Synthesis of zinc oxide nanoparticles-chitosan for extraction of methyl orange from water samples: cuckoo optimization algorithm-artificial neural network

Microwave-assisted synthesis of cross-linked chitosan-metal oxide nanocomposite for methyl orange dye removal from unary and complex effluent matrices

Textile/Dyeing industries have been considered as one of the intense water-consuming units, resulting in the generation of a large volume of dye(s) contaminated effluent posing a heavy burden on the receiving water bodies. Therefore, the identification of methods to synthesize bulk quantity of adsorbent(s) and further their evaluation for the efficient treatment of effluent is one of the most prominent topics. Hence, microwave-assisted method was proposed for the rapid synthesis of nanocomposite (C-CS@ZnO) from natural biomolecule (chitosan-CS), a well-known crosslinker (tripolyphosphate) and metal-oxide (ZnO) nanoparticles. Detailed characterization was performed to identify the structure (SEM, XRD) and composition (FT-IR, XPS) of the sorbent. Sorption experiments with methyl orange (MO) dye solution were carried out under different pH (2.0-12.0), dye concentrations (150-350 mg L^-1), reaction times (0-210 min) and temperature (25-45 °C) to establish the adsorbent at the lab-scale. The maximum sorption capacity (185.2 mg g^-1) was obtained because of the ligand-exchange, Yoshida H-bonding and electrostatic interactions and was best elucidated by Freundlich (R² ≥ 0.99) and pseudo-second-order (R² ≥ 1) models. To simulate the field conditions, the effects of co-existing ions (anions/cations), cocktail dyes/ions mixture and regenerant were also studied. The obtained results suggest its promising applicability at a large scale for textile effluent treatment.

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.

Determination of Phthalate Esters in Cosmetics and Baby Care Products by a Biosorbent Based on Lawsone Capped Chitosan and Followed by Liquid Chromatography

This research presents a green synthetic pathway for the preparation of a new biosorbent and eco-friendly extraction process of three phthalate esters: dimethyl phthalate, di-butyl phthalate and benzyl butyl phthalate, from cosmetics and baby care products. Dispersive solid-phase extraction was used based on a new core-shell biomass/sorbent; chitosan-loaded lawsone. The proposed method provides fortunate trapping of phthalate esters in a one-step extraction. Under the optimized extraction conditions, the current work was presented low detection limits (0.03-0.15 ng. g-1), limits of quantification (0.1-0.5 ng·g-1) and reasonable linearity (0.1-10 000 ng. g-1). The applicability of the method was estimated by recovery experiments at different spiking levels (n = 5) for phthalate esters in the real samples.

Biosynthesis of ZnO Nanoparticles Using Bacillus Subtilis: Characterization and Nutritive Significance for Promoting Plant Growth in Zea mays L

Nano-fertilizer(s), an emerging field of agriculture, is alternate option for enhancement of plant growth replacing the synthetic fertilizers. Zinc oxide nanoparticles (ZnO NPs) can be used as the zinc source for plants. The present investigation was carried out to assess the role of ZnO NPs in growth promotion of maize plants. Biosynthesized ZnO NPs (using Bacillus sp) were characterized using Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM), X-ray diffraction (XRD) and Zeta potential. Different concentrations of ZnO NPs (2, 4, 8, 16 mg/L) were explored in pot culture experiment. Size of ZnO NPs ranged between 16 and 20 nm. A significant increase in growth parameters like shoot length (61.7%), root length (56.9%) and significantly higher level of protein was observed in the treated plants. The overall pattern for growth biomarkers including the protein contents was maximum at 8 mg/L of ZnO NPs. It was observed that application of biosynthesized ZnO NPs has improved majority of growth biomarkers including plant growth parameters, protein contents and leaf area. Therefore, biosynthesized ZnO NPs could be considered as an alternate source of nutrient in Zn deficient soils for promoting the modern agriculture.

Co-Fe-layered double hydroxide decorated amino-functionalized zirconium terephthalate metal-organic framework for removal of organic dyes from water samples

In this study, a new efficient adsorbent of Co-Fe-layered double hydroxides@metal-organic framework (Co-Fe-LDH@UiO-66-NH₂) was synthesized and used for extraction of methylene blue (MB) and methylene red (MR) from water samples prior to their determination by UV-Vis spectrophotometer. The adsorbent was characterized by Fourier Transform Infrared Spectroscopy (FT-IR), Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX), X-ray Diffraction (XRD), and Brunauer-Emmett-Teller (BET) analyses. The impact of various parameters such as pH of the aqueous phase, extraction time, amount of adsorbent, type and volume of eluent solvent, desorption time, and sample volume were studied. The maximum extraction recovery was obtained at an optimized pH 8.0 and extraction time 10.0 min. The adsorption process was fitted by the Langmuir model with a maximum adsorption capacity of 555.62 mg/g and 588.2 mg/g, respectively, for MB and MR. Under optimum conditions, the limit of detection (LOD) for MB was 0.7 μgL^-1 and 0.9 μgL^-1 for MR. Furthermore, the Co-Fe-LDH@UiO-66-NH₂ composite showed high efficiency for the removal of the analytes from environmental water samples.

Poly(lauryl methacrylate)-Grafted Amino-Functionalized Zirconium-Terephthalate Metal-Organic Framework: Efficient Adsorbent for Extraction of Polycyclic Aromatic Hydrocarbons from Water Samples

In this study, a novel porous hybrid material, poly(lauryl methacrylate) polymer-grafted UiO-66-NH2 (UiO = University of Oslo), was synthesized for efficient extraction of polycyclic aromatic hydrocarbons (PAHs) from aqueous samples. The polymer end-tethered covalently to the MOF's surface was synthesized by surface-initiated atom transfer radical polymerization, revealing a distinct type of morphology. The adsorbent was characterized by scanning electron microscopy, energy-dispersive spectroscopy, transmission electron microscopy, powder X-ray diffraction, N2 adsorption-desorption analysis, Fourier transform infrared spectroscopy, and thermogravimetric analysis. The analyses were carried out by gas chromatography-mass spectrometry. Parameters including the type and volume of the eluent, the amount of the adsorbent, and adsorption and desorption times were investigated and optimized. Under optimal conditions, the limit of detection, intraday precision, and interday precision were in the range of 3-8 ng L-1, 1.4-3.1, and 4.1-6.5%, respectively. The procedure was used for analysis of PAHs from natural water samples.

Nitrogen-Doped Carbon Dots from Averrhoa carambola Fruit Extract as a Fluorescent Probe for Methyl Orange

In this study, a simple and green hydrothermal treatment was performed to prepare nitrogen-doped carbon dots (NCDs) from Averrhoa carambola (AC) fruit extract as a carbon precursor and L-arginine (Arg) as a nitrogen dopant. The AC-NCDs were characterized by UV light, fluorescence spectroscopy, transmission electron microscopy, FTIR spectroscopy, Raman spectroscopy, UV-vis spectroscopy, and zeta potential analyzer. The AC-NCDs were spherical and the average diameter was estimated to be 6.67 nm. The AC-NCDs exhibited the maximum emission intensity at 446 nm with 360 nm excitation wavelength. The fluorescence quenching behavior of AC-NCDs after interacting with methyl orange (MO) dye was studied. The interaction of AC-NCDs and MO was achieved within 3 min and the fluorescence quenching was maintained to a fixed value even after 30 min. The linearity was obtained in the range of 1 to 25 μM MO with a 0.30 μM detection limit. Furthermore, the pH values affected the quenching behavior of the AC-NCDs/MO system where the interaction mechanisms were driven by the electrostatic interaction, π-π interaction, inner filter effect, and energy transfer. The pH 5 maintained higher quenching efficiency while other pH values slightly decreased the quenching efficiency. Incoming applications, the AC-NCDs can be used in various important fields, especially for environmental protection.

Green synthesis and characterization of zinc oxide nanoparticles using carboxylic curdlan and their interaction with bovine serum albumin

Carboxylic curdlan (Cc), as a versatile β-1,3-polyglucuronic acid derivative, was used as both reducing and capping agents for the green synthesis of zinc oxide nanoparticles (ZnO NPs).

Simultaneous automatic electrochemical detection of zinc, cadmium, copper and lead ions in environmental samples using a thin-film mercury electrode and an artificial neural network

In this study a device for automatic electrochemical analysis was designed. A three electrodes detection system was attached to a positioning device, which enabled us to move the electrode system from one well to another of a microtitre plate. Disposable carbon tip electrodes were used for Cd(II), Cu(II) and Pb(II) ion quantification, while Zn(II) did not give signal in this electrode configuration. In order to detect all mentioned heavy metals simultaneously, thin-film mercury electrodes (TFME) were fabricated by electrodeposition of mercury on the surface of carbon tips. In comparison with bare electrodes the TMFEs had lower detection limits and better sensitivity. In addition to pure aqueous heavy metal solutions, the assay was also performed on mineralized rock samples, artificial blood plasma samples and samples of chicken embryo organs treated with cadmium. An artificial neural network was created to evaluate the concentrations of the mentioned heavy metals correctly in mixture samples and an excellent fit was observed (R² = 0.9933).