Application of artificial neural network for the prediction of biosorption capacity of immobilized Bacillus subtilis for the removal of cadmium ions from aqueous solution

Calcium-Alginate-Chitosan Nanoparticle as a Potential Solution for Pesticide Removal, a Computational Approach

Pesticides have a significant negative impact on the environment, non-target organisms, and human health. To address these issues, sustainable pest management practices and government regulations are necessary. However, biotechnology can provide additional solutions, such as the use of polyelectrolyte complexes to encapsulate and remove pesticides from water sources. We introduce a computational methodology to evaluate the capture capabilities of Calcium-Alginate-Chitosan (CAC) nanoparticles for a broad range of pesticides. By employing ensemble-docking and molecular dynamics simulations, we investigate the intermolecular interactions and absorption/adsorption characteristics between the CAC nanoparticles and selected pesticides. Our findings reveal that charged pesticide molecules exhibit more than double capture rates compared to neutral counterparts, owing to their stronger affinity for the CAC nanoparticles. Non-covalent interactions, such as van der Waals forces, π-π stacking, and hydrogen bonds, are identified as key factors which stabilized the capture and physisorption of pesticides. Density profile analysis confirms the localization of pesticides adsorbed onto the surface or absorbed into the polymer matrix, depending on their chemical nature. The mobility and diffusion behavior of captured compounds within the nanoparticle matrix is assessed using mean square displacement and diffusion coefficients. Compounds with high capture levels exhibit limited mobility, indicative of effective absorption and adsorption. Intermolecular interaction analysis highlights the significance of hydrogen bonds and electrostatic interactions in the pesticide-polymer association. Notably, two promising candidates, an antibiotic derived from tetracycline and a rodenticide, demonstrate a strong affinity for CAC nanoparticles. This computational methodology offers a reliable and efficient screening approach for identifying effective pesticide capture agents, contributing to the development of eco-friendly strategies for pesticide removal.

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.

Simultaneous removal of ternary heavy metal ions by a newly isolated Microbacterium paraoxydans strain VSVM IIT(BHU) from coal washery effluent

In the present work, the removal of Cr (VI), Cd (II) and Pb (II) at 50 mg/L of each metal ion concentration was investigated by Microbacterium paraoxydans strain VSVM IIT(BHU). The heavy metal binding on the bacterial cell surface was confirmed through X-ray photoelectron spectroscopy and energy dispersive X-ray. X-ray photoelectron spectroscopy analysis also confirmed the reduction of Cr (VI) to Cr (III). Heavy metal removal dynamics was investigated by evaluating dimensionless, and the value of N_k (9.49 × 10^-3, 9.92 × 10^-3 and 1.23 × 10^-2 for Cr (VI), Cd (II) and Pb (II) ions) indicated that the removal of heavy metals by bacterial isolate was mixed diffusion and transfer controlled. It was found that both the experimental and predicted values for isolated bacterial strain coincided with each other with a good R² value in the L-M Algorithm range of 0.94-0.98 for the ternary metal ion system. The bacterial isolate presented a maximum heavy metal ion removal efficiency of 91.62% Cr (VI), 89.29% Pb (II), and 83.29% Cd (II) at 50 mg/L.

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.

Application of neural network in metal adsorption using biomaterials (BMs): a review

ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.

Polysaccharides as Support for Microbial Biomass-Based Adsorbents with Applications in Removal of Heavy Metals and Dyes

The use of biosorbents for the decontamination of industrial effluent (e.g., wastewater treatment) by retaining non-biodegradable pollutants (antibiotics, dyes, and heavy metals) has been investigated in order to develop inexpensive and effective techniques. The exacerbated water pollution crisis is a huge threat to the global economy, especially in association with the rapid development of industry; thus, the sustainable reuse of different treated water resources has become a worldwide necessity. This review investigates the use of different natural (living and non-living) microbial biomass types containing polysaccharides, proteins, and lipids (natural polymers) as biosorbents in free and immobilized forms. Microbial biomass immobilization performed by using polymeric support (i.e., polysaccharides) would ensure the production of efficient biosorbents, with good mechanical resistance and easy separation ability, utilized in different effluents' depollution. Biomass-based biosorbents, due to their outstanding biosorption abilities and good efficiency for effluent treatment (concentrated or diluted solutions of residuals/contaminants), need to be used in industrial environmental applications, to improve environmental sustainability of the economic activities. This review presents the most recent advances related the main polymers such as polysaccharides and microbial cells used for biosorbents production; a detailed analysis of the biosorption capability of algal, bacterial and fungal biomass; as well as a series of specific applications for retaining metal ions and organic dyes. Even if biosorption offers many advantages, the complexity of operation increased by the presence of multiple pollutants in real wastewater combined with insufficient knowledge on desorption and regeneration capacity of biosorbents (mostly used in laboratory scale) requires more large-scale biosorption experiments in order to adequately choose a type of biomass but also a polymeric support for an efficient treatment process.

A meta-analysis of metal biosorption by suspended bacteria from three phyla

Biosorption of heavy metals by bacterial biomass has been the subject of significant research interest in last decades due to its efficiency, relatively low cost and minimal negative effects for the surrounding environment. In this meta-analysis, the biosorption efficiencies of different bacterial strains for Cu(II), Cd(II), Zn(II), Cr(III), Mn(II), Pb(II) and Ni(II) were evaluated. Optimum conditions for the biosorption process such as initial metal concentration, temperature, pH, contact time, metal type, biomass dosage and bacterial phyla, were evaluated for each heavy metal. According to the results, the efficiencies of bacterial biomass for removal of heavy metal were as follows: Cd(II) > Cr(III) > Pb(II) > Zn(II) > Cu(II) > Ni(II) > Mn(II). Firmicute phyla showed the highest overall (living and dead) biosorption efficiency for heavy metals. Living biomass of Proteobacteria had the best biosorption performance. Living bacterial biomass was significantly more efficient in biosorption of Cu(II), Zn(II) and Pb(II) than dead biomass. The maximum biosorption efficiency of bacterial strains for Cd(II), Pb(II) and Zn(II) was achieved at pH values between 6 and 7.5. High temperatures (>35 °C) reduced the removal efficiencies for Cu(II) and Zn(II) and increased the efficiencies for Cd(II) and Cr(III) ions. The maximum biosorption efficiency of non-essential heavy metals occurred with short contact times (<2 h). Essential metals such as Zn and Cu were more efficiently removed with long biosorption durations (>24 h). The mean biosorption capacity of bacterial biomass was between 71.26 and 125.88 mg g^-1. No publication bias existed according to Egger's and Begg's test results.

Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants

Heavy metals and emerging engineered nanoparticles (ENPs) are two current environmental concerns that have attracted considerable attention. Cerium oxide nanoparticles (CeO₂NPs) are now used in a plethora of industrial products, while cadmium (Cd) is a great environmental concern because of its toxicity to animals and humans. Up to now, the interactions between heavy metals, nanoparticles and plants have not been extensively studied. The main objectives of this study were (i) to determine the synergistic effects of Cd and CeO₂NPs on the physiological parameters of Brassica and their accumulation in plant tissues and (ii) to explore the underlying physiological/phenotypical effects that drive these specific changes in plant accumulation using Artificial Neural Network (ANN) as an alternative methodology to modeling and simulating plant uptake of Ce and Cd. The combinations of three cadmium levels (0 [control] and 0.25 and 1 mg/kg of dry soil) and two CeO₂NPs concentrations (0 [control] and 500 mg/kg of dry soil) were investigated. The results showed high interactions of co-existing CeO₂NPs and Cd on plant uptake of these metal elements and their interactive effects on plant physiology. ANN also identified key physiological factors affecting plant uptake of co-occurring Cd and CeO₂NPs. Specifically, the results showed that root fresh weight and the net photosynthesis rate are parameters governing Ce uptake in plant leaves and roots while root fresh weight and F_v/F_m ratio are parameters affecting Cd uptake in leaves and roots. Overall, ANN is a capable approach to model plant uptake of co-occurring CeO₂NPs and Cd.

Immobilized microbial biosorbents for heavy metals removal

Intensive industrial and urban growth has led to the release of increasing amounts of environmental pollutants. Contamination by metals, in particular, deserves special attention due to their toxicity and potential to bioaccumulate via the food chain. Conventional techniques for the removal of toxic metals, radionuclides and precious metals from wastewater all have a number of drawbacks, such as incomplete metal extraction, high cost and risk of generating hazardous by-products. Biosorption is a cost-effective and environment-friendly technology, an alternative to conventional wastewater treatment methods. Biosorption is a metabolically independent process, in which dead microbial biomass is capable of removal and concentrating metal ions from aqueous solutions. Free microbial biosorbents are of small size and low density, insufficient mechanical stability and low elasticity, which causes problems with metal ion desorption, separation of the sorbent from the medium and its regeneration. Hence, the possibilities for the implementation of continuous biosorbent processes for metal removal in flow-type reactor systems are reduced and the practical application of biosorption in industrial conditions is limited. By immobilizing microbial biomass on suitable carriers the disadvantages of free biosorbents are eliminated and more opportunities for practical use of biosorption become available. This review examines different immobilization techniques and carriers, certain basic features and possibilities of using immobilized microbial biosorbents for the removal and concentration of metals from aqueous solutions.

Biosorption of Cadmium by Non-Toxic Extracellular Polymeric Substances (EPS) Synthesized by Bacteria from Marine Intertidal Biofilms

Cadmium is a major heavy metal found in polluted aquatic environments, mainly derived from industrial production processes. We evaluated the biosorption of solubilized Cd²⁺ using the extracellular polymeric substances (EPS) produced by Bacillus sp. MC3B-22 and Microbacterium sp. MC3B-10 (Microbactan); these bacteria were originally isolated from intertidal biofilms off the coast of Campeche, Mexico. EPS were incubated with different concentrations of cadmium in ultrapure water. Residual Cd²⁺ concentrations were determined by Inductive Coupled Plasma-Optic Emission Spectrometry and the maximum sorption capacity (Qmax) was calculated according to the Langmuir model. EPS were characterized by X-ray photoelectron spectroscopy (XPS) before and after sorption. The Qmax of Cd²⁺ was 97 mg g⁻¹ for Microbactan and 141 mg g⁻¹ for MC3B-22 EPS, these adsorption levels being significantly higher than previously reported for other microbial EPS. In addition, XPS analysis revealed changes in structure of EPS after biosorption and showed that amino functional groups contributed to the binding of Cd²⁺, unlike other studies that show the carbohydrate fraction is responsible for this activity. This work expands the current view of bacterial species capable of synthesizing EPS with biosorbent potential for cadmium and provides evidence that different chemical moieties, other than carbohydrates, participate in this process.

Magnetic ethylene diamine-functionalized graphene oxide as novel sorbent for removal of lead and cadmium ions from wastewater samples

In this paper, magnetic ethylene diamine-functionalized graphene oxide (MDFGO) as a novel sorbent was synthesized and applied for removal of Pb(II) and Cd(II) from real wastewater samples. The morphology and molecular structure of MDFGO were studied by different analytical methods. The effective parameters in adsorption efficiency of Pb(II) and Cd(II) were studied and optimized using experimental design. Under the optimal condition, the effective parameters including pH, sorbent dosage, shaking rate, and adsorption time were 6.2, 33.0 mg, 500 rpm, and 11 min, respectively. Mechanism of adsorption kinetic was investigated using the Lagergren pseudo-first-order, pseudo-second-order, and intraparticle diffusion models. It was found that adsorption of lead and cadmium ions in the MDFGO sorbent followed from pseudo-first-order and pseudo-second-order models, respectively. Thermodynamic parameters (ΔG°, ΔH°, and ΔS°) for the lead and cadmium ions uptake onto the MDFGO sorbent were calculated and indicated that the adsorption processes were spontaneous and endothermic in nature for both cations. In order to investigate the isotherm model for adsorption of Pb(II) and Cd(II), the experimental data were studied using the Langmuir, Freundlich, and Harkins-Jura isotherm models. The results fitted well with Freundlich model for both metal ions. The new sorbent (MDFGO) was applied to remove Pb(II) and Cd(II) from battery wastewater and electroplating wastewater. The removal percentage of Pb(II) and Cd(II) were 99.6±0.5 and 99.4±0.6, respectively, and demonstrated that the new sorbent was very suitable for removal of lead and cadmium ion from the real wastewater samples.

Characteristics of uranium biosorption from aqueous solutions on fungus Pleurotus ostreatus

Uranium(VI) biosorption from aqueous solutions was investigated in batch studies by using fungus Pleurotus ostreatus biomass. The optimal biosorption conditions were examined by investigating the reaction time, biomass dosage, pH, temperature, and uranium initial concentration. The interaction between fungus biomass and uranium was confirmed using Fourier transformed infrared (FT-IR), scanning electronic microscopy energy dispersive X-ray (SEM-EDX), and X-ray photoelectron spectroscopy (XPS) analysis. Results exhibited that the maximum biosorption capacity of uranium on P. ostreatus was 19.95 ± 1.17 mg/g at pH 4.0. Carboxylic, amine, as well as hydroxyl groups were involved in uranium biosorption according to FT-IR analysis. The pseudo-second-order model properly evaluated the U(VI) biosorption on fungus P. ostreatus biomass. The Langmuir equation provided better fitting in comparison with Freundlich isotherm models. The obtained thermodynamic parameters suggested that biosorption is feasible, endothermic, and spontaneous. SEM-EDX and XPS were additionally conducted to comprehend the biosorption process that could be described as a complex process involving several mechanisms of physical adsorption, chemisorptions, and ion exchange. Results obtained from this work indicated that fungus P. ostreatus biomass can be used as potential biosorbent to eliminate uranium or other radionuclides from aqueous solutions.

Synthesis of magnetic γ-Fe2O3-based nanomaterial for ultrasonic assisted dyes adsorption: Modeling and optimization

γ-Fe2O3 nanoparticles were synthesized and loaded on activated carbon. The prepared nanomaterial was characterized by field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier transforms infrared spectroscopy (FT-IR) and X-ray diffraction (XRD). The γ-Fe2O3 nanoparticle-loaded activated carbon (γ-Fe2O3-NPs-AC) was used as novel adsorbent for the ultrasonic-assisted removal of methylene blue (MB) and malachite green (MG). Response surface methodology and artificial neural network were applied to model and optimize the adsorption of the MB and MG in their individual and binary solutions followed by the investigation on adsorption isotherm and kinetics. The individual effects of parameters such as pH, mass of adsorbent, ultrasonication time as well as MB and MG concentrations in addition to the effects of their possible interactions on the adsorption process were investigated. The numerical optimization revealed that the optimum adsorption (>99.5% for each dye) is obtained at 0.02g, 15mgL(-1), 4min and 7.0 corresponding to the adsorbent mass, each dye concentration, sonication time and pH, respectively. The Freundlich, Langmuir, Temkin and Dubinin-Radushkevich isotherms were studied. The Langmuir was found to be most applicable isotherm which predicted maximum monolayer adsorption capacities of 195.55 and 207.04mgg(-1) for the adsorption of MB and MG, respectively. The pseudo-second order model was found to be applicable for the adsorption kinetics. Blank experiments (without any adsorbent) were run to investigate the possible degradation of the dyes studied in presence of ultrasonication. No dyes degradation was observed.

Immobilized materials for removal of toxic metal ions from surface/groundwaters and aqueous waste streams

Heavy metals from industrial processes are of special concern because they produce chronic poisoning in the aquatic environment. More strict environmental regulations on the discharge of toxic metals require the development of various technologies for their removal from polluted streams (i.e. industrial wastewater, mine waters, landfill leachate, and groundwater). The separation of toxic metal ions using immobilized materials (novel sorbents and membranes with doped ligands), due to their high selectivity and removal efficiency, increased stability, and low energy requirements, is promising for improving the environmental quality. This critical review is aimed at studying immobilized materials as potential remediation agents for the elimination of numerous toxic metal (e.g. Pb, Cd, Hg, and As) ions from polluted streams. This study covers the general characteristics of immobilized materials and separation processes, understanding of the metal ion removal mechanisms, a review of the application of immobilized materials for the removal of toxic metal ions, as well as the impacts of various parameters on the removal efficiency. In addition, emerging trends and opportunities in the field of remediation technologies using these materials are addressed.

Bioremoval of heavy metals by bacterial biomass

Heavy metals are among the most common pollutants found in the environment. Health problems due to the heavy metal pollution become a major concern throughout the world, and therefore, various treatment technologies such as reverse osmosis, ion exchange, solvent extraction, chemical precipitation, and adsorption are adopted to reduce or eliminate their concentration in the environment. Biosorption is a cost-effective and environmental friendly technique, and it can be used for detoxification of heavy metals in industrial effluents as an alternative treatment technology. Biosorption characteristics of various bacterial species are reviewed here with respect to the results reported so far. The role of physical, chemical, and biological modification of bacterial cells for heavy metal removal is presented. The paper evaluates the different kinetic, equilibrium, and thermodynamic models used in bacterial sorption of heavy metals. Biomass characterization and sorption mechanisms as well as elution of metal ions and regeneration of biomass are also discussed.