastewater.

Assessing the cyanobacteria disinfection in sewage and its compliance with international-standards requires determining the concentration and viability, which can be achieve using Imaging Flow Cytometry device called FlowCAM. The objective is to thoroughly investigate the sonolytic morphological changes and disinfection-performance towards toxic cyanobacteria existing in sewage using the FlowCAM. After optimizing the process conditions, over 80% decline in cyanobacterial cell counts was observed, accompanied by an additional 10-15% of cells exhibiting injuries, as confirmed through morphological investigation. Moreover, for the first time, the experimentally collected data was utilized to build deep-learning probabilistic-neural-networks (PNN) and natural-gradient-boosting (NGBoost) models for predicting disinfection efficiency and ABD area as target outputs. The findings suggest that the NGBoost model exhibited superior prediction performance for both targets, with high test coefficient of determination (R2 > 0.87) and lower test errors (RMSE < 7.10, MAE < 4.14). The confidence interval examination in NGBoost prediction performance showed a minute variation from the experimentally calculated values, suggesting a high accuracy in model prediction. Finally, SHAP analysis suggests the sonolytic time alone contributes around 50% to the cyanobacteria disinfection. Overall, the findings demonstrate the effectiveness of the FlowCAM device and the potential of machine-learning modeling in predicting disinfection outcomes.

Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal.

Photocatalytic reactions with semiconductor-based photocatalysts have been investigated extensively for application to wastewater treatment, especially dye degradation, yet the interactions between different process parameters have rarely been reported due to their complicated reaction mechanisms. Hence, this study aims to discern the impact of each factor, and each interaction between multiple factors on reaction rate constant (k) using a decision tree model. The dyes selected as target pollutants were indigo and malachite green, and 5 different semiconductor-based photocatalysts with 17 different compositions were tested, which generated 34 input features and 1527 data points. The Boruta Shapley Additive exPlanations (SHAP) feature selection for the 34 inputs found that 11 inputs were significantly important. The decision tree model exhibited for 11 input features with an R2 value of 0.94. The SHAP feature importance analysis suggested that photocatalytic experimental conditions, with an importance of 59%, was the most important input category, followed by atomic composition (39%) and physicochemical properties (2%). Additionally, the effects on k of the synergy between the metal cocatalysts and important experimental conditions were confirmed by two feature SHAP dependence plots, regardless of importance order. This work provides insight into the single and multiple factors that affect reaction rate and mechanism.

Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green.

This study focuses on the potential capability of numerous machine learning models, namely CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree, Bagging, light gradient boosting machine (LGBM), GaussianProcess, artificial neural network (ANN), and light long short-term memory (LightLSTM). These models were investigated to predict the photocatalytic degradation of malachite green from wastewater using various NM-BiFeO3 composites. A comprehensive databank of 1200 data points was generated under various experimental conditions. The ten input variables selected were the catalyst type, reaction time, light intensity, initial concentration, catalyst loading, solution pH, humic acid concentration, anions, surface area, and pore volume of various photocatalysts. The MG dye degradation efficiency was selected as the output variable. An evaluation of the performance metrics suggested that the CatBoost model, with the highest test coefficient of determination (0.99) and lowest mean absolute error (0.64) and root-mean-square error (1.34), outperformed all other models. The CatBoost model showed that the photocatalytic reaction conditions were more important than the material properties. The modeling results suggested that the optimized process conditions were a light intensity of 105 W, catalyst loading of 1.5 g/L, initial MG dye concentration of 5 mg/L and solution pH of 7. Finally, the implications and drawbacks of the current study were stated in detail.

Constructing magnetic Pt-loaded BiFeO3 nanocomposite for boosted visible light photocatalytic and antibacterial activities.

Visible light-responsive Pt-loaded coral-like BiFeO3 (Pt-BFO) nanocomposite at different Pt loadings was synthesized via a two-step hydrothermal synthesis method. The as-synthesized photocatalyst was characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), UV-vis diffuse reflectance spectroscopy (UV-vis DRS), photoluminescence (PL) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and magnetic hysteresis loop (M-H loop) analyses. The FESEM images revealed that Pt nanoparticles were evenly distributed on the coral-like BFO. The UV-vis DRS results indicated that the addition of Pt dopant modified the optical properties of the BFO. The as-synthesized Pt-BFO nanocomposite was effectively applied for the photodegradation of malachite green (MG) dye under visible light irradiation. Specifically, 0.5 wt% Pt-BFO nanocomposite presented boosted photocatalytic performance than those of the pure BFO and commercial TiO2. Such a remarkably improved photoactivity could be mainly attributed to the formation of good interface between Pt and BFO, which not only boosted the separation efficiency of charge carriers but also possessed great redox ability for significant photocatalytic reaction. Moreover, the strong magnetic property of the Pt-BFO nanocomposite was helpful in the particle separation along with its great recyclability. The radical scavenger test indicated that hole (h+), hydroxyl (·OH) radical, and hydrogen peroxide (H2O2) were the main oxidative species for the Pt-BFO photodegradation of MG. Finally, the Pt-BFO nanocomposite was revealed high antibacterial activity towards Bacillus cereus (B. cereus) and Escherichia coli (E. coli) microorganisms, highlighting its potential photocatalytic and antibacterial properties at different industrial and biomedical applications.