Chitosan-based magnetic adsorbent for removal of water-soluble anionic dye: Artificial neural network modeling and molecular docking insights

Utilization of battery waste derived ZnO in the removal of dye from aqueous solution: A waste to wealth approach

Every year a huge amount of zinc carbon batteries is discarded as waste and the management of such waste has become a growing concern all over the world. However, from these waste carbon batteries different kinds of valuable materials could be recovered. On the other hand, different industries discharged large volumes of dye wastewater into the environment which has a profound impact on environment and as well as human health. In this study, ZnO was recovered from the waste carbon batteries through pyrometallurgy process and utilized it for the treatment of methylene blue and methyl orange dye water. The batch adsorption process was carried out to observe the effect of adsorbent dosage, pH, contact time, stirring speed and temperature. Under the obtained optimal conditions adsorption kinetics (Pseudo-first order and pseudo-second order) and adsorption isotherms (Langmuir, Freundlich and Temkin) were analyzed. The results disclosed that 0.5 g and 0.6 g of ZnO showed maximum removal efficiency for MB and MO dye solution (50 ppm) whereas pH 13 and 6 were the optimal for MB and MO respectively. Kinetic studies indicate that both the adsorption processes were pseudo-second order. It was also revealed that based on regression coefficient R2 value the adsorption of MB and MO on ZnO is followed Langmuir model. Furthermore, the findings revealed that the MO adsorption on ZnO is a chemical adsorption process and MB adsorption is a physical adsorption process.

Chitosan anchored nZVI bionanocomposites for treatment of textile wastewater: Optimization, mechanism, and phytotoxic assessment

In recent years, there has been a growing focus on treating textile wastewater due to its escalating threat to aquatic ecosystems and exposed communities. The present study investigates the adsorption efficacy of biopolymer functionalized nanoscale zero-valent iron (CS@nZVI) composite for the treatment of textile wastewater using the RSM-CCD model. The structure and morphology of CS@nZVI were characterized using XRD, FTIR, FESEM, and EDX. CS@nZVI was then evaluated for its adsorption potential in removing COD, color, and other physico-chemical parameters from textile wastewater. The results showed the high efficacy of CS@nZVI for COD and color removal from textile wastewater. Under optimal conditions (pH 6, contact time 60 min, and 1.84 g CS@nZVI), COD removal reached a maximum of 85.53%, and decolorization efficiency was found to be 89.73%. The coefficient of determination R2 (0.98) and AIC (269.75) values suggested quadratic model as the best-fitted model for optimizing the process parameters for COD removal. Additionally, the physico-chemical parameters were found to be within permissible limits after treatment with CS@nZVI. The influence of coexisting ions on COD removal followed the order PO43- > SO42- > Cl- >Na+ > Ca2+. The kinetics data fitted well with the pseudo-first-order reaction, indicating physisorption as the primary mechanism. The thermodynamic study revealed the endothermic nature of the removal process. Reusability tests demonstrated that great regeneration capacity of spent CS@nZVIafter five consecutive cycles. Furthermore, toxicological studies showed reduced toxicity in treated samples, leading to improved growth of Vigna radiata L. These findings suggest that CS@nZVI bionanocomposites could serve as an efficient, cost-effective, and eco-friendly remediation agent for the treatment of textile effluents, presenting significant prospects for commercial applications.

A computational investigation of DMSO/water separation through functionalized GO multilayer nanosheet membrane using molecular dynamics simulation and deep neural network model for membrane performance prediction

In this molecular dynamics (MD) simulation study, the separation of dimethyl sulfoxide (DMSO) from water was investigated using multilayer functionalized graphene oxide (GO) membranes. The GO nanosheets were modified with chemical groups (-F, -H) to alter their properties. The study analyzed the influence of pressure and functional groups on the separation rate. Additionally, a deep neural network (DNN) model was developed to predict membrane behavior under different conditions in water treatment processes. Results revealed that the fluorine-functionalized membrane exhibited higher permeation compared to the hydrogen-functionalized one, with potential of mean force (PMF) analysis indicating higher energy barriers for water molecules passing through the hydrogen-functionalized membrane. The study used density profile, water density map analysis, and radial distribution function (RDF) analysis to understand water and DMSO molecule interactions. The diffusion coefficient of water molecules was also calculated, showing higher diffusion in the fluorine-functionalized system. Overall, the findings suggest that functionalized GO membranes are effective for DMSO-water separation, with the fluorine-functionalized membrane showing superior performance. The DNN model accurately predicts membrane behavior, contributing to the optimization of membrane separation systems.

Surface modification of chitin nanofibers with dopamine as efficient nanosorbents for enhanced removal of dye pollution and metal ions

The development of environmentally friendly and low-cost adsorbents with high adsorption capacity remains a challenge. Herein, chitin nanofiber-polydopamine composite materials (CNDA) have been obtained by surface modification of chitin nanofiber using dopamine. According to the results of transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier Transform Infrared Spectrometer (FTIR), and X-ray photoelectron spectrometer (XPS), polydopamine have been successfully coated on the surface of chitin nanofiber (ChNF). The ability to remove methylene blue (MB) has been analyzed via standard adsorption experiments, indicating that the maximum adsorption capacity (qmax) can reach 196.6 mg/g at MB initial concentration of 50 mg/L. Most importantly, the adsorption kinetics, isotherm, and thermodynamics were used to investigate the MB adsorption mechanism on composites. This indicated that the polydopamine on the surface of chitin nanofiber (ChNF) plays an important role in the MB dye adsorption. Moreover, the removal ability of CNDA to metal ions has also been investigated, indicating high capacities for Fe3+, Mn2+, Cu2+, and Ni2+. Based on their biodegradability and good adsorption capacity, the CNDA composite material can be considered a promising adsorbent for wastewater treatment.

Phytoremediative adsorption methodologies to decontaminate water from dyes and organic pollutants

Persistent organic pollutants and dyes cause major problems during ecofriendly wastewater treatment. To overcome this huge problem, several techniques have been considered and in practice for the safe disposal of organic pollutants in recent years; some of them are discussed and compared herein. This review focuses on new trends for wastewater treatment and compares them with certain other techniques alongside their pros and cons; adsorption is considered the safest among them. Adsorbents derived from agri-wastes have good capacity for the removal of these contaminants owing to their great sorption capacity, high reusability, easy operation, etc. Sometimes they need some modifications for the removal of dyes, which are also discussed in this review. This capacity of adsorbents to chelate dye molecules can be affected by factors, such as pH, the concentration of dyes and adsorbents, and temperature of the system. pH has direct influence on the ionization potential and charge on the outer surface of adsorbents. The findings on isotherms, kinetics, and desorption of plant waste-based biomaterials that are safe for the ecosystem and user friendly and are used for hazardous contaminant removal from water are summarized in this review. Finally, conclusions and future perspectives are presented, and some other materials, such as CNTs and MOFs, are also discussed as efficient adsorbents for eliminating dyes from wastewater. Finally, it is predicted that the adsorption of dyes is a more feasible solution for this dye pollution problem.

Adsorption of Brilliant Green Dye onto a Mercerized Biosorbent: Kinetic, Thermodynamic, and Molecular Docking Studies

This study reports the valorization of pistachio shell agricultural waste, aiming to develop an eco-friendly and cost-effective biosorbent for cationic brilliant green (BG) dye adsorption from aqueous media. Pistachio shells were mercerized in an alkaline environment, resulting in the treated adsorbent (PS_NaOH). The morphological and structural features of the adsorbent were analyzed using scanning electron microscopy, Fourier transform infrared spectroscopy, and polarized light microscopy. The pseudo-first-order (PFO) kinetic model best described the adsorption kinetics of the BG cationic dye onto PS_NaOH biosorbents. In turn, the equilibrium data were best fitted to the Sips isotherm model. The maximum adsorption capacity decreased with temperature (from 52.42 mg/g at 300 K to 46.42 mg/g at 330 K). The isotherm parameters indicated improved affinity between the biosorbent surface and BG molecules at lower temperatures (300 K). The thermodynamic parameters estimated on the basis of the two approaches indicated a spontaneous (ΔG < 0) and exothermic (ΔH < 0) adsorption process. The design of experiments (DoE) and the response surface methodology (RSM) were employed to establish optimal conditions (sorbent dose (SD) = 4.0 g/L and initial concentration (C₀) = 10.1 mg/L), yielding removal efficiency of 98.78%. Molecular docking simulations were performed to disclose the intermolecular interactions between the BG dye and lignocellulose-based adsorbent.

Kinetics and Thermodynamics Study of Methylene Blue Adsorption to Sucrose- and Urea-Derived Nitrogen-Enriched, Hierarchically Porous Carbon Activated by KOH and H3PO4

Hierarchically porous nitrogen-enriched carbon materials synthesized by polymerization of sucrose and urea (SU) were activated by KOH and H₃PO₄ (SU-KOH and SU-H₃PO₄, respectively). Characterization was undertaken and the synthesized materials were tested for their ability to adsorb methylene blue (MB). Scanning electron microscopic images along with the Brunauer-Emmett-Teller (BET) surface area analysis revealed the presence of a hierarchically porous system. X-ray photoelectron spectroscopy (XPS) confirms the surface oxidation of SU upon activation with KOH and H₃PO₄. The best conditions for removing dyes utilizing both activated adsorbents were determined by varying the pH, contact time, adsorbent dosage, and dye concentration. Adsorption kinetics were evaluated, and the adsorption of MB followed second-order kinetics, suggesting the chemisorption of MB to both SU-KOH and SU-H₃PO₄. Times taken to reach the equilibrium by SU-KOH and SU-H₃PO₄ were 180 and 30 min, respectively. The adsorption isotherm data were fitted to the Langmuir, Freundlich, Temkin, and Dubinin models. Data were best described by the Temkin isotherm model for SU-KOH and the Freundlich isotherm model for SU-H₃PO₄. Thermodynamics of the adsorption of MB to the adsorbent was determined by varying the temperature in the range of 25-55 °C. Adsorption of MB increased with increasing temperature, suggesting that the adsorption process is endothermic. The highest adsorption capacities of SU-KOH and SU-H₃PO₄ (1268 and 897 mg g^-1, respectively) were obtained at 55 °C. Synthesized adsorbents were effective in removing MB for five cycles with some loss in activity. The results of this study show that SU activated by KOH and H₃PO₄ are environmentally benign, favorable, and effective adsorbents for MB adsorption.

Ultrasonic-Assisted Rapid Preparation of Sulfonated Polyether Ether Ketone (PEEK) and Its Testing in Adsorption of Cationic Species from Aqueous Solutions

Herein, we report a new approach for the sulfonation of polyether ether ketone (PEEK) following a shorter path of reaction undertaken at 60 °C under ultrasonication. The application of this method enabled the reduction of the reaction time from several hours to less than one hour, achieving a relevant sulfonation degree. The sulfonated-PEEK (SPEEK) was characterized by advanced chemical and physical instrumental methods. According to ¹H-NMR analysis, the degree of sulfonation of the polymer was equal to 70.3%. Advanced microscopy (SEM) showed that the fabricated SPEEK beads (2-4 mm) were porous inside with a log-normal distribution of pore sizes within the range 1.13-151.44 μm. As an application, the SPEEK polymer was tested for the adsorption of a cationic organic pollutant (Methylene blue, MB) from aqueous solutions. The equilibrium studies (isotherms) disclosed maximum adsorption capacities of 217 mg/g, 119 mg/g, and 68 mg/g at temperatures of 323 K, 313 K, and 300 K, respectively. The thermodynamic calculations indicated an endothermic effect (ΔH_ad = +11.81 kJ/mol) of the investigated adsorption process. The maximum removal efficiency of 99.14% was established by process optimization using the design of experiments strategy and data-driven modeling. Additionally, molecular docking simulations were performed to disclose the mechanism of interaction at the molecular level between the adsorbent (SPEEK) and pollutant.

Oxidized Biomass and Its Usage as Adsorbent for Removal of Heavy Metal Ions from Aqueous Solutions

Nowadays, very coarse wool fibers are considered waste biomass and are discarded at random or burned. Therefore, it is of actual interest to valorize coarse wool fibers as utile products. In this sense, we report herein an environmentally-friendly process for the preparation of a new material based on oxidized wool fibers and designed for efficient adsorption of heavy metals from wastewater. The morphology and the structure of the obtained product were characterized by scanning electron microscopy (SEM) coupled with an X-ray energy-dispersive module (EDX) and by Fourier-transform infrared spectroscopy (FTIR). Likewise, the performances of the oxidized wool fibers for the adsorption of heavy metal cations (Cu²⁺, Cd²⁺, Pb²⁺) from aqueous solutions were tested. The adsorption kinetics data were analyzed by applying the pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetic models. The equilibrium of the adsorption process was investigated by using the Freundlich and Langmuir isotherm models. According to the Langmuir isotherms registered at 300 K, the maximum adsorption capacities of the oxidized wool were found to increase from Cu²⁺ (9.41 mg/g) and Cd²⁺ (10.42 mg/g) to Pb²⁺ (30.71 mg/g). Consequently, the removal efficiency of metal ions was found to vary in the range of 96.8–99.7%. The thermodynamic parameters (e.g., enthalpy, entropy, and Gibbs free energy) were calculated and discussed.

Surface Modification of Biochar for Dye Removal from Wastewater

Nowadays, biochar is being studied to a great degree because of its potential for carbon sequestration, soil improvement, climate change mitigation, catalysis, wastewater treatment, energy storage, and waste management. The present review emphasizes on the utilization of biochar and biochar-based nanocomposites to play a key role in decontaminating dyes from wastewater. Numerous trials are underway to synthesize functionalized, surface engineered biochar-based nanocomposites that can sufficiently remove dye-contaminated wastewater. The removal of dyes from wastewater via natural and modified biochar follows numerous mechanisms such as precipitation, surface complexation, ion exchange, cation–π interactions, and electrostatic attraction. Further, biochar production and modification promote good adsorption capacity for dye removal owing to the properties tailored from the production stage and linked with specific adsorption mechanisms such as hydrophobic and electrostatic interactions. Meanwhile, a framework for artificial neural networking and machine learning to model the dye removal efficiency of biochar from wastewater is proposed even though such studies are still in their infancy stage. The present review article recommends that smart technologies for modelling and forecasting the potential of such modification of biochar should be included for their proper applications.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Does skin permeation kinetics influence efficacy of topical dermal drug delivery system?: Assessment, prediction, utilization, and integration of chitosan biomacromolecule for augmenting topical dermal drug delivery in skin

Skin permeation is an integral part of penetration of topical therapeutics. Zero order in addition to Higuchi permeation kinetic is usually preferred in topical drug delivery cargo. Penetration of therapeutic entities through epidermal barrier is a major challenge for scientific fraternity. Furthermore, penetration of therapeutic entities determines the transportation and ultimately therapeutic efficacy of topical dermal dosage forms. Apart from experimentation models, mathematical equations, in silico docking, molecular dynamics (MDs), and artificial neural network (Neural) techniques are being used to assess free energies and prediction of electrostatic attractions in order to predict the permeation phenomena of therapeutic entities. Therefore, in the present review, we have summarized the significance of kinetic equations, in silico docking, MDs, and ANN in assessing and predicting the penetration behavior of topical therapeutics through dermal dosage form. In addition, the role of chitosan biomacromolecule in modulating permeation of topical therapeutics in skin has also been illustrated using computational techniques.

Determination of acidity in metal incorporated zeolites by infrared spectrometry using artificial neural network as chemometric approach

The NH₃-TPD analysis is a costly and tedious method to determine zeolites acidity. Thus, to do so, FTIR spectroscopy was quantitatively used as a fast and cost-effectively method. Back-propagation artificial neural network (BP-ANN) was used for the analysis of multivariate base on the characteristic absorbance of 11 zeolite samples after metal substitution in the ~3612 cm^-1 region. The successive projection algorithm (SPA) was conducted for the uninformative variable elimination and feature selection strategies. The effect of pre-processing methods (e.g. MC and MSC) was examined. It is observed after using MSC for minimizing the light scattering effect and signal-to-noise correction, the minimum mean squared error (MSE) value of the testing set data reduced from 5.36 × 10^-2 to 2.19 × 10^-4 and R_tot increases from 0.91 to 0.99. Also, the results of nonparametric Wilcoxon t-test and Sign test methods also confirmed that there is no clear difference between the zeolite acidity obtained by two conventional method and the proposed method.

Acid Red 66 Dye Removal from Aqueous Solution by Fe/C-based Composites: Adsorption, Kinetics and Thermodynamic Studies

The presence of synthetic dyes in water causes serious environmental issues owing to the low water quality, toxicity to environment and human carcinogenic effects. Adsorption has emerged as simple and environmental benign processes for wastewater treatment. This work reports the use of porous Fe-based composites as adsorbents for Acid Red 66 dye removal in an aqueous solution. The porous FeC and Fe/FeC solids were prepared by hydrothermal methods using iron sulfates and sucrose as precursors. The physicochemical properties of the solids were evaluated through X-ray diffraction (XRD), Scanning electron microscopy coupled with Energy dispersive spectroscopy (SEM-EDS), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared s (FTIR), Raman and Mössbauer spectroscopies, nitrogen adsorption–desorption isotherms, Electron Paramagnetic Resonance (EPR) and magnetic saturation techniques. Results indicated that the Fe species holds magnetic properties and formed well dispersed Fe₃O₄ nanoparticles on a carbon layer in FeC nanocomposite. Adding iron to the previous solid resulted in the formation of γ-Fe₂O₃ coating on the FeC type structure as in Fe/FeC composite. The highest dye adsorption capacity was 15.5 mg·g⁻¹ for FeC nanocomposite at 25 °C with the isotherms fitting well with the Langmuir model. The removal efficiency of 98.4% was obtained with a pristine Fe sample under similar experimental conditions.

Removal of methylene blue via bioinspired catecholamine/starch superadsorbent and the efficiency prediction by response surface methodology and artificial neural network-particle swarm optimization

This paper demonstrates coupling of the artificial neural network (ANN) technique with the particle swarm optimization (PSO) method and compares the performance of ANN-PSO with response surface methodology (RSM) in prediction of the adsorption of methylene blue (MB) by a novel bio-superadsorbent. To this, a starch-based superadsorbent was synthesized using acrylic acid and acryl amid polymers and then catecholamine functional groups were combined onto the surface with oxidative polymerization of dopamine. The adsorption of MB was considered as a function of pH, dye concentration, and contact time. The best topology of the ANN was found to be 3-7-1, and prediction model of the adsorption capacity was demonstrated as a matrix of explicit equations. ANN-PSO is more accurate than RSM. The results revealed that the root-mean-square error, correlation coefficient, and normalized standard deviation for the ANN-PSO are 22.46, 0.99, and 16.83, respectively, while for RSM are 82.89, 0.98, and 65.41, respectively.