Preparation of nanomaterials for the ultrasound-enhanced removal of Pb2+ ions and malachite green dye: Chemometric optimization and modeling

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.

Sonoactivated Nanomaterials: A potent armament for wastewater treatment

The world is currently facing a critical issue of water pollution, with wastewater being a major contributor. It comes from different types of pollutants, including industrial, medical, agricultural, and domestic. Effective treatment of wastewater requires efficient degradation of pollutants and carcinogens prior to discharge. Commonly used methods for wastewater treatment include filtration, adsorption, biodegradation, advanced oxidation processes, and Fenton oxidation, among others.The sonochemical effect refers to the decomposition, oxidation, reduction, and other reactions of pollutant molecules in wastewater upon ultrasound activation, achieving pollutants removal. Furthermore, the micro-flow effect generated by ultrasonic waves creates tiny bubbles and eddies. This significantly increases the contact area and exchange speed of pollutants and dissolved oxygen, thereby accelerating pollutant degradation. Currently, ultrasonic-assisted technology has emerged as a promising approach due to its strong oxidation ability, simple and cheap equipments, and minimal secondary pollution. However, the use of ultrasound in wastewater treatment has some limitations, such as high energy consumption, lengthy treatment time, limited water treatment capacity, stringent water quality requirements, and unstable treatment effects. To address these issues, the combination of enhanced ultrasound with nanotechnology is proposed and has shown great potential in wastewater treatment. Such a combination can greatly improve the efficiency of ultrasonic oxidation, resulting in an improved performance of wastewater purification. This article presents recent progress in the development of sonoactivated nanomaterials for enhanced wastewater disposal. Such nanomaterials are systematically classified and discussed. Potential challenges and future prospects of this emerging technology are also highlighted.

Full factorial design and dynamic modelling of silent and ultrasound-assisted lead and cadmium removal by porous biosorbent

Present work aimed to analyse single and competitive lead and cadmium batch adsorption, using experimental studies and mathematical modelling. The experiments were conducted in silent and ultrasound-assisted systems, in aqueous environment, using grinded hazelnut shells as porous biosorbent. The influence of process factors (pH, adsorbent concentration, adsorbent particle size, and initial species concentration in liquid phase) on species removal efficiency was evaluated when process equilibrium was attained. The statistical study, following a 2⁴ factorial experimental design, allowed the development of a model to predict variables influence. Based on the obtained results a deeper analysis of the separation efficiency, depending on process factors, was conducted. The dynamic study was performed based on experimentally obtained removal rates, modelled considering species diffusion, with reversible kinetics of sorption inside solid particles. Hence, the dynamics of removal efficiency was determined for several representative experiments. The equilibrium isotherms data, best fitted by an appropriate Langmuir model, were used in the dynamic model to reduce the number of model parameters which normally require experimental identification.

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.

RSM, ANN-GA and ANN-PSO modeling of SDBS removal from greywater in rural areas via Fe2O3-coated volcanic rocks

Decontamination and reuse of greywater in rural areas has attracted increasing attention. Typical contaminants in grey water are SDBS, which has a stubborn molecular structure. In this study, Fe₂O₃-coated volcanic rocks (Fe₂O₃-VR) prepared from FeCl₃ solution by a heating evaporation method can reach 95% removal of SDBS, which is 80% higher than before. The effect of contact time, pH, initial concentration, FeCl₃ solution concentration, adsorbent dosage and calcination temperature on the removal rate was researched and modeled by response methodology (RSM) and artificial neural network (ANN). Based on the univariate test, the Box-Behnken design method was used to establish the data sample, which represented a quadratic polynomial model with p-value <0.001, R² = 0.9872, while the ANN model has the better performance with R² = 0.9961. The weights of the BP-ANN model were further analyzed using the Garson equation, and the results showed that the validity ranking of the variables was as follows: contact time (37.31%) > calcination temperature (29.43%) > dosage (24.44%) > initial concentration (17.18%) > FeCl₃ solution concentration (17.18%) > pH (11.56%). Genetic algorithm (GA) and particle swarm optimization (PSO) were selected to optimize the process parameters. The results showed that ANN-PSO methodology presented a satisfactory alternative and the predicted removal efficiency was 99.9982% with relative error = 0.2230. The optimum level of contact time, pH, initial SDBS concentration, FeCl₃ solution concentration, adsorbent dosage and calcination temperature is 136.45 min, 5.64, 22.4 mg L⁻¹, 0.3 mol L⁻¹, 83.21 g L⁻¹, 274.02 °C, respectively. Moreover, Fe₂O₃-VR was characterized via instrumental analyses (SEM-EDS, FTIR, XRD, BET).

This paper provides a new method for SDBS removal and parameter optimization of the adsorption process using RSM and ANN models.

Green Preparation of Robust Hydrophobic β-Cyclodextrin/Chitosan Sponges for Efficient Removal of Oil from Water

A relatively straightforward green method to fabricate robust hydrophobic sponges for effective removal of oil pollutants and other organic contaminants was developed. These sponges were constructed from bio-sources: citronellal and palmitic acid-modified aminoethyl cyclodextrin-sodium phytate-chitosan (ACCTCS). The modified sponge exhibited desirable mechanical properties and strong hydrophobicity with a water contact angle (WCA) of 147.8°. Scanning electron microscopy showed that the ACCTCS sponge had a highly porous structure that was particularly suitable for organic component absorption. The sponge exhibited excellent absorption capacities for n-hexane, trichloromethane, vacuum pump oil, and peanut oil (47.9, 32.3, 32.6, and 32.2 g/g, respectively). The removal rate of oil was more than 80% (>26.2 g/g) after 10 absorption-desorption cycles. The ACCTCS sponge also showed good oil/water and organic components/water separation performance. The bio-source materials, green preparation method, and new absorbed-oil recovery strategy provided a novel pathway to construct multifunctional absorbents for oil/water separation in industrial wastewater.

Porous Carbon Nanofibers with Heteroatoms Doped by Electrospinning Exhibit Excellent Acetone and Carbon Dioxide Adsorption Performance: The Contributions of Pore Structure and Functional Groups

Rich chemical properties and a well-developed pore structure are the key factors of porous materials for gas storage. Herein, rich heteroatom-doped porous carbon nanofibers (U₁K₂-X) with a large surface area were prepared by electrospinning followed by potassium hydroxide (KOH) activation. Low-cost urea was chosen as the nitrogen source and structural guiding agent. U₁K₂-X have a high specific surface area (628-2688 m² g^-1), excellent pore volume (0.468-1.571 cm³ g^-1), and abundant nitrogen (2.5-12.8 atom %) and oxygen (4.5-12.5 atom %) contents. Acetone and carbon dioxide were used as target adsorbents to evaluate the adsorption properties of U₁K₂-X by experiments. These U₁K₂-X exhibit excellent adsorption performance (260.03-955.74 mg g^-1, 25 °C, 18 kPa) and multilayer adsorption (the adsorption layer number n > 2) for acetone, which is mainly attributed to the large specific surface area and pore volume. Besides this, the carbon dioxide uptake reached 2.73-3.34 mmol g^-1 at 25 °C. This was attributed to the combination of high nitrogen-oxygen contents and microporous structure. Furthermore, U₁K₂-X show the desirable repeatability. This study provides a new direction for the preparation of heteroatom-doped porous carbon nanofibers, which will be a promising material for gas adsorption.

Super adsorption performance of carboxymethyl cellulose/copper oxide-nickel oxide nanocomposite toward the removal of organic and inorganic pollutants

A novel nanocomposite bead based on polymeric matrix of carboxymethyl cellulose and copper oxide-nickel oxide nanoparticles was synthesized, characterized, and applied for adsorptive removal of inorganic and organic contaminants at trace level of part per million (mgL^-1) from aqueous sample. Carboxymethyl cellulose/copper oxide-nickel oxide (CMC/CuO-NiO) adsorbent beads were selective toward the removal of Pb(II) among other metal ions. The removal percentage of Pb(II) was more than 99% with 3 mgL^-1. The waste beads after Pb (II) adsorption (Pb@CMC/CuO-NiO) and CMC/CuO-NiO nanocomposite beads were employed as adsorbents for removing of various dyes. It was found that Pb@CMC/CuO-NiO can be reused as adsorbent for the removal of Congo Red (CR), while CMC/CuO-NiO nanocomposite beads were more selective for removal of Eosin Yellow (EY) from aqueous media. The adsorption of CR and EY was optimized, and the removal percentages were 93% and 96.4%, respectively. The influence of different parameters was studied on the uptake capacity of Pb(II), CR, and EY, and lastly, the CMC/CuO-NiO beads exhibited responsive performance in relation to pH and other parameters. Thus, the prepared CMC/CuO-NiO beads were found to be a smart material which is effective and played super adsorption performance in the removal of Pb(II), CR, and EY from aqueous solution. These features make CMC/CuO-NiO beads suitable for numerous scientific and industrial applications and may be used as an alternative to high-cost commercial adsorbents.

Adsorption and Release of Sulfamethizole from Mesoporous Silica Nanoparticles Functionalised with Triethylenetetramine

Mesoporous silica nanoparticles (MSN) were synthesised and functionalised with triethylenetetramine (MSN-TETA). The samples were fully characterised (transmission electron microscopy, small angle X-ray scattering, Fourier transform infrared spectroscopy, thermogravimetric analysis, zeta potential and nitrogen adsorption/desorption isotherms) and used as carriers for the adsorption of the antimicrobial drug sulphamethizole (SMZ). SMZ loading, quantified by UV-Vis spectroscopy, was higher on MSN-TETA (345.8 mg g-1) compared with bare MSN (215.4 mg g-1) even in the presence of a lower surface area (671 vs. 942 m2 g-1). The kinetics of SMZ adsorption on MSN and MSN-TETA followed a pseudo-second-order model. The adsorption isotherm is described better by a Langmuir model rather than a Temkin or Freundlich model. Release kinetics showed a burst release of SMZ from bare MSN samples (k1 = 136 h-1) in contrast to a slower release found with MSN-TETA (k1 = 3.04 h-1), suggesting attractive intermolecular interactions slow down SMZ release from MSN-TETA. In summary, the MSN surface area did not influence SMZ adsorption and release. On the contrary, the design of an effective drug delivery system must consider the intermolecular interactions between the adsorbent and the adsorbate.

Innovative method for minimization of waste containing Fe, Mn and Ti during comprehensive utilization of vanadium slag

More than 1.2 million tons of tailings containing approximately 30 wt% of Fe from traditional vanadium extraction processes are discarded every year as solid waste, which waste resources. In order to achieve effective and green utilization of waste, a novel process was proposed to keep Cr and V at Cr³⁺ and V³⁺ during extraction by using AlCl₃-NaCl-KCl molten salt in Ar gas atmosphere to control the valuable elements (Cr, V, Mn and Fe) from oxidized. The morphological features of vanadium slag reacted in the temperature range from 200 °C to 800 °C and volatilization of samples under different AlCl₃/slag ratios were analyzed. Meanwhile, the chlorinated kinetics of V, Cr, Mn and Fe in vanadium slag were systemically investigated in temperature range of 850 °C-950 °C. The kinetics investigation indicated that the chlorination processes of Fe and Mn were restricted by mass transfer in product layer (Al-Si-O mixture) and the chlorination processes of V and Cr were controlled by surface reaction. The apparent activation energies for Fe, Mn, V, and Cr are 105.28 kJ/mol, 94.26 kJ/mol, 64.64 kJ/mol, and 63.30 kJ/mol, respectively. After chlorination, the separation of metal chlorides was achieved. TiCl₄ is hydrolyzed to obtain TiO₂. Mn can be separated from VCl₃, CrCl₃, FeCl₂, and MnCl₂ by controlling the electrolytic voltages. Fe-V-Cr alloy was obtained by electrolysis at 2.3 V.

Ultrasound-irradiated synthesis of 3-mercaptopropyl trimethoxysilane-modified hydroxyapatite derived from fish-scale residues followed by ultrasound-assisted organic dyes removal

We report a novel method for the synthesis of 3-mercaptopropyl trimethoxysilane-modified hydroxyapatite (FHAP-SH) derived from fish-scale residues by using ultrasound irradiation. Scanning electron microscopy, transmission electron microscopy, energy-dispersive spectroscopy, X-ray diffraction, and Fourier transform infrared spectroscopy were used for the FHAP-SH characterization. Then, the organic dye adsorption on the FHAP-SH was monitored through an ultrasound process. After the dye removal optimization, significant improvements were observed in the maximum adsorption capacities for Congo Red (CR, 500 mg g^-1), Coomassie Brilliant Blue G 250 (CB, 235 mg g^-1), and Malachite Green (MG, 625 mg g^-1). The adsorption behaviors of these dyes were fitted by using the Langmuir isotherm model with a high coefficient of determination values ranging from 0.9985 to 0.9969. The adsorption of the three dyes onto FHAP-SH was an endothermic process based on the adsorption thermodynamics model, while the adsorption kinetics analysis of the dyes presented a good alignment with the pseudo-second-order kinetics. The FHAP-SH exhibits a remarkably high adsorption capacity, is inexpensive, and fulfills the ecofriendly requirements of dye wastewater treatment, especially in the textile industry.

Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia

Lead (Pb) is a primary toxic heavy metal (HM) which present throughout the entire ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial intelligence (AI) models include overfitting, normalization, validation against classical AI models, and lack in learning/technology transfer. This study explores the extreme gradient boosting (XGBoost) model as a superior SuperLearning (SL) algorithms for Pb prediction. The proposed model was examined using historical data at the Bramble and Deception Bay (BB and DB) stations, Australia. The model was trained at one of the stations and transferred to a cross-station and vice versa. XGBoost showed higher reliability with less declination in (R²: coefficient of determination), i.e., 0.97 % over the testing phase, among others models at BB. At the cross-station (DB), the performance of the XGBoost model was decreased by 2.74 % (R²) against random forests (RF). The mean absolute error (MAE) observed 40 % (XGBoost) and 47 % (RF) less than artificial neural network (ANN). The XGBoost model performance declined by 3.44 % (R²) over testing (DB), which is minor among validated models. At the cross-station (BB), the XGBoost model showed the least decrement in terms of R², i.e., 7.99 % against the ANN (8.31 %), RF (10.26 %), and support vector machine (SVM, 36.19 %).

Preparation, Performances and Mechanisms of Co@AC Composite for Herbicide Atrazine Removal in Water

In this study, a high-performance adsorbent Co@AC was prepared by loading cobalt ions (Co2+) on activated carbon (AC) via solution impregnation and high-temperature calcination technology, and was used to remove atrazine in water. The preparation factors on the adsorbent properties were studied, and the characteristics were analyzed by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and Fourier transform infrared spectrometer (FTIR). The results showed that Co@AC possessed the best performance when the factors were 7.0% of Co2+ (w/v), 7.0 h of immersing time, 500 °C of calcination temperature and 4.0 h of calcination time. The adsorption conditions and mechanisms for atrazine removal by Co@AC were also studied scientifically. As the conditions were pH 4.0, reaction time 90 min and temperature 25 °C, Co@AC had the largest adsorption capacity, which was 92.95 mg/g, and the maximum removal rate reached 94.79%. The correlation coefficient of the Freundlich isotherm was better than that of the Langmuir isotherm, and the adsorption process followed the pseudo-second-order kinetic model. Cycle experiments showed that the removal efficiency of atrazine by Co@AC remained above 85% after five repeated experiments, indicating that Co@AC showed a strong stable performance and is a promising material for pesticides removal.

Simultaneous removal of organics and heavy metals from industrial wastewater: A review

The co-existence of heavy metals and organics in industrial effluents is a prevalent problem. These pollutants usually have dissimilar compositions and properties, making their complete removal very tedious even with the use of conventional methods. In some cases, organics and heavy metals usually exist in a mixed matrix in industrial wastes. This poses harmful health risks to humans, aquatic lives and the entire ecosystem, because majority of these mixed pollutants amass in water in concentrations which are more than the permissible discharge limits in the environment. Therefore, it is necessary to remove these pollutants in order to prevent them from contaminating both the surface and ground water. Although, the removal of organic compounds and heavy metals (such as Hg, Pb, Cd, As and Cr) could be easily achieved individually, however, these pollutants exist together in many industrial effluents and even in surface waters. Hence the complete removal of these pollutants concurrently in a polluted system is the focus of this study. Several technologies have been used for the simultaneous removal of organics and heavy metal pollutants from water, which includes adsorption, ion exchange, photocatalysis, and coagulation. The success of these techniques depends on the water matrices and the choice of water treatment media such as adsorbents, resins, photocatalysts, and coagulants. The advantages and limitations of these technologies together with their respective mathematical modelling is critically examined in this review. Finally, the effect of joint existence of organic pollutants and heavy metals on the removal efficiency were examined in addition to the mathematical models that discusses the mechanisms of their combine elimination.

Fast and efficient removal of Pb(II) ion and malachite green dye from wastewater by using magnetic activated carbon-cobalt nanoparticles

A high-surface-area and inexpensive activated carbon has been produced from lemon peel using chemical activation with H₃PO₄ at 500 °C in a N₂ atmosphere. Afterwards, the synthesized cobalt nanoparticles using coprecipitation method were adsorbed on the activated carbon surface, and as a result magnetic activated carbon was obtained. Sample characterization has been assessed via X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, nitrogen adsorption-desorption and magnetic properties. It was found that magnetic activated carbon-cobalt nanoparticles (MAC/Co) synthesized had a high saturation magnetization. The MAC/Co revealed super-paramagnetic behaviors at room temperature, and have been readily isolated from solution by using an exterior magnet. Next, adsorption behavior of malachite green and Pb(II) onto the generated MAC/Co has been examined. Sorption kinetics and equilibrium have been studied using batch procedure. The kinetic and isothermal adsorption results were matched completely with the Elovich and Langmuir models, respectively. Based on the Langmuir model, the highest adsorption capacities of malachite green dye and Pb(II) ion respectively were 263.2 and 312.5 mg g^-1 at room temperature. Based on the results, the MAC/Co is a probable economic and effective adsorbent that can be employed as a new adsorbent to remove malachite green dye and Pb(II) from wastewater.

Tackling environmental challenges in pollution controls using artificial intelligence: A review

This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.

In Situ Copolymerized Polyacrylamide Cellulose Supported Fe3O4 Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies

The inimical effects associated with heavy metals are serious concerns, particularly with respect to global health-related issues, because of their non-ecological characteristics and high toxicity. Current research in this area is focused on the synthesis of poly(acrylamide) grafted Cell@Fe3O4 nanocomposites via oxidative free radical copolymerization of the acrylamide monomer and its application for the removal of Pb(II). The hybrid material was analyzed using different analytical techniques, including thermogravimetric analysis (TGA), Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Brunauer-Emmett-Teller (BET) analysis. The efficacious impact of variable parameters, including contact time, pH, material dose, initial Pb(II) concentration, and the temperature, was investigated and optimized using both batch and artificial neural networks (ANN). Surface digestion of metal ions is exceedingly pH-dependent, and higher adsorption efficiencies and adsorption capacities of Pb(II) were acquired at a pH value of 5. The acquired equilibrium data were analyzed using different isotherm models, including Langmuir, Freundlich, Temkin, and Redlich-Peterson models. In this investigation, the best performance was obtained using the Langmuir model. The maximum adsorption capacity of the material investigated via monolayer formation was determined to be 314.47 mg g-1 at 323 K, 239.74 mg g-1 at 313 K, and 100.79 mg g-1 at 303 K.

Determination of adsorption characteristics of synthetic NaX nanoparticles

Zeolite nanoparticles were synthesized by means of a hydrothermal method and the sorption performances were tested in batch experiments using single and binary components of Pb²⁺, Ni²⁺and Cu²⁺ synthetic solutions. Fast adsorption was observed and the majority of sorption occurred within the first 5 min for each case of metal cations. Percentage metal removal increased with the adsorbent dosage and it was adversely affected by the initial metal concentrations. XRD, FTIR-ATR and XPS analyses revealed that the removal of metal ions occurred mainly on the basis of ion-exchange. Equilibrium sorption data were best described by Sips isotherm model and the maximum attainable metal amount on the NaX were estimated as 1.23, 1.76 and 2.20 mmol/g for the Pb²⁺, Ni²⁺, and Cu²⁺ cations, respectively. A single step desorption process conducted in NaCl solution seemed to be practically applicable for regeneration and after three adsorption/desorption cycles, 79, 76 and 48% of initial sorption capacity of NaX were preserved for lead, nickel and copper ions, respectively. Single isotherm parameters used for the prediction of binary equilibrium data were satisfactory. Binary sorption experiments for Pb²⁺-Ni²⁺, Pb²⁺-Cu²⁺ and Ni²⁺-Cu²⁺ couples reflected that the presence of secondary ions decreased the uptake of the primary one. Lead exhibited greater inhibition of the sorption of nickel and copper, demonstrating the stronger affinity of NaX for Pb²⁺. Extended Freundlich model best described all the three binary metal systems. Adsorption experiments carried out in real wastewater demonstrated that NaX nanoparticle has a high affinity for all the cations except copper.

Iron oxide Permeated Mesoporous rice-husk nanobiochar (IPMN) mediated removal of dissolved arsenic (As): Chemometric modelling and adsorption dynamics

Adsorption based technologies are most widely used to mitigate the global predominance of heavy-metal groundwater contaminants like Arsenic (As), owing to their high efficiency and economic operation. The current study involves the optimization of Iron oxide Permeated Mesoporous rice-husk nanobiochars (IPMN) for As removal, which were synthesized through a chemically amended pyrolytic approach. The IPMN variants were screened based on preliminary OVAT (one-variable-at-a-time) studies for As removal. Chemometric investigations employing a central composite design matrix of Response surface methodology was further used to understand the influence of the process parameters on the adsorption of As on the most efficient IPMN variant. A Multi-Layered-Perceptron based artificial neural network was further used to confirm the veracity of the experimental and predictive conditions, to derive the optimal condition for the best adsorption efficiency. In addition, the dynamics of As adsorption by the optimal IPMN variant was modelled using pseudo-first-order (Lagergren) and pseudo-second-order (Ho) rate kinetic equations followed by isotherm studies using non-linear regression of Langmuir, Freundlich and Sips adsorption isotherms. The IPMNs have an appreciably higher uptake capacity (>90%) for dissolved As, as compared to the native milled rice husk (∼20%), alongside a substantial recyclability, thereby establishing their potential as a highly efficient, economical and sustainable nanobiochar for As removal.

Facile synthesis of acid-modified UiO-66 to enhance the removal of Cr(VI) from aqueous solutions

The adsorption behavior and mechanism of Cr(VI) on different acid-modified UiO-66s (Form-UiO-66 and Ac-UiO-66) were systematically investigated for the first time through a series of characterizations, and theoretical calculations of batch experiments. The characterization results demonstrate that acid-modified UiO-66 exhibited a larger specific surface area than did unmodified UiO-66. In addition, since the regulator (formic acid) of Form-UiO-66 was the stronger competition, the specific surface area of Form-UiO-66 (1138 m² g^-1) was larger than that of Ac-UiO-66 (915 m² g^-1). Under optimal experimental conditions, the maximum adsorption capacity of Cr(VI) was 243.9 mg g^-1 on Form-UiO-66, and 151.52 mg g^-1 on Ac-UiO-66, which was far higher than on the reported unmodified UiO-66 (36.4 mg g^-1). The results of pH testing, zeta potential, and X-ray photoelectron spectroscopy analysis indicate that Cr(VI) ions were fixed to adsorbent surfaces via electrostatic adsorption. Acid-modified UiO-66 increased the surface active site via the increase in its specific surface area to enhance adsorption capacity of Cr(VI). These results indicated that both the surface charge and specific surface area of the adsorbent primarily determined the Cr(VI) adsorption capacity. Acid modified UiO-66 exhibited enhanced adsorption capacity, stability, and regeneration, compared to traditional adsorbents, and these results provide new insights into adsorption by MOFs.

Process optimisation for green synthesis of zero-valent iron nanoparticles using Mentha piperita

The potential of Mentha piperita in the iron nanoparticles (FeNPs) production was evaluated for the first time. The influences of the variables such as incubation time, temperature, and volume ratio of the extract to metal ions on the nanoparticle size were investigated using central composite design. The appearance of SPR bands at 284 nm in UV-Vis spectra of the mixtures verified the nanoparticle formation. Incubating the aqueous extract and metal precursor with 1.5 volume ratio at 50°C for 30 min leads to the formation of the smallest nanoparticles with the narrowest size distribution. At the optimal condition, the nanoparticles were found to be within the range of 35-50 nm. Experimental measurements of the average nanoparticle size were fitted well to the polynomial model satisfactory with R2 of 0.9078. Among all model terms, the linear term of temperature, the quadratic terms of temperature, and mixing volume ratio have the significant effects on the nanoparticle average size. FeNPs produced at the optimal condition were characterised by transmission electron microscopy, thermogravimetry analysis (TGA), and Fourier-transform infrared spectroscopy. The observed weight loss in the TGA curve confirms the encapsulation of FeNPs by the biomolecules of the extract which were dissociated by heat.

Acrylic Acid-Functionalized Metal-Organic Frameworks for Sc(III) Selective Adsorption

The increasing demand for rare-earth elements (REEs) due to their extensive high-tech applications has encouraged the development of new sustainable approaches for REE recovery and separation. In this work, a series of acrylic acid-functionalized metal-organic framework materials (named as y-AA- x@MIL-101s) were prepared and used for selective adsorption of Sc(III). The adsorbent was characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, nitrogen adsorption, X-ray photoelectron spectroscopy, and zeta potential and surface functional-group titration. The adsorption isotherm and kinetics data were accurately described by the Langmuir and pseudo-second-order models. The adsorption capacity of the material for Sc(III), Nd(III), Gd(III), and Er(III) was 90.21, 104.59, 58.29, and 74.94 mg g^-1, respectively. Importantly, the adsorbent was better for selective recovery of Sc(III) not only from the 16 REE mixed system but also the Cu(II), Zn(II), Mn(II), Co(II), and Al(III) coexistence solution. Except for Sc(III), the material displayed high affinity for Nd(III) in the light rare-earth mixture and for Gd(III) in the middle rare-earth mixture. All in all, this study provides a new method for separation and recovery of REEs, which makes this work highly significant in separation and enrichment.

Unary and binary adsorption studies of lead and malachite green onto a nanomagnetic copper ferrite/drumstick pod biomass composite

Modern-day practices are the major contributors in water quality deterioration, consequently results in clean water scarcity. Herein, co-precipitation procedure was adopted to develop a nanomagnetic copper ferrite/drumstick pod biomass (CuFe₂O₄/DC) composite, which was characterized, and optimized to sequester malachite green (MG) and lead (Pb(II)) in unary and binary systems from aqueous environment. Mesoporous CuFe₂O₄/DC surface with 16.96 m²/g BET surface area and acid functionalities predominance was observed. Under the studied experimental conditions, MG adsorption on CuFe₂O₄/DC in unary system was comparatively higher than that of Pb(II). MG and Pb(II) equilibrium results were fitted to Langmuir isotherm model, their respective maximum monolayer adsorption capacities at 328 K being 952.4 and 921.1 mg/g. On the other hand, binary system (in presence of MG) fastened Pb(II) adsorption kinetics and increased its uptake capacity. Additionally, humic acid (HA) matrix enhanced Pb(II) adsorption kinetics. Recovery studies showed maximal MG and Pb(II) elution with C₂H₅OH and 0.1 mol/L HCl, respectively. An 82.7% drop in Pb(II) adsorption was found after the first regeneration cycle, while only 17.6% fall in MG adsorption was witnessed after five consecutive regeneration cycles. Hence, it could be concluded that CuFe₂O₄/DC is a cost-effective and promising adsorbent for an efficient and rapid removal of Pb(II) and MG from both unary and binary systems.

Adsorption of cadmium and lead in wastewater by four kinds of biomass xanthates

This study determined the adsorption of Cd²⁺ and Pb²⁺ (100 mg·L^-1 of each) in simulated wastewater by biomass xanthates made from starch, chitosan, wheat stalk and corn stalk. The results showed that the adsorption efficiency of Pb²⁺ and Cd²⁺ ions followed the order: corn stalk xanthate > wheat stalk xanthate ≥ chitosan xanthate > starch xanthate. The results of kinetic modeling showed that the adsorption process was characterized by physical-chemical adsorption, and that a second-order kinetics equation described the adsorption process well. The optimum conditions for the adsorption of Cd²⁺ and Pb²⁺ by corn stalk xanthate were: adsorption time 2 hours, temperature 20-25 °C, and pH 6-8. The results serve as a reference for treating wastewater containing Cd²⁺ and Pb2⁺.

Competitive removal of Pb2+ and malachite green from water by magnetic phosphate nanocomposites

The competitive removal of Pb²⁺ and malachite green (MG) from water by three magnetic phosphate nanocomposites (Fe₃O₄/Ba₃(PO₄)₂, Fe₃O₄/Sr₅(PO₄)₃(OH), and Fe₃O₄/Sr_5xBa_3x(PO₄)₃(OH), namely "FBP", "FSP", and "FSBP", respectively) was systematically investigated compared with Fe₃O₄ ("F") nanoparticle. Temperature and adsorbent dosage for competitive removal were optimized to be 20 °C and 0.05 g in 50 mL. The kinetic and isothermal adsorption results were fitted well with the pseudo-second-order model and Langmuir model, respectively. In the competitive removal process, FSP showed a high affinity to Pb²⁺ (202.8 mg/g) while FBP possessed high selectivity for MG (175.4 mg/g), and FSBP was effective at simultaneous removal of Pb²⁺ and MG, with a capacity of 143.7 and 90.9 mg/g, respectively. The magnetic contents in nanocomposites allow magnetic separation of materials from the water after treatment. We proposed that the simultaneous removal mechanism by FSBP was due to ion exchange between Pb²⁺ and Sr²⁺ in the lattice and then the formation of hydrogen bonds between PO₄^3- outside the material's surface and positively charged hydrogen in MG. This study indicates the potential of these phosphate nanocomposites to be used as effective materials for selective or simultaneous removal of Pb²⁺ and MG from water.

Resonance light scattering sensor of the metal complex nanoparticles using diethyl dithiocarbamate doped graphene quantum dots for highly Pb(II)-sensitive detection in water sample

This study was aimed to detect Pb²⁺ using diethyl dithiocarbamate-doped graphene quantum dots (DDTC-GQDs) based pyrolysis of citric acid. The excitation maximum wavelength (λ_max, ex = 337 nm) of the DDTC-GQDs solution was blue shift from bare GQDs (λ_max, ex = 365 nm), with the same emission maximum wavelength (λ_max, em = 459 nm) indicating differences in the desired N, S matrices decorating in the nanoparticles. Their resonance light scattering intensities were peaked at the same λ_max, ex/em = 551/553 nm without any background effect of both ionic strength and masking agent. Under optimal conditions, the linear range was 1.0-10.0 μg L^-1 (R² = 0.9899), limit of detection was 0.8 μg L^-1 and limit of quantification was 1.5 μg L^-1. The precision, expressed as the relative standard deviations, for intra-day and inter-day analyses was 0.87% and 4.47%, respectively. The recovery study of Pb²⁺ for real water samples was ranged between 80.8% and 109.5%. The proposed method was also proved with certified water sample containing 60 μg L^-1 Pb²⁺ giving an excellent accuracy and was then implied satisfactorily for ultra-trace determination of Pb²⁺ in drinking water and tap water samples.

Magnetic dispersive micro-solid phase extraction with the CuO/ZnO@Fe3O4-CNTs nanocomposite sorbent for the rapid pre-concentration of chlorogenic acid in the medical extract of plants, food, and water samples

CuO/ZnO@Fe₃O₄-CNTs-NC was synthesized and used as a sorbent in a MDMSPE method for the determination of chlorogenic acid in the medical extract of plants, food, and water samples.

Facile synthesis, growth process, characterisation of a nanourchin-structured α-MnO2 and their application on ultrasonic-assisted adsorptive removal of cationic dyes: A half-life and half-capacity concentration approach

Textile dyes pose a serious threat in terms of water pollution due to its complex aromatic structures and poor degradability. In order to reduce the toxic effects of Crystal Violet (CV) and Methylene Blue (MB), an ultrasonic-assisted dye adsorption using urchin like α-MnO₂ nanostructures was studied. The adsorbent was synthesised by hydrothermal method at low-temperature. The crystallinity and morphology were determined to investigate the growth mechanism of α-MnO₂ nanourchins which consists of two main stages. The initial stage includes the formation of α-MnO₂ microspheres followed by the epitaxial growth of nanoneedles on to the surface of them. The α-MnO₂ was characterised by BET, XRD, FT-IR, XPS, SEM, TEM and TGA. At 5.6, the point of zero charge of α-MnO₂ nanostructures was determined. The total pore volume and average pore radius were confirmed to be 4.751 × 10^-2 cc/g and 10.99 Å respectively from the BET analysis. Batch adsorption experiments were performed to investigate the effect of pH, adsorbent dosage, sonication time, initial dye concentration, temperature, ultrasonic frequency and power. The adsorption mechanism was studied using several isotherm and kinetic models. The adsorption data of CV and MB at equilibrium was observed to adopt the Langmuir isotherm model and pseudo-second order kinetic model. The maximum adsorption capacities for CV and MB were found to be 5882.3 and 5000 mg/g respectively. The thermodynamic study predicted that the process was exothermic for CV and endothermic for MB. The effects of competitive ions, ionic strength and humic acid on the uptake of both the dyes were also investigated. And finally, the reusability of recovered α-MnO₂ after dye adsorption was studied up to five cycles for its potential industrial applications.

Synthesis of poly(acrylamide-co-itaconic acid)/MWCNTs superabsorbent hydrogel nanocomposite by ultrasound-assisted technique: Swelling behavior and Pb (II) adsorption capacity

In this research, the poly (acrylamide-co-itaconic acid)/multi-walled carbon nanotubes (P(AAm-co-IA)/MWCNTs) as a novel superabsorbent hydrogel nanocomposite was synthesized by graft copolymerization of acrylamide (AAm) and itaconic acid (IA) mixture in the presence of the MWCNTs using ammonium persulfate (APS) as a free radical initiator and methylenebisacrylamide (MBA) as a crosslinker under ultrasound-assisted condition. The blank P(AAm-co-IA) hydrogel and its composite with the MWCNTs were characterized by means of SEM, FTIR, XRD and TGA methods. The effects of different parameters such as pH, time, the MWCNTs content and salt solutions on swelling behavior were investigated. The stability of the hydrogel increased by any increase in the MWCNTs content, which might be attributed to the hydrophobic nature of the MWCNTs as well as the increase of the crosslinker density. The water retention capacity (WRC) of the P(AAm-co-IA) hydrogel increased in the presence of the MWCNT (10 wt%). The synthesized hydrogel nanocomposite was studied for Pb (II) adsorption from aqueous solution. The effects of different parameters such as contact time (5-90 min), Pb (II) initial concentration (25-175 mg/L) and initial pH (1.5-4.5) of solution on Pb (II) adsorption were investigated by batch method. In comparison to P(AAm-co-IA) hydrogel, the P(AAm-co-IA)/MWCNTs hydrogel nanocompoite showed better adsorption behavior toward Pb (II). One of the most important aspects of this research was to investigate the effects of ultrasonic waves on polymer matrix and its ability.

Ultrasound wave assisted adsorption of congo red using gold-magnetic nanocomposite loaded on activated carbon: Optimization of process parameters

In this study, gold-magnetic nanocomposite in the presence of ultrasound wave assisted was synthesized and loaded on activated carbon (Au-Fe₃O₄-NCs-AC) by simple, fast and low-cost process. This novel material was applied for ultrasound assisted adsorption of congo red (CR) as model of toxic and even carcinogenic substance from aqueous solution. The detail of morphology and identity of Au-Fe₃O₄-AC was characterized by SEM and TEM techniques and correlation among response to variables such as pH (2-10), adsorbent mass (0.005-0.025 g), initial CR concentration (10-30 mg L^-1) and ultrasound time (2-6 min) was investigated by response surface methodology (RSM) under central composite design (CCD). Analysis of variance (ANOVA) exhibit a high R² value of 0.999 and confirm suitability of constructed second-order regression model for excellent evaluation and prediction of the experimental data. The interaction and main factor and optimum conditions of the under study process were determined from response surface plots based on desirability function. The maximum CR adsorption were achieved at pH of 4, 15 mg L^-1 of CR, 0.017 g of Au-Fe₃O₄-AC and 5 min sonication which owing to 99.49% removal efficiency is highly recommended for future CR removal from different matrixes. Adsorption kinetic follow second-order rate expression in combination to inter particle diffusion and equilibrium adsorption data best represented by the Langmuir isotherm with maximum mono-layer adsorption capacity of 43.88 mg g^-1.

Development of self-assembled nanocrystalline cellulose as a promising practical adsorbent for methylene blue removal

This study is focused on nanocrystalline cellulose (NCC) flakes for methylene blue (MB) removal via adsorption. NCC flakes exhibit a high adsorption capacity (188.7 mg/g fixed at 0.7 g/L adsorbent dosage, 25 °C and pH 6) compared to other nanomaterials, such as carbon nanotube and other cellulosic materials, such as coffee husks. Unlike NCC powder, it was observed that NCC flakes can be easily separated from wastewater containing MB. Further adsorption studies were conducted on NCC flakes, and it was found that 0.7 g/L was the optimum adsorbent dosage, which fitted well with the Langmuir Isotherm. The mean free energy value from Dubinin-Radushkevich isotherm was less than 8 kJ/mol. ΔG_o values at different temperatures were within the -20 kJ/mol to 0 kJ/mol range. In conclusion, NCC flakes is a promising and practical 'green' nanomaterial that can be further developed for industrial applications.

A Novel Cyclodextrin-Functionalized Hybrid Silicon Wastewater Nano-Adsorbent Material and Its Adsorption Properties

A novel cyclodextrin-functionalized hybrid silicon nano-adsorbent material (6-EA-β-CD-Si) was synthesized via the nucleophilic substitution method. The structure was detected by Fourier transform infrared (FT-IR), X-ray, thermogravimetric analysis, and Brunauer-Emmett-Teller (BET) analysis. Results reveal that the BET surface area of 6-EA-β-CD-Si is 240 m²/g and the average pore size is 4.16 nm. The adsorption properties of 6-EA-β-CD-Si onto methylene blue (MB) were studied and fitted with adsorption kinetic models. Both the Freundlich adsorption isotherm model and pseudo-second-order model were fitted with well shows that the multi-layer adsorption with chemisorption and physisorption co-existing in the system. The maximum adsorption capacities are 39.37, 39.21, 36.90, and 36.36 mg/g at temperatures 303, 313, 323, and 333 K, respectively. The maximum removal rate of MB could reach 99.5%, indicating the potential application value of 6-EA-β-CD-Si in wastewater treatment. The adsorption mechanisms of 6-EA-β-CD-Si showed that the hydrophobic cave of β-CD plays an important role on the adsorption of MB.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection.

Nanopowder synthesis of novel Sn(II)-imprinted poly(dimethyl vinylphosphonate) by ultrasound-assisted technique: Adsorption and pre-concentration of Sn(II) from aqueous media and real samples

In this research, a novel Sn(II)-imprinted poly(dimethyl vinylphosphonate) nanopowder (Sn(II)-IPDMVPN) was prepared using Sn²⁺, dimethyl vinylphosphonate, azobis isobutyronitril and ethylene glycol dimethacrylate as the template, ligand, initiator and cross linker, respectively. The non-imprinted poly(dimethyl vinylphosphonate) nanopowder (NIPDMVPN) was also synthesized utilizing the same procedure without using SnCl₂·2H₂O in order to compare the results with the Sn(II)-IPDMVPN. The structure, morphology and composition of the products were characterized by XRD, SEM, EDX, XRF, BET, FT-IR and NMR techniques. Some experimental conditions including pH, eluent concentration and sample volume were optimized to maximize Sn(II) adsorption by the Sn(II)-IPDMVPN. It was found that the optimum conditions are pH = 5, 1.00 M of HNO₃ as eluent and sample volume up to 50 mL. The results obtained by ICP-MS indicated that the Sn(II)-IPDMVPN had much higher adsorption capacity for Sn(II) ions (about threefold) than the NIPDMVPN. The applicability of the Sn(II)-IPDMVPN was also investigated in three different real samples. Under the best experimental conditions, the calibration graphs were linear in the range of 0.19-90 μg L^-1 with a coefficient of determination (R²) of 0.990. The detection limit was calculated to be 0.06 μg L^-1. The relative standard deviation (RSD) for six replicate measurements of Sn(II) at 1.00 ng mL^-1 was determined to be 1.8%. The results showed that the Sn(II)-IPDMVPN-ICP-MS is a very simple, rapid, sensitive and efficient method for the determination of Sn(II) ions in water samples.