Amoxicillin degradation from contaminated water by solar photocatalysis using response surface methodology (RSM)

A novel interpretable machine learning model approach for the prediction of TiO2 photocatalytic degradation of air contaminants

Air contaminants lead to various environmental and health issues. Titanium dioxide (TiO2) features the benefits of autogenous photocatalytic degradation of air contaminants. To evaluate its performance, laboratory experiments are commonly used to determine the kinetics of the photocatalytic-degradation rate, which is labor intensive, time-consuming, and costly. In this study, Machine Learning (ML) models were developed to predict the photo-degradation rate constants of air-borne organic contaminants with TiO2 nanoparticles and ultraviolet irradiation. The hyperparameters of the ML models were optimized, which included Artificial Neural Network (ANN) with Bayesian optimization, gradient booster regressor (GBR) with Bayesian optimization, Extreme Gradient Boosting (XGBoost) with optimization using Hyperopt, and Catboost combined with Adaboost. The organic contaminant was encoded through Molecular fingerprints (MF). Imputation method was applied to deal with the missing data. A generative ML model Vanilla Gan was utilized to create synthetic data to further augment the size of available dataset and the SHapley Additive exPlanations (SHAP) was employed for ML model interpretability. The results indicated that data imputation allowed for the full utilization of the limited dataset, leading to good machine learning prediction performance and preventing common overfitting problems with small-sized data. Additionally, augmenting experimental data with synthetic data significantly improved prediction accuracy and considerably reduced overfitting issues. The results ranked the feature importance and assessed the impacts of different experimental variables on the rate of photo-degradation, which were consistent with physico-chemical laws.

Multifunctional Cu:ZnS quantum dots for degradation of Amoxicillin and Dye Sulphon Fast Black-F and efficient determination of urea for assessing environmental aspects

This work is particularly aimed at the preparation of ZnS and Cu doped ZnS (Cu:ZnS) QDs by facile and easy technique, chemical precipitation method for the degradation of water pollutants and a simple scheme was proposed to prepare the urea-sensing system. The morphological and optical properties of the synthesized QDs was studied using high resolution transmission and scanning electron microscopes, X-ray diffraction, energy dispersive X-ray analysis, fluorescence and ultraviolet-visible spectroscopy, differential thermal and thermogravimetric analyses, Brunauer-Emmett-Teller analysis. The photocatalytic performance was systematically assessed by the photodegradation of an important pharmaceutical water pollutant, Amoxicillin (AMX) and a dye Fast Sulphon Black F (SFBF) in aqueous medium under UV light irradiation. Also, a very sensitive system was prepared by depositing the dots over an indium-tin-oxide (ITO) glass substrate for the sensing of biologically active molecule urea as it is an important monitor of public health in water and soil productivity. The results illustrated excellent photocatalytic efficiency (86.46% for AMX and 99.41% for SFBF) with stability up to four cycles of degradation reaction. The optimal photocatalyst dosage for achieving maximum removal of AMX was found to be 70 mg at a pH of 9.5, with a treatment time of 40 min. Similarly, for SFBF, the optimal photocatalyst dosage was determined to be 60 mg at pH 9, with a treatment time of 60 min. Further, the electrochemical analysis was done by fabricating Urease enzyme (UR)/Cu:ZnS QDs/ITO bioelectrode and then the fabricated bioelectrode, was utilized to determine the different concentrations of urea by cyclic voltammetry. Thus, the obtained limit of detection and sensitivity of the fabricated biosensing device for urea detection was obtained to be 0.0092 μM and 12 μA μM-1cm-2, respectively; under the optimized experimental conditions. Hence, it is anticipated that Cu:ZnS QDs can also successfully be applied as a promising material for fabrication of novel bioelectrode for urea determination and the biosensing platform is desirable and viable.

The Impacts and Analysis of Individual and Social Risks of the Stochastic Emission of Benzene from Floating-Roof Tanks Using Response Surface Analysis and MPACT Model

In the present study, the researchers used an integrated approach composed of response surface analysis (RSM) and MPACT model to predict fatality rates caused by benzene emitted from floating-roof tanks. RSM scenarios were configured in Expert Design (version 7.0) software using the central composite design (CCD) method and five variables of wind speed, relative humidity, atmospheric temperature, failure diameter, and emission height were considered. Continuous Pasquill-Gifford Gaussian model was used to estimate the results of the RSM scenarios. The response values were considered for exposure concentrations above 50 ppm (slight damages), 150 ppm (moderate damage), and 1000 ppm (high damage). The analysis of individual and social risks for each scenario was done using the MPACT model in SAFETI program (version 8.22) by providing two variables of population characteristics and the frequency of tank wall failure. The results showed that atmospheric temperature, wind speed, failure diameter, and emission height have positive effects on the dispersion of the cloud of toxic benzene vapor with a concentration of 1000 ppm. Intolerable individual risk distances were estimated to be lower for indoor environments than for outdoor. Maximum distances of intolerable individual risks for the worst-case scenarios were estimated up to 2500 m from the emission point, which resulted from exposure to a concentration of 1000-ppm benzene. Results regarding the estimation of social risks showed that over 1600 fatalities should be expected under the worst-case scenarios. The three factors of high temperature, low wind speed, and low emission height play a major role in the occurrence of scenarios with the highest fatalities. High wind speed and high emission height were the most important factors in most scenarios with zero fatalities rate. Generally, the findings of this study show the necessity to provide an emergency response plan in the studied industry in both autumn and winter due to low wind speed. However, the coupling of the developed statistical models based on regional meteorological conditions with the MPACT model can help researchers to design an emergency response plan to deal with leakage incidents in petrochemical industries.

Biosynthesis of (S)-1-(1-naphthyl) ethanol by microbial ketoreductase

(S)-1-(1-naphthyl) ethanol (SNE) is a chiral drug intermediate for the production of mevinic acid analog, a potent cholesterol agent. It acts as an HMG-CoA reductase inhibitor and is hence used in the synthesis of statins. Statins are lipid-lowering drugs used to lower cholesterol in the body. In our present study, we carried out whole-cell bioreduction of 1-Acetonaphthone to enantiopure SNE using selected microorganisms acquired by soil acclimation technique. The microorganism which exhibited higher bioreduction activity was determined using high-performance liquid chromatography (HPLC), and it was identified as Pichia kudriavzevii by ITS primer sequencing. After optimizing the parameters, Pichia sp. produced SNE with good conversion (75%), yield (67%), and excellent enantiomeric excess (100%). The microbial enzyme showed higher activity at 24-h-old supernatant. The crude and partially purified enzyme exhibited a specific activity of 51.13 U/mL and 62.72 U/mL, respectively, with a 1.22 purification fold.

Solar-light driven photodegradation of antimicrobials, their transformation by-products and antibiotic resistance determinants in treated wastewater

This study aimed to assess the possibility of using solar light-driven photolysis and TiO₂-based photocatalysis to remove (1) antibiotic residues, (2) their transformation products (TPs), (3) antibiotic resistance determinants, and (4) genes identifying the indicator bacteria in a treated wastewater (secondary effluent). 16 antimicrobials belonging to the different classes and 45 their transformation by-products were selected for the study. The most susceptible to photochemical decomposition was tetracycline, which was completely removed in the photocatalysis process and in more than 80% in the solar light-driven photolysis. 83.8% removal (on average) was observed using photolysis and 89.9% using photocatalysis in the case of the tested genes, among which the genes sul1, uidA, and intI1 showed the highest degree of removal by both methods. The study revealed that applied methods promisingly remove the tested antibiotics, their TPs and genes even in such a complex matrix including treated wastewater and photocatalysis process had a higher removal efficiency of antibiotics, TPs and genes tested. Moreover, the high percentage removal of the intI1 gene (>93%) indicates the possibilities of use of the solar light-driven photolysis and TiO₂-based photocatalysis in minimizing the antibiotic resistance genes transfer by mobile genetic elements.

Response surface algorithm for improved biotransformation of 1,4-dioxane using Staphylococcus capitis strain AG

The present investigation reports the biotransformation of an endrocrine disrupting agent; 1,4-dioxane through bacterial metabolism. Initially, potential bacterial isolates capable of surviving with minimum 1,4-dioxane were screened from industrial wastewater. Thereafter, screening was done to isolate a bacteria which can biotransform higher concentration (1000 mg/L) of 1,4-dioxane. Morphological and biochemical features were examined prior establishing their phylogenetic relationships and the bacterium was identified as Staphylococcus capitis strain AG. Biotransformation experiments were tailored using response surface tool and predictions were made to elucidate the opimal conditions. Critical factors influencing bio-transformation efficiency such as tetrahydrofuran, availability of 1,4-dioxane and inoculum size were varied at three different levels as per the central composite design for ameliorating 1,4-dioxane removal. Functional attenuation of 1,4-dioxane by S. capitis strain AG were understood using spectroscopic techniques were significant changes in the peak positions and chemical shifts were visualized. Mass spectral profile revealed that 1.5 (% v/v) S. capitis strain AG could completely (∼99%) remove 1000 mg/L 1,4-dioxane, when incubated with 2 μg/L tetrahydrofuran for 96 h. The toxicity of 1,4-dioxane and biotransformed products by S. capitis strain AG were tested on Artemia salina. The results of toxicity tests revealed that the metabolic products were less toxic as they exerted minimal mortality rate after 48 h exposure. Thus, this research would be the first to report the response prediction and precise tailoring of 1,4-dioxane biotransformation using S. captis strain AG.

Improving the 6-Aminopenicillanic acid release process using vermiculite-alginate biocomposite bead on drug delivery system

The present study deals with developing vermiculite (VMT)-alginate (Alg) composites with different cross-linker concentrations (CaCl₂) to deliver the controlled 6-aminopenicillin acid (6-APA). The Characterization of synthesized composites was conducted by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analyses. Optimization attempts were explored via the response surface method (RSM) to best predict the actual amount of compound. The adsorption capacity of 6-APA onto this adsorbent was found to be 208.33 mg/g, which was higher than that for other clays. The equilibrium and Kinetic studies (chemical reaction and diffusion-based models) indicated that drug absorption on VMT-Alg is homogeneous with chemical interaction. An increase in cross-linker (CaCl₂) concentration leads to improvement in the drug encapsulation efficiency while having no significant effect on loading efficiency. The in-vitro release of the pure drug shows a rapid burst release followed by 100% cumulative release within 6 h. Whereas, the synthesized drug with Alg substantially showed less release of 43% after 8 h. Release experiments revealed that the presence of the CaCl₂ delayed the release of the 6-APA less than 35% after 12 h. The kinetic release of 6-APA is followed by the Korsmeyer-Peppas model based on Fick's law mechanism due to the kinetic exponent (n < 0.5). All studied composites antibacterial activity after 24 h exposure against E. Coli and S. aureus. The antibacterial activities of composites were evaluated by the halo of no growth. The results showed that the VMT-Alg-6APA composite had strong activity against Gram-positive and Gram-negative bacteria.

Degradation of amoxicillin with sono, photo, and sonophotocatalytic oxidation under low-frequency ultrasound and visible light

The presence of pharmaceutically active compounds in aquatic bodies is a global concern, and suitable treatment technologies are required. In this study, the efficacy of photocatalytic, sonocatalytic, and sonophotocatalytic oxidation processes for the degradation of amoxicillin (AMX) was investigated using visible light with N doped TiO₂ (N-TiO₂) nanoparticles as the catalyst and low-frequency ultrasound in a novel multifrequency reactor. The influence of different operational parameters on the extent of AMX degradation was studied. Sonophotocatalytic oxidation was found more efficient for AMX degradation when compared to photocatalysis or sonocatalysis alone, and may be due to the reduced bandgap of the catalyst, enhanced cavitation effect due to the presence of the solid catalyst, and improved mass transfer of pollutants. AMX degradation during sono, photo, and sonophotocatalytic oxidation processes was in good agreement with pseudo-first-order kinetics. Empirical kinetic models were also developed using multiple linear regression for predicting the degradation efficiency accounting for the operational parameters. Scavenger experiments suggested that ^•OH radicals largely contributed to AMX degradation, and a plausible mechanism for degradation was proposed. Further, possible degradation pathways for all three treatment processes are also proposed after identifying the degradation products.

A generalized predictive model for TiO2-Catalyzed photo-degradation rate constants of water contaminants through artificial neural network

Titanium dioxide (TiO₂) is a well-known photocatalyst in the applications of water contaminant treatment. Traditionally, the kinetics of photo-degradation rates are obtained from experiments, which consumes enormous labor and experimental investments. Here, a generalized predictive model was developed for prediction of the photo-degradation rate constants of organic contaminants in the presence of TiO₂ nanoparticles and ultraviolet irradiation in aqueous solution. This model combines an artificial neural network (ANN) with a variety of factors that affect the photo-degradation performance, i.e., ultraviolet intensity, TiO₂ dosage, organic contaminant type and initial concentration in water, and initial pH of the solution. The molecular fingerprints (MF) were used to interpret the organic contaminants as binary vectors, a format that is machine-readable in computational linguistics. A dataset of 446 data points for training and testing was collected from the literature. This predictive model shows a good accuracy with a root mean square error (RMSE) of 0.173.

Tungsten Trioxide (WO3)-assisted Photocatalytic Degradation of Amoxicillin by Simulated Solar Irradiation

This study investigates the photocatalytic degradation of amoxicillin (AMO) by simulated solar irradiation using WO3 as a catalyst. A three-factor-three-level Box-Behnken design (BBD) consisting of 30 experimental runs is employed with three independent variables: initial AMO concentration, catalyst dosage, and pH. The experimental results are analyzed in terms of AMO degradation and mineralization, the latter of which is measured using dissolved organic carbon (DOC). The results show that the photocatalytic degradation of AMO follows pseudo-first-order kinetics. AMO degradation efficiency and the pseudo-first-order rate constants decrease with increasing initial AMO concentration and pH and increase with increasing catalyst dosage. Though AMO degradation is almost fully complete under the experimental conditions, DOC removal is much lower; the highest DOC removal rate is 35.82% after 180 min. Using these experimental results, second-order polynomial response surface models for AMO and DOC removal are constructed. In the AMO removal model, the first-order terms are the most significant contributors to the prediction, followed by the quadratic and interaction terms. Initial AMO concentration and pH have a significant negative impact on the photocatalytic degradation of AMO, while catalyst dosage has a significant positive impact. In contrast, in the DOC removal model, the quadratic terms make the most significant contribution to the prediction and the first-order terms the least. The optimal conditions for the photocatalytic degradation of AMO are found to be an initial AMO concentration of 1.0 μM, a catalyst dosage of 0.104 g/L, and a pH of 4, under which almost complete removal of AMO is achieved (99.99%).

Removal of metronidazole and amoxicillin mixtures by UV/TiO2 photocatalysis: an insight into degradation pathways and performance improvement

The degradation efficiencies and pathways of metronidazole (MNZ) and amoxicillin (AMX) in binary mixtures by UV/TiO₂ photocatalysis were studied. The presence of AMX significantly decreased the degradation of MNZ, whereas the existence of MNZ slightly reduced the degradation of AMX. This is basically due to the difference in attack ability of oxidizing agents present during TiO₂ photocatalysis. All oxidizing agents (hydroxyl radicals, superoxide radicals, and holes) could attack AMX molecules, but hydroxyl radicals showed insignificant attack ability in MNZ degradation. In TiO₂ photocatalysis of binary mixture, six transformation products were recognized by a high-resolution LC-QTof/MS. Because of competitive effect, only one product was sourced from MNZ degradation and four others were formed due to AMX degradation. The remaining one was a new product of the side reaction. This work indicated that the molecular structure of AMX determined its preferred degradation in a mixture. It not only affected the removal of antibiotics but also figured out the appearance of transformation products. In contrast to single systems, the extent of degradation reduced for each antibiotic in the presence of the second antibiotic was related to the availability of degradation pathways of each antibiotic. Moreover, suitable pH programming was applied to enhance the mineralization of the mixtures.

Photocatalytic degradation of yellow 2G dye using titanium dioxide/ultraviolet A light through a Box-Behnken experimental design: Optimization and kinetic study

Yellow 2G (Y2G), a type of anionic, synthetic monoazo dye that is widely used in household applications, textiles, and food industries, has been found to have cardiovascular and neurological effects on all living beings. In the present study, heterogeneous photocatalytic degradation of commercial Y2G was conducted using pure titanium dioxide (TiO₂) in a batch reactor system under ultraviolet A (UVA) light for 180 min. TiO₂ dosage, pH, and initial Y2G concentration were the three experimental parameters selected and studied to obtain preliminary information about the photocatalytic activities within a specified range. The Box-Behnken design method (BBD) was used to determine optimal values of the results using the above parameters of Y2G photocatalysis under response surface methodology (RSM). The optimum conditions were 0.914 g L^-1 TiO_2, pH 3.45, and an initial Y2G concentration of 20 mg L^-1. The Y2G degradation efficiency was 96.19% using a second-order polynomial equation with R² ≈ 0.999. The experimental results also showed that the photocatalytic process could be successfully explained using the modified Langmuir-Hinshelwood model, where k_c and K_LH were 0.787 mg L^-1 min and 0.010 L mg^-1, respectively.