Adsorption of tetracycline by polycationic straw: Density functional theory calculation for mechanism and machine learning prediction for tetracyclines' remediation

Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables

Biochar adsorption technology has been widely used to remove ammonia nitrogen from water bodies. However, existing methods for predicting adsorption efficiency often lack sufficient accuracy and practical usability. This study evaluated eight machine learning models, including XGB, LR, KNN, DT, RF, GBR, SVR, and ANN, to predict the adsorption efficiency of ammonia nitrogen. The evaluation utilized a dataset comprising 770 instances of ammonia nitrogen adsorption by biochar. The models' prediction performances were systematically compared, and cross-validation was applied to enhance their generalization ability, leading to the selection of the best-performing model. The selected model's parameters were further optimized using Bayesian optimization to improve the prediction accuracy. The Bayesian-optimized XGB model achieved the highest predictive performance, with a coefficient of determination (R2) of 0.978. The R2 values of the other models ranged from 0.556 (LR) to 0.927 (RF). Key factors influencing ammonia nitrogen adsorption efficiency were identified using SHAP analysis. These factors included biochar dosage, adsorption time, initial ammonia nitrogen concentration, solution pH, pyrolysis time, and O%. Their optimal ranges were further determined through partial dependency plots. This study developed a reliable machine learning tool for accurately predicting ammonia nitrogen adsorption efficiency. Additionally, it provided insights into optimizing the preparation processes and adsorption conditions of biochar, contributing to its practical application in treating ammonia nitrogen pollution in water bodies.

Adsorption and desorption of parachlormetaxylenol by aged microplastics and molecular mechanism

The addition of active ingredients such as antibacterial agent and non-active ingredients such as plastic microspheres (MPs) in personal care products (PCPs) are the common pollutants in the aquatic environment, and their coexistence poses potential threat to the aquatic ecosystem. As a substitute for the traditional antibacterial ingredients triclosan and triclocarban, the usage of parachlormetaxylenol (PCMX) is on the rise and is widely used in PCPs. In this study, the adsorption and desorption behaviors of PCMX were investigated with two typical MPs, polyvinyl chloride (PVC) and polyethylene (PE), and the effects of different aging modes and molecular mechanisms were explored through batch experiments and density functional theory calculation. Both laboratory aging and field aging resulted in surface wrinkles of MPs, along with an increased proportion of oxygen-containing functional groups (CO, -OH). At the same aging time, the degree of laboratory aging was stronger than that of field aging, and the aging degree of PVC was greater that of PE. The aging process enhanced the adsorption capacity of MPs for PCMX. The equilibrium adsorption capacity of PVC increased from 3.713 mg/g (virgin) to 3.823 mg/g (field aging) and 3.969 mg/g (laboratory aging), while that of PE increased from 3.509 mg/g to 3.879 mg/g and 4.109 mg/g, respectively. Meanwhile, aging also resulted in an increase in the desorption capacity of PCMX from PVC and PE. Oxygen-containing functional groups in aged MPs could serve as adsorption sites for PCMX and improved the electrostatic adsorption capacity. Oxygen-containing groups generated on the surface of aged MPs formed hydrogen bonding with the phenolic hydroxyl groups of PCMX, which became the main driving force for adsorption. Our results reveal the potential impact and mechanism of aging on the adsorption of PCMX by MPs, which provides new insights for the interaction mechanism between environmental MPs and associated contaminants.

A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent

The existence of antibiotics in water sources poses substantial hazards to both the environment and public health. To effectively monitor and combat this problem, accurate predictive models are essential. This research focused on employing machine learning (ML) techniques to construct some models for analyzing the adsorption capacity of ciprofloxacin (CIP) antibiotic from contaminated water. The robustness of ten machine learning algorithms was evaluated using performance metrics such as the Coefficient of determination (R2), Mean Square Error (MSE), Median Absolute Error (MedAE), Mean Absolute Error (MAE), Correlation coefficient (R), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Square Error (RMSE). The hyperparameters of the ML models were fine-tuned using the Bayesian optimization algorithm. The optimized models were comprehensively evaluated using feature importance analysis to quantify the relative significance of operational variables accurately. After a thorough assessment and comparison of various machine learning models, it was evident that the HistGradientBoosting (HGB) model outperformed others in terms of CIP adsorption performance. This was supported by their low MAE value of 0.1865 and high R2 value of 0.9999. The modeling projected the highest antibiotic adsorption (99.28%) under optimized conditions, including 10 mg/L of CIP, 357 mg/L of CuWO4@TiO2 adsorbent, a contact time of 60 min at room temperature, and near neutral pH (7.5). The combination of advanced ML algorithms and nano adsorbents has great potential for addressing the problem of antibiotic pollution in water sources.

Enhanced removal of tetracycline by vitamin C-modified cow manure biochar in water

Vitamin C (VC), due to its chemical properties, can provide more oxygen-containing functional groups such as hydroxyl groups for biochar (BC), which promotes the adsorption of tetracycline on biochar. Therefore, in this study, cow dung biochar (CDBC) was modified with VC and VC-modified CDBC (CDBC-VC) was synthesized. The modified biochar was characterized and related factors, adsorption kinetics, isotherms and adsorption mechanisms were investigated. Adsorption kinetics indicate a fast rate of adsorption. The adsorption isotherms showed that the maximum adsorption capacity was 31.72 mg/g (CDBC) and 50.90 mg/g (CDBC-VC), respectively, and the adsorption process was inhomogeneous with multiple molecular layers and the adsorbent has a higher affinity. Mechanistic studies showed that hydrogen bonding interactions, π-π electron donor-acceptor interactions, hydrophobic interactions, and electrostatic interactions were the key to the adsorption process. The analysis of adsorbent regeneration showed that CDBC-VC had good adsorption performance. CDBC and CDBC-VC showed the best performance in simulated industrial wastewater with removal rates of 78.81% and 93.69%. The adsorption mechanism was comprehensively analyzed using six machine learning models. The extreme gradient boosting model gave the best fit. Analysis of the weights of the input variables for predicting adsorption efficiency showed that the ratio of initial TC concentration to BC dosage (29.8%), specific surface area (23%), isoelectric point (8.8%), and ash content (7.7%) had a significant effect on the predicted results.

Collaboration of bacterial consortia for biodegradation of high concentration phenol and potential application of machine learning

Mixed culture of microorganisms is an effective method to remove high concentration of phenol in wastewater. At present, it is still a challenge for microorganisms to remove high-concentration phenol from wastewater. In this study, a phenol-degrading consortium was isolated, which could rapidly degrade 1800 mg/L phenol within 30 h, and the highest phenol degradation concentration was 2000 mg/L. Further exploration of how microbial consortium cooperates to promote phenol biodegradation was studied: the core bacteria of the microbial consortium was relatively stable during phenol degradation; the bacteria could improve the adaptability to environment and metabolic ability of phenol, by producing more surfactants and betaine, thereby improving the degradation rate. The determination coefficient (R2) in the machine learning model showed that the back propagation artificial neural network (BP-ANN) can predict the biodegradation of phenol under different conditions, saving time and economic costs. This study explains how microbial consortium cooperates to degrade phenol from the aspects of microbial consortium composition and metabolic analysis, which provides a theoretical basis for mixed culture microorganisms to degrade pollutants.

Cu-Co bimetallic organic framework as effective adsorbents for enhanced adsorptive removal of tetracycline antibiotics

In this study, the removal effect of a new MOF-on MOF adsorbent based on Cu-Co bimetallic organic frameworks on tetracycline antibiotics (TCs) in water system was studied. The adsorbent (Cu-MOF@Co-MOF) were synthesized by solvothermal and self-assembly method at different concentrations of Co2+/Cu2+. The characterization results of SEM, XRD, XPS, FTIR and BET indicated that the MOF-on MOF structure of Cu-MOF@Co-MOF exhibited the best recombination and physicochemical properties when the molar ratio of Co2+: Cu2+ is 5:1. In addition, the Cu-MOF@Co-MOF have a high specific surface area and bimetallic clusters, which can achieve multi-target synergistic adsorption of TCs. Based on above advantages, Cu-MOF@Co-MOF provided a strong affinity and could efficiently adsorb more than 80% of pollutants in just 5 to 15 min using only 10 mg of the adsorbent. The adsorption capacity of tetracycline and doxycycline was 434.78 and 476.19 mg/g, respectively, showing satisfactory adsorption performance. The fitting results of the experimental data were more consistent with the Langmuir isotherm model and pseudo-second-order kinetic model, indicating that the adsorption process of TC and DOX occurred at the homogeneous adsorption site and was mainly controlled by chemisorption. Thermodynamic experiments showed that Cu-MOF@Co-MOF was thermodynamically advantageous for the removal of TCs, and the whole process was spontaneous. The excellent adsorption capacity and rapid adsorption kinetics indicate the prepared MOF-on MOF adsorbent can adsorb TCs economically and quickly, and have satisfactory application prospects for removing TCs in practical environments. The results of the study pave a new way for preparing novel MOFs-based water treatment materials with great potential for efficient removal.

Macro-manufacturing robust and stable metal-organic framework beads for antibiotics removal from wastewater

Metal-organic frameworks (MOFs) have shown great prospects in wastewater remediation. However, the easy aggregation, difficult separation and inferior reusability greatly limit their large-scale application. Herein, we proposed a facile, green and low-cost strategy to construct robust and stable MOF-based hydrogel beads (Fe-BTC-HBs) in a gram scale, and employed them to remove antibiotics from wastewater. As a result, the Fe-BTC-HBs demonstrated outstanding adsorption capacity for both ofloxacin (OFL) and tetracycline (TC) (281.17 mg/g for OFL and 223.60 mg/g for TC) under a near-neutral environment. The main adsorption mechanisms of OFL and TC were hydrogen bonding and π-π stacking interaction. Owing to its macroscopic granule and stable structure, Fe-BTC-HBs can be separated rapidly from wastewater after capturing antibiotics, and more than 85% adsorption capacity still remained after six cycles, while the powdered Fe-BTC only showed less than 6% recovery efficiency with massive weight loss (around 92%). In real industrial effluent, the adsorption performance of Fe-BTC-HBs toward two antibiotics exhibited negligible decreases (2.9% for OFL and 2.2% for TC) compared with that in corresponding solutions. Furthermore, Fe-BTC-HBs also had appealing economic and environmental benefit. Overall, the macro-manufactured MOF beads have the promising potential for the large-scale wastewater treatment.