Optimization and modeling of methyl orange adsorption onto polyaniline nano-adsorbent through response surface methodology and differential evolution embedded neural network

Amine-functionalized organically modified silica for the effective adsorption of Chlorpyrifos and Triazophos Residues from Orange juice

Excessive application of pesticides to combat pests in orange fruit has resulted in pervasive buildup of their residues leading to severe risks to human health. The study reports the synthesis of amine-functionalized-organically-modified silica (ormosil) to efficiently extract two target pesticides; chlorpyrifos (CPF) and triazophos (TAP) from orange juice. Synthesized ormosil was characterized using different analytical techniques such as scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and fourier transform infrared spectroscopy (FTIR). Adsorption kinetics of ormosil for CPF and TAP followed a pseudo-first-order indicating a physisorption process with excellent removal efficiencies. Detection limits for CPF and TAP were 0.075 and 0.048 μg L-1, respectively. Similarly, limits of quantification were 0.8 and 0.6 μg L-1. Furthermore, there was no significant decrease in the performance of ormosil against CPF and TAP over six successive adsorptive cycles. These findings underscore the potential of investigated ormosil in food analysis.

Highly efficient dye adsorption by hierarchical porous SA/PVA/ZIF-8 composite microgels prepared via microfluidics

Hierarchical porous composite microgels (SPZ microgels) were synthesized using microfluidic technology, composed of sodium alginate (SA), polyvinyl alcohol (PVA), and zeolitic imidazolate framework-8 (ZIF-8). The incorporation of ZIF-8 nanoparticles led to the formation of significant porous structures within the microgels, greatly enhancing their dye adsorption performance. Additionally, the diffusion of acetone during the crosslinking reaction resulted in sodium chloride crystal formation, creating a hierarchical porous structure with larger internal porous channels and smaller external channels. These SPZ microgels exhibited remarkable adsorption capabilities for both anionic and cationic dyes. The SPZ microgels showed exceptional adsorption capacities of 180 mg/g for methyl orange (MO) and 210 mg/g for methylene blue (MeB), far exceeding the performance of control microgels without the hierarchical porous structure (20 mg/g for MO and 150 mg/g for MeB). The hierarchical porous structure provided a larger surface area and facilitated improved diffusion and faster adsorption kinetics, contributing to the superior adsorption performance of the SPZ microgels. Kinetic studies revealed that MeB adsorption followed pseudo-second-order kinetics, while MO adsorption followed pseudo-first-order kinetics. Isotherm studies established that the Langmuir model accurately described MeB adsorption, indicating monolayer adsorption, while the Freundlich model effectively characterized MO adsorption, indicating multilayer interactions.

Application and innovation of artificial intelligence models in wastewater treatment

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

Neuroprotective Effects of Morin Against Cadmium- and Arsenic-Induced Cell Damage in PC12 Neurons

Arsenic and cadmium, both toxic metals and widespread environmental pollutants, can trigger apoptosis and oxidative stress in various tissues and cells. Morin, a natural flavonoid with diverse biological properties, has been found to protect neurons from oxidative stress and apoptosis-induced damage. This research aimed to examine the protective properties of morin against neurotoxicity caused by arsenic and cadmium, utilizing PC12 cells as a model for nerve cells. The cells were pre-treated with different concentrations of morin and then exposed to arsenic and cadmium, after which cell viability and reactive oxygen species (ROS) production were assessed. Additionally, western blotting was performed to evaluate the protein levels of the Bax/Bcl-2 ratio and cleaved-caspase-3. Following exposure to arsenic and cadmium, there were significant increases in ROS, Bax/Bcl-2 ratio, and cleaved-caspase-3. However, the results of the study demonstrated the beneficial effects of morin at various concentrations, as it increased cell viability and decreased ROS production. Furthermore, morin at a concentration of 10 µM was found to reduce the elevated levels of cleaved-caspase-3 induced by arsenic and diminish the increased Bax/Bcl-2 ratio after exposure to arsenic and cadmium. The findings of this study suggest that morin can effectively protect cells from arsenic and cadmium-induced neurotoxicity through its antioxidant and anti-apoptotic effects. Thus, morin should be considered a promising agent for treating arsenic and cadmium toxicity.

β-Cyclodextrin mediated controlled release of phenothiazine from pH-responsive pectin and pullulan-based hydrogel optimized through experimental design

Drugs with lower permeability and water solubility provide major challenges for producing safe and efficient formulations. The current work aims to prepare ICs of the drug phenothiazine and β-cyclodextrin via physical, microwave, freeze-drying, and kneading methods. Many analytical methods, such as 1H NMR, ROESY, FT-IR, DSC, SEM, and XRD, were then used to confirm the formation of inclusion complexes. The natural polysaccharide-based hydrogel comprising pectin and pullulan was synthesized in air and optimized through various parameters. In order to maximize the reaction parameters, Response Surface Methodology design was employed for experimental optimization. We use FT-IR, TGA, SEM, EDX, and XRD to investigate hydrogel formation. At 37 °C, an investigation was carried out on the in vitro controlled release of PN at pH 2, 7, and 7.4. The analysis of drug release data revealed that PM and KM exhibited an initial burst release of drugs, with the MW and FD method proving to be the most suitable approach for achieving precise ICs of PN and β-CD for sustained drug release. The kinetics of drug release were evaluated using various kinetic models, with the Riteger-Peppas and Peppas-Sahlin models demonstrating the best fit for drug release in all instances.

Predicting Cd(II) adsorption capacity of biochar materials using typical machine learning models for effective remediation of aquatic environments

The screening and design of "green" biochar materials with high adsorption capacity play a pivotal role in promoting the sustainable treatment of Cd(II)-containing wastewater. In this study, six typical machine learning (ML) models, namely Linear Regression, Random Forest, Gradient Boosting Decision Tree, CatBoost, K-Nearest Neighbors, and Backpropagation Neural Network, were employed to accurately predict the adsorption capacity of Cd(II) onto biochars. A large dataset with 1051 data points was generated using 21 input variables obtained from batch adsorption experiments, including preparation conditions for biochar (2 features), physical properties of biochar (4 features), chemical composition of biochar (9 features), and adsorption experiment conditions (6 features). The rigorous evaluation and comparison of the ML models revealed that the CatBoost model exhibited the highest test R2 value (0.971) and the lowest RMSE (20.54 mg/g), significantly outperforming all other models. The feature importance analysis using Shapley Additive Explanations (SHAP) indicated that biochar chemical compositions had the greatest impact on model predictions of adsorption capacity (42.2 %), followed by adsorption conditions (37.57 %), biochar physical characteristics (12.38 %), and preparation conditions (7.85 %). The optimal experimental conditions optimized by partial dependence plots (PDP) are as follows: as high Cd(II) concentration as possible, C(%) of 33 %, N(%) of 0.3 %, adsorption time of 600 min, pyrolysis time of 50 min, biochar dosage of less than 2 g/L, O(%) of 42 %, biochar pH value of 11.2, and DBE of 1.15. This study unveils novel insights into the adsorption of Cd(II) and provides a comprehensive reference for the sustainable engineering of biochars in Cd(II) wastewater treatment.

Modeling and optimization of triclosan biodegradation by the newly isolated Bacillus sp. DL4: kinetics and pathway speculation

Triclosan is a widely used antibacterial agent and disinfectant, and its overuse endangered ecological safety and human health. Therefore, reducing residual TCS concentrations in the environment is an urgent issue. Bacillus sp. DL4, an aerobic bacterium with TCS biodegradability, was isolated from pharmaceutical wastewater samples. Response surface methodology (RSM) and artificial neural network (ANN) were carried out to optimize and verify the different condition variables, and the optimal growth conditions of strain DL4 were obtained (35 °C, initial pH 7.31, and 5% v/v). After 48 h of cultivation under the optimal conditions, the removal efficiency of strain DL4 on TCS was 95.89 ± 0.68%, which was consistent with the predicted values from RSM and ANN models. In addition, higher R2 value and lower MSE and ADD values indicated that the ANN model had a stronger predictive capability than the RSM model. Whole genome sequencing results showed that many functional genes were annotated in metabolic pathways related to TCS degradation (e.g., amino acid metabolism, xenobiotics biodegradation and metabolism, carbohydrate metabolism). Main intermediate metabolites were identified during the biodegradation process by liquid chromatography-mass spectrometry (LC-MS), and a possible pathway was hypothesized based on the metabolites. Overall, this study provides a theoretical foundation for the characterization and mechanism of TCS biodegradation in the environment by Bacillus sp. DL4.

Environmental resilience through artificial intelligence: innovations in monitoring and management

The rapid rise of artificial intelligence (AI) technology has revolutionized numerous fields, with its applications spanning finance, engineering, healthcare, and more. In recent years, AI's potential in addressing environmental concerns has garnered significant attention. This review paper provides a comprehensive exploration of the impact that AI has on addressing and mitigating critical environmental concerns. In the backdrop of AI's remarkable advancement across diverse disciplines, this study is dedicated to uncovering its transformative potential in the realm of environmental monitoring. The paper initiates by tracing the evolutionary trajectory of AI technologies and delving into the underlying design principles that have catalysed its rapid progression. Subsequently, it delves deeply into the nuanced realm of AI applications in the analysis of remote sensing imagery. This includes an intricate breakdown of challenges and solutions in per-pixel analysis, object detection, shape interpretation, texture evaluation, and semantic understanding. The crux of the review revolves around AI's pivotal role in environmental control, examining its specific implementations in wastewater treatment and solid waste management. Moreover, the study accentuates the significance of AI-driven early-warning systems, empowering proactive responses to environmental threats. Through a meticulous analysis, the review underscores AI's unparalleled capacity to enhance accuracy, adaptability, and real-time decision-making, effectively positioning it as a cornerstone in shaping a sustainable and resilient future for environmental monitoring and preservation.

Decolorization of Textile Azo Dye via Solid-State Fermented Wheat Bran by Lasiodiplodia sp. YZH1

Textile dyes are one of the major water pollutants released into water in various ways, posing serious hazards for both aquatic organisms and human beings. Bioremediation is a significantly promising technique for dye decolorization. In the present study, the fungal strain Lasiodiplodia sp. was isolated from the fruiting bodies of Schizophyllum for the first time. The isolated fungal strain was examined for laccase enzyme production under solid-state fermentation conditions with wheat bran (WB) using ABTS and 2,6-Dimethoxyphenol (DMP) as substrates, then the fermented wheat bran (FWB) was evaluated as a biosorbent for Congo red dye adsorption from aqueous solutions in comparison with unfermented wheat bran. A Box-Behnken design was used to optimize the dye removal by FWB and to analyze the interaction effects between three factors: fermentation duration, pH, and dye concentration. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) were applied to study the changes in the physical and chemical characteristics of wheat bran before and after fermentation. An additional experiment was conducted to investigate the ability of the Lasiodiplodia sp. YZH1 to remove Congo red in the dye-containing liquid culture. The results showed that laccase was produced throughout the cultivation, reaching peak activities of ∼6.2 and 22.3 U/mL for ABTS and DMP, respectively, on the fourth day of cultivation. FWB removed 89.8% of the dye (100 mg L-1) from the aqueous solution after 12 h of contact, whereas WB removed only 77.5%. Based on the Box-Behnken design results, FWB achieved 93.08% dye removal percentage under the conditions of 6 days of fermentation, pH 8.5, and 150 mg L-1 of the dye concentration after 24 h. The fungal strain removed 95.3% of 150 mg L-1 of the dye concentration after 8 days of inoculation in the dye-containing liquid culture. These findings indicate that this strain is a worthy candidate for dye removal from environmental effluents.

One-pot synthesis of metal oxide-clay composite for the evaluation of dye removal studies: Taguchi optimization of parameters and environmental toxicity studies

The present study demonstrates the synthesis of eco-friendly metal oxide-clay composites (MgO-clay and CaO-clay) with phytochemical functionalization. The physical and chemical properties of prepared composites were characterized using standard techniques viz. scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction. The effect of pH on the dye adsorption capability of the synthesized composites was studied. The adsorption of an anionic dye methyl orange (MO) and a cationic due methylene blue (MB) was favored in the acidic and basic regions, respectively. The Taguchi design approach was adopted for the removal of MO and MB from wastewater using the synthesized composites. The obtained results suggest that initial dye concentration and composite dosage were the most influential parameters in dye removal among all the studied parameters. The adsorption experiments were carried out using MgO-clay and CaO-clay composites with the optimum conditions obtained from Taguchi optimization to validate the predicted response. The experimental parameters viz. the effect of contact time, initial dye concentration, and solution temperature were studied for screened composite (CaO-clay) with optimized conditions. The obtained results were interpreted using standard isotherms and kinetic models. A maximum adsorption capacity of 571 ± 10 and 859 ± 14 mg g^-1 was obtained from the Langmuir adsorption isotherm for MO and MB, respectively. Regeneration studies suggested that the CaO-clay composite can be utilized up to 3 cycles with reduced adsorption capacity of the dyes over cycles due to the solid binding nature of dyes on the CaO-clay composite. The fresh and utilized CaO-clay composite were tested for their environmental toxicity analysis using ecologically important soil microorganisms. The obtained results suggested no detrimental effects on soil microbe's functionality, indicating their threat-free disposal in the soil environment.

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.

Molecularly imprinted ormosil as a sorbent for targeted dispersive solid phase micro extraction of pyriproxyfen from strawberry samples

Novel molecularly imprinted organically modified silica was prepared by reacting acrylamide and 3-(tri-methoxysilyl) propyl methacrylate followed by condensation and hydrolysis with tetraethyl ortho-silicate for the determination of pyriproxyfen. The sorbent proved to be highly selective for the template molecule, pyriproxyfen. The characterization of sorbent was carried out using SEM, BET and TGA. The prominent peaks in FTIR at 3700 cm^-1 and 1071 cm^-1 confirmed the stretching of amide group's N-H and Si-O-Si bond linkage of MI_Ormosil. The pseudo-first-order model (R² 0.99) described the adsorption kinetics of the MI_Ormosil, whereas among adsorption isotherms, Freundlich model showed the best fit (R² 0.99). The molecularly imprinted silica was applied for the determination of target analytes from strawberries sample using dispersive solid-phase micro extraction (DSPME) followed by high-performance liquid chromatography (HPLC). The LOD (4.93 x10^-5 μg mL^-1) and LOQ (1.49 x10^-4 μg m-¹) values were calculated by signal to noise ratio through HPLC. Results show that the maximum binding capacity and percentage recovery values of MI_Ormosil were 13 mg g^-1 (n = 5) and 97.3% respectively.

Modeling and optimization approach for phytoremediation of formaldehyde from polluted indoor air by Nephrolepis obliterata plant

This study aimed to model the removal of formaldehyde as an indoor air pollutant by Nephrolepis obliterata (R.Br.) J.Sm. plant using response surface methodology (RSM) and artificial neural network (ANN) models, and optimization of the models by particle swarm optimization algorithm (PSO). The data obtained in pilot-scale experiments under a controlled environment were used in this study. The effects of parameters on the removal efficiency such as formaldehyde concentration, relative humidity, light intensity, and leaf surface area were empirically investigated and considered as model parameters. The results of the RSM model, with power transformation, were in meaningful compromise with the experiments. A multilayer perceptron (MLP) neural network was also designed, and the mean of squared error (MSE), mean absolute error (MAE), and R² were used to evaluate the network. Several training algorithms were assessed and the best one, the Levenberg Marquardt (LM), was selected. The PSO algorithm proved that the highest removal efficiency of formaldehyde was obtained in the presence of light, maximum leaf surface area and relative humidity, and at the lowest inlet concentration. The empirical system breakthrough occurred at 15 mg/m³ of formaldehyde, and the maximum elimination capacity was about 0.96 mg per m² of leaves. The findings indicated that the ANN model predicted the removal efficiency more accurately compared to the RSM model.

Cellulose-based hydrogel for adsorptive removal of cationic dyes from aqueous solution: isotherms and kinetics

The development of economic and recyclable adsorbents for removing pollutants from contaminated water is gaining increasing attention. Agro residue or nature-based material sourced absorbents could revolutionize the future of wastewater treatment. Hence in this study, nanocellulose was synthesized from coconut husk fiber and immobilized onto chitosan to form hydrogel beads. The BET surface area and zeta potential of the adsorbent nanocrystalline cellulose-chitosan hydrogel (NCC-CH) bead was 25.77 m2 g-1 and +50.6 mV, respectively. The functional group analysis also confirmed that the adsorbent had functional groups appropriate for the adsorption of textile dyes. The adsorption performance of NCC-CH and also the influence of initial dye concentration, adsorbent dose, pH, and contact time was evaluated by batch adsorption studies with crystal violet (CV) and methylene blue (MB) dyes. The most favorable operational conditions achieved through I-optimal design in response surface methodology were 0.5 g NCC-CH, 1 h, 9 pH, and 60 mg L-1 for CV removal (94.75%) and 0.13 g NCC-CH, 1 h, 9 pH, and 30 mg L-1 for MB removal (95.88%). The polynomial quadratic model fits the experimental data with an R 2 value of 0.99 and 0.98 for CV and MB removal, respectively. The optimum depiction of the isotherm data was obtained using the Freundlich model for MB adsorption and Freundlich and Langmuir model for CV adsorption. The Dubinin-Radushkevich (D-R) isotherm was also a good fit to the adsorption of CV and MB dye, suggesting the physisorption due to its free energy of adsorption < 8 kJ mol-1. The kinetics were effectively explained by a pseudo-second order model for both the dyes suggesting that chemical mechanisms influenced the adsorption of CV and MB dyes onto NCC-CH. The intraparticle diffusion model best suited the MB adsorption with three stages rather than the CV with a single step process. Also, the removal efficiency of adsorbent was retained at above 60% even after seven adsorption-desorption cycles indicating the effectiveness of the NCC-CH hydrogel beads for the removal of textile dyes.

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.

Optimization and modeling of ammonia nitrogen removal from anaerobically digested liquid dairy manure using vacuum thermal stripping process

During anaerobic digestion (AD) of liquid dairy manure, organic nitrogen converts to ammonia nitrogen (NH₃-N) and subsequently escalates the NH₃-N concentrations in manure. Among different available NH₃-N removal processes treating anaerobically digested liquid dairy manure (ADLDM), vacuum thermal stripping is reported to be an effective technique. However, none of the studies have performed multi-parameter optimization, which is of utmost significance in maximizing process efficiency. In this study, critical operational parameters for vacuum thermal stripping of NH₃-N from ADLDM were optimized and modeled for the first time via integrating grey relational analysis (GRA)-based Taguchi design, response surface methodology (RSM), and RSM-artificial neural network (ANN). The initial experimental trials conducted using the GRA coupled with Taguchi L₁₆ orthogonal array revealed the order of influence of the process parameters on NH₃-N removal as vacuum pressure (kPa) > temperature (°C) > treatment time (min) > mixing speed (rpm) > pH. The values of the first three most influential operating parameters were then further optimized and modeled using RSM and RSM-ANN models. Under the optimized conditions (temperature: 69.6 °C, vacuum pressure: 43.5 kPa, and treatment time: 87.65 min), the NH₃-N removal efficiency of 93.58 ± 0.59 % was experimentally observed and was in line with the RSM and RSM-ANN models' predicted values. While the RSM-ANN model showed a better prediction potential than did the RSM model when compared statistically. Moreover, the nutrient contents (nitrogen, N and sulfur, S) of the recovered NH₃-N as ammonium sulfate ((NH₄)₂SO₄) were in reasonable agreement with the market-available (NH₄)₂SO₄ fertilizer. The results presented in this study provide important insights into improving the treatment process performance and will help design and operate future pilot- and full-scale vacuum thermal stripping processes in dairy farms.

Adsorptive Optimization of Crystal Violet Dye Using Central Composite Rotatable Design and Response Surface Methodology: Statistical Analysis, Kinetic and Isotherm Studies

Water contamination is emerging as the most critical global issues in the world, calling for the treatment eco-techniques. Taking advantage of biowastes as adsorbent materials is not only in accordance with the purpose of environmental protection but also enhance the higher value-added products. In this work, water hyacinth (Eichhornia crassipes) powder was used as an efficient adsorbent for the removal of crystal violet from aqueous solutions. The structure of water hyacinth powder adsorbent was characterized by Fourier transform infrared spectroscopy and scanning electron microscopy analysis. Based on the central composite rotatable design and response surface methodology, the effect of different parameters such as initial pH solution, contact time, adsorbent dosage, and initial crystal violet concentration was optimized. The maximum adsorption capacity of 180.336 mg/g was achieved under the optimum condition as initial pH solution of 6.246, contact time of 125.698 min, the adsorbent dosage of 1.382 g/L, and initial dye concentration of 615.865 mg/L. Moreover, the Langmuir isotherm provided the best fit with a high correlation coefficient of 0.9981 and a maximum monolayer adsorption capacity of 181.818 mg/g at 30 °C. The kinetic studies indicated that the pseudo-second-order model was adequately applied for the adsorption kinetic of crystal violet on the water hyacinth powder adsorbent. The utilization of the water hyacinth plant, an abundant species, as a low-cost biosorbent to remove crystal violet using the central composite rotatable design combined with response surface methodology approach is recommended for the real treatment of organic dyes.

Sustained release of edaravone from (2-hydroxypropyl)-β-cyclodextrin mediated tamarind kernel powder/kappa-carrageenan hydrogel: Microwave-assisted synthesis and optimization using experimental design

In the current study, a sustained release formulation made of natural polysaccharide tamarind kernel powder/kappa-carrageenan and (2-hydroxypropyl)-β-cyclodextrin (2-Hp-β-CD) was chosen to increase drug effectiveness. A kappa-carrageenan and tamarind kernel powder 3-D hydrogel network was synthesized with the aid of microwave irradiations. The ICs complexes were prepared using a physical mixture (PM), kneading (KM), and microwave (MW) approach and were then successfully loaded into the hydrogel. The synthesis of ICs was verified as a true IC using DSC, SEM, FTIR, ¹H NMR, and 2D NMR ROESY. A study on the in vitro sustained release of EV at pH 2, 7, and 7.4 was conducted at 37 °C. The microwave (MW) method was the most effective method for preparing true ICs of EV and 2-Hp-β-CD for sustained drug release, as evidenced by the drug release data, which indicated that PM and KM displayed a burst release of the drug. Ritger-Peppas and Peppas-Sahlin were essential models for drug release. A phase solubility analysis was done to evaluate the IC's stoichiometry and complexation constant. Studies on drug release have shown that 2-Hp-β-CD was effective at causing pH-responsive sustained drug release.

Lead ferrite-activated carbon magnetic composite for efficient removal of phenol from aqueous solutions: synthesis, characterization, and adsorption studies

A novel lead ferrite-magnetic activated carbon (lead ferrite-MAC) composite was developed using the chemical co-precipitation method. Instrumental analyses such as X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and Brunauer-Emmett-Teller (BET) analysis were performed to characterize adsorbent. The uptake of phenol from aqueous solutions using the developed adsorbent was compared to that of pristine activated carbon. The maximum adsorption capacity of lead ferrite-MAC composite (145.708 mg/g) was more than that of pristine activated carbon (116.606 mg/g) due to the metal hydroxides coated on activated carbon since they improve the retention of phenol on the available active sites of adsorbent and create an additional electrostatic interaction with the phenol adsorbate. Regarding the high value of the coefficient of determination (R2) and adjusted determination coefficient (R2adj), coupled with the lower values of average relative error (ARE) and minimum squared error (MSE), it can be found that the isothermal data for the lead ferrite-MAC adsorbent were in agreement with the isotherm models of Redlich-Peterson and Langmuir. From the kinetic viewpoint, pseudo-second-order and linear driving force models explained the phenol adsorption data for both adsorbents. The reusability tests for lead ferrite-MAC composite revealed that after six cycles, 85% of the initial adsorption capacity was maintained. The developed adsorbent can be successfully applied to uptake phenol from aqueous solutions.

Sensitivity Analysis with the Monte Carlo Method and Prediction of Atenolol Removal Using Modified Multiwalled Carbon Nanotubes Based on the Response Surface Method: Isotherm and Kinetics Studies

Atenolol (ATN) is a β-blocker drug extensively used to treat arrhythmias and high blood pressure. Because the human body cannot metabolize it completely, this drug has been commonly found in many environmental matrices. In the present study, the response surface method (RSM) was used for adsorption prediction of ATN on modified multiwalled carbon nanotubes (M-MWCNTs) by NaOCl and ultrasonic. The sensitivity analysis was done by the Monte Carlo method. Sensitivity analysis was performed to determine the effective parameter by the Monte Carlo simulator. Statistical analysis of variance (ANOVA) was performed by using the nonlinear second-order model of RSM. The influential parameters including contact time (min), adsorbent dosage (g/L), pH, and the initial concentration (mg/L) of ATN were investigated, and optimal conditions were determined. Kinetic of ATN adsorption on M-MWCNTs was evaluated using pseudo-first, pseudo-second-order, and intraparticle diffusion models. Equilibrium isotherms for this system were analyzed by the ISOFIT software. As per our result, optimum conditions in the adsorption experiments were pH 7, 60 min of contact time, 0.5 mg/L ATN initial concentration, and 150 mg/L adsorbent dose. In terms of ATN removal efficiency, coefficients of R2 and adjusted R2 were 0.999 and 0.998, respectively. Sensitivity analysis also showed that contact time has the greatest effect on increasing the removal of ATN. Pseudo-first-order (R2 value of 0.99) was the best kinetic model for the adsorption of ATN, and for isotherm, BET (AICC value of 3.27) was the best model that fit the experimental data. According to the obtained results from sensitive analysis, time was the most important parameter, and after that, the adsorbent dose and pH affect positively on ATN removal efficiency. It can be concluded that the modified multiwalled carbon nanotubes can be applied as one of the best adsorbents to remove ATN from the aqueous solution.

Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System

This study investigates chromium removal onto modified maghemite nanoparticles in batch experiments based on a central composite design. The effect of modified maghemite nanoparticles on the adsorptive removal of chromium was quantitatively elucidated by fitting the experimental data using artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modeling approaches. The ANN and ANFIS models, relating the inputs, i.e., pH, adsorbent dose, and initial chromium concentration to the output, i.e., chromium removal efficiency (RE), were developed by comparing the predicted value with that of the experimental values. The RE of chromium ranged from 49.58% to 92.72% under the influence of varying pH (i.e., 2.6–9.4) and adsorbent dose, i.e., 0.8 g/L to 9.2 g/L. The developed ANN model fits the experimental data exceptionally well with correlation coefficients of 1.000 and 0.997 for training and testing, respectively. In addition, the Pearson’s Chi-square measure (χ2) of 0.0004 and 0.0673 for the ANN and ANFIS models, respectively, indicated the superiority of ANN over ANFIS. However, a small discrepancy in the predictability of the ANFIS model was observed owing to the fuzzy rule-based complexity and overtraining of data. Thus, the developed models can be used for the online prediction of RE onto synthesized maghemite nanoparticles with different sets of input parameters and it can also predict the operational errors in the system.

Optimized synthesis of lignin sulfonate nanoparticles by solvent shifting method and their application for adsorptive removal of dye pollutant

Synthesis of value added products from wastes is of importance from different perspectives. Wood and paper industry produces tons of wastewaters that contains lignin. In this paper, we report a new approach, called solvent-shifting method, to synthesize lignin sulfonate nanoparticles (LS-NPs). The effective parameters on size of LS-NPs were carefully tuned and the size of LS-NPs was minimized by response surface methodology. The results suggested that LS-NPs with size of 53 nm can be synthesized at low lignin sulfonate concentration (0.28 g/mL), moderate surfactant concentration (0.32 g/mL) but relatively high anti-solvent content (92 mL of ethanol for 40 mL of the aqueous phase). The as-synthesized LS-NPs were characterized by different analytical techniques, where presence of various negatively charged functional groups on surface of LS-NPs was conformed. To investigate the potential of LS-NPs for adsorptive removal of pollutant molecules, basic red 2 (known as Safranin-O) was used as a model pollutant dye. The results suggested that the maximum removal occurs at alkaline pH, where there is strong electrostatic interactions between LS-NPs and cationic Safranin-O molecules. The adsorption capacity was 85.14 mg/gr, where the isotherm data was best described by Redlich-Peterson isotherm model. The kinetic data also revealed that the adsorption is very fast in the first 20 min, where there is three diffusional steps to complete the adsorption in 90 min. The results of this study could open up new window to the field of value-added products to synthesize waste-driven nanomaterials for environmental applications.

A reusable mesoporous adsorbent for efficient treatment of hazardous triphenylmethane dye wastewater: RSM-CCD optimization and rapid microwave-assisted regeneration

In this research, mesoporous calcium aluminate nanostructures (meso-CaAl2O4) were synthesized using a citric acid-assisted sol-gel auto-combustion process as the potential adsorbent to eliminate toxic triphenylmethane dye malachite green (MG) from synthetic/real effluent. The surface morphology of meso-CaAl2O4 was highly porous with nanometric size and non-homogeneous surface. The specific surface area, total pore volume, and BJH pore diameter of meso-CaAl2O4 were 148.5 m2 g-1, 1.39 cm3 g-1, and 19 nm, respectively. The meso-CaAl2O4 also showed a very high heat resistance, due to losing only 7.95% of its weight up to 800 °C, which is mainly related to the moisture loss. The optimal adsorption conditions were obtained based on response surface methods (RSM)-central composite design (CCD) techniques. The Langmuir isotherm model was used for fitting the adsorption measurements, which presented 587.5 mg g-1 as the maximum adsorption capacity of the dye. The data obtained from the adsorption kinetics model were found to correspond to the pseudo-second-order model. Also, the thermodynamic parameters including enthalpy change (ΔH°), entropy change (ΔS°), and Gibbs free energy change (ΔG°) indicated that MG dye adsorption by the meso-CaAl2O4 was feasible, endothermic, and occurred spontaneously. Furthermore, the meso-CaAl2O4 was regenerated by microwave irradiation under 900 W at 6 min, and the MG dye removal efficiency was remained over 90% after the five cycles of microwave regeneration.

Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping

Waste streams with high ammonia nitrogen (NH3-N) concentrations are very commonly produced due to human intervention and often end up in waterbodies with effluent discharge. The removal of NH3-N from wastewater is therefore of utmost importance to alleviate water quality issues including eutrophication and fouling. In the present study, vacuum thermal stripping of NH3-N from high strength synthetic wastewater was conducted using a rotary evaporator and the process was optimized and modeled using response surface methodology (RSM) and RSM–artificial neural network (ANN) approaches. RSM was first employed to evaluate the process performance using three independent variables, namely pH, temperature (°C) and stripping time (min), and the optimal conditions for NH3-N removal (response) were determined. Later, the obtained data from the designed experiments of RSM were used to train the ANN for predicting the responses. NH3-N removal was found to be 97.84 ± 1.86% under the optimal conditions (pH: 9.6, temperature: 65.5 °C, and stripping time: 59.6 min) and was in good agreement with the values predicted by RSM and RSM–ANN models. A statistical comparison between the models revealed the better predictability of RSM–ANN than that of the RSM. To the best of our knowledge, this is the first attempt comparing the RSM and RSM–ANN in vacuum thermal stripping of NH3-N from wastewater. The findings of this study can therefore be useful in designing and carrying out the vacuum thermal stripping process for efficient removal of NH3-N from wastewater under different operating conditions.

Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass

Heavy metal ions in aqueous solutions are taken into account as one of the most harmful environmental issues that ominously affect human health. Pb(II) is a common pollutant among heavy metals found in industrial wastewater, and various methods were developed to remove the Pb(II). The adsorption method was more efficient, cheap, and eco-friendly to remove the Pb(II) from aqueous solutions. The removal efficiency depends on the process parameters (initial concentration, the adsorbent dosage of T-Fe3O4 nanocomposites, residence time, and adsorbent pH). The relationship between the process parameters and output is non-linear and complex. The purpose of the present study is to develop an artificial neural networks (ANN) model to estimate and analyze the relationship between Pb(II) removal and adsorption process parameters. The model was trained with the backpropagation algorithm. The model was validated with the unseen datasets. The correlation coefficient adj.R² values for total datasets is 0.991. The relationship between the parameters and Pb(II) removal was analyzed by sensitivity analysis and creating a virtual adsorption process. The study determined that the ANN modeling was a reliable tool for predicting and optimizing adsorption process parameters for maximum lead removal from aqueous solutions.

Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches

In the present study, reactive red 198 (RR198) dye removal from aqueous solutions by adsorption using municipal solid waste (MSW) compost ash was investigated in batch mode. SEM, XRF, XRD, and BET/BJH analyses were used to characterize MSW compost ash. CNHS and organic matter content analyses showed a low percentage of carbon and organic matter to be incorporated in MSW compost ash. The design of adsorption experiments was performed by Box-Behnken design (BBD), and process variables were modeled and optimized using Box-Behnken design-response surface methodology (BBD-RSM) and genetic algorithm-artificial neural network (GA-ANN). BBD-RSM approach disclosed that a quadratic polynomial model fitted well to the experimental data (F-value = 94.596 and R² = 0.9436), and ANN suggested a three-layer model with test-R² = 0.9832, the structure of 4-8-1, and learning algorithm type of Levenberg-Marquardt backpropagation. The same optimization results were suggested by BBD-RSM and GA-ANN approaches so that the optimum conditions for RR198 absorption was observed at pH = 3, operating time = 80 min, RR198 = 20 mg L^-1 and MSW compost ash dosage = 2 g L^-1. The adsorption behavior was appropriately described by Freundlich isotherm, pseudo-second-order kinetic model. Further, the data were found to be better described with the nonlinear when compared to the linear form of these equations. Also, the thermodynamic study revealed the spontaneous and exothermic nature of the adsorption process. In relation to the reuse, a 12.1% reduction in the adsorption efficiency was seen after five successive cycles. The present study showed that MSW compost ash as an economical, reusable, and efficient adsorbent would be desirable for application in the adsorption process to dye wastewater treatment, and both BBD-RSM and GA-ANN approaches are highly potential methods in adsorption modeling and optimization study of the adsorption process. The present work also provides preliminary information, which is helpful for developing the adsorption process on an industrial scale.

Kinetic modeling and quasi-economic analysis of fermentative glycolipopeptide biosurfactant production in a medium co-optimized by statistical and neural network approaches

This study presents the kinetics of production of a glycolipopeptide biosurfactant in a medium previously co-optimized by response surface and neural network methods to gain some insight into its volumetric and specific productivities for possible scale-up towards industrial production. Significant kinetic parameters including maximum specific growth rate, µmax, specific substrate consumption rate, qs and specific biosurfactant yield, Yp/x were determined from logistic model parameters after comparison with other kinetic models. Results showed that bio-catalytic rates of lipase and urease reached exponential values within the first 12 h of fermentation leading to high specific rates of substrate consumption and bacterial growth. Volumetric biosurfactant production reached significantly high levels during prolonged stationary growth and specific urease activity. This suggests that glycolipopeptide biosynthesis may proceed through stationary phase transpeptidation of the glycolipid base. A high cross-correlation coefficient of 0.950 confirmed that substrate consumption and glycolipopeptide production occurred contemporaneously during the 66-h fermentation. The maximum biosurfactant concentration of 132.52 g/L, µmax of 0.292 h^-1, qp of 1.674 g/gDCW/h, rp of 2.008 g/(Lh) and Yp/x of 4.413 g/g predicted by the selected logistic model and a unit cost of €0.57/g glycolipopeptide in the optimized medium may lead to technical and economic benefits.

Magnetic nanocomposites for sustainable water purification-a comprehensive review

Numerous contaminants in huge amounts are discharged to the environment from various anthropogenic activities. Waterbodies are one of the major receivers of these contaminants. The contaminated water can pose serious threats to humans and animals, by distrubing the ecosystem. In treating the contaminated water, adsorption processes have attained significant maturity due to lower cost, easy operation and environmental friendliness. The adsorption process uses various adsorbent materials and some of emerging adsorbent materials include carbon- and polymer-based magnetic nanocomposites. These hybrid magnetic nanocomposites have attained extensive applications in water treatment technologies due to their magnetic properties as well as combination of unique characteristics of organic and inorganic elements. Carbon- and polymer-related magnetic nanocomposites are more adapted materials for the removal of various kinds of contaminants from waterbodies. These nanocomposites can be produced via different approaches such as filling, pulse-laser irradiation, ball milling, and electro-spinning. This comprehensive review is compiled by reviewing published work of last the latest recent 3 years. The review article extensively focuses on different approaches for producing various carbon- and polymer-based magnetic nanocomposites, their merits and demerits and applications for sustainable water purification. More specifically, use of carbon- and polymer-based magnetic nanocomposites for removal of heavy metal ions and dyes is discussed in detail, critically analyzed and compared with other technologies. In addition, commercial viability in terms of regeneration of adsorbents is also reviewed. Furthermore, the future challenges and prospects in employing magnetic nanocomposites for contaminant removal from various water sources are presented.

Preparation and Kinetic Studies of Cross-Linked Chitosan Beads Using Dual Crosslinkers of Tripolyphosphate and Epichlorohydrin for Adsorption of Methyl Orange

Preparation of cross-linked chitosan beads using dual crosslinkers of tripolyphosphate (TPP) and epichlorohydrin (ECH) for the adsorption and kinetic studies of methyl orange (MO) had been carried out. FTIR spectra showed that TPP could act as the protecting agent of the NH₂ group of chitosan and ECH reacted with the primary hydroxyl group of chitosan. Various concentrations of TPP, ECH, and immersing time in the TPP solution for bead formation were studied. The effect of pH and kinetics of adsorption were investigated to define the mechanism of adsorption and rate-limiting step. As a result, pH 3, 10% (w/v) TPP, 5% (v/v) ECH, and 12 h immersing time in TPP were selected as the optimum conditions for preparing the beads as indicated by the highest adsorption amount of MO. The cross-linked chitosan beads' adsorption capacity for MO under optimum condition was found to be 79.55 mg/g with the adsorption rate constant (k) of 1.29 × 10⁻³/min. Furthermore, it was found that a low concentration of ECH could maintain the stability of chitosan in acidic conditions, whereas the concentration of TPP and immersing time controlled pore size and morphology of chitosan beads. The mechanism of adsorption of MO was controlled by the pore and rigidity of cross-linked chitosan beads. Bulk diffusion acted as a rate-limiting step, and a high concentration of MO inhibited diffusion and adsorption itself.

Preparation of a GO/MIL-101(Fe) Composite for the Removal of Methyl Orange from Aqueous Solution

The composite material graphene oxide (GO)/MIL-101(Fe) was prepared by a simple one-pot reaction method. MIL-101(Fe) grown on the surface of a GO layer was confirmed by scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). The adsorption performance and the mechanism of MIL-101(Fe) and GO/MIL-101(Fe) for methyl orange (MO) were studied. The results have shown that the adsorption capacity of GO/MIL-101(Fe) for MO was significantly better than that of MIL-101(Fe), and its capacity was the highest when 10% GO was added. The Langmuir specific surface areas of MIL-101(Fe) and GO/MIL-101(Fe) were 1003.47 and 888.289 m²·g^-1, respectively. The maximum adsorption capacities of MO on MIL-101 (Fe) and 10% GO/MIL-101 (Fe) were 117.74 and 186.20 mg·g^-1, respectively. The adsorption isotherms were described by the Langmuir model, and the adsorption kinetic data suggested the pseudo-second order to be the best fit model. GO/MIL-101(Fe) can be reused at least three times.

Fe3O4@C Nanoparticles Synthesized by In Situ Solid-Phase Method for Removal of Methylene Blue

Fe3O4@C nanoparticles were prepared by an in situ, solid-phase reaction, without any precursor, using FeSO4, FeS2, and PVP K30 as raw materials. The nanoparticles were utilized to decolorize high concentrations methylene blue (MB). The results indicated that the maximum adsorption capacity of the Fe3O4@C nanoparticles was 18.52 mg/g, and that the adsorption process was exothermic. Additionally, by employing H2O2 as the initiator of a Fenton-like reaction, the removal efficiency of 100 mg/L MB reached ~99% with Fe3O4@C nanoparticles, while that of MB was only ~34% using pure Fe3O4 nanoparticles. The mechanism of H2O2 activated on the Fe3O4@C nanoparticles and the possible degradation pathways of MB are discussed. The Fe3O4@C nanoparticles retained high catalytic activity after five usage cycles. This work describes a facile method for producing Fe3O4@C nanoparticles with excellent catalytic reactivity, and therefore, represents a promising approach for the industrial production of Fe3O4@C nanoparticles for the treatment of high concentrations of dyes in wastewater.