Adsorption of methylene blue cationic dye onto brookite and rutile phases of titanium dioxide: Quantum chemical and molecular dynamic simulation studies

Rheological Properties and Inkjet Printability of a Green Silver-Based Conductive Ink for Wearable Flexible Textile Antennas

The development of e-textiles necessitates the creation of highly conductive inks that are compatible with precise inkjet printing, which remains a key challenge. This work presents an innovative, syringe-based method to optimize a novel bio-sourced silver ink for inkjet printing on textiles. We investigate the relationships between inks' composition, rheological properties, and printing behavior, ultimately assessing the electrical performance of the fabricated circuits. Using Na-alginate and polyethylene glycol (PEG) as the suspension matrix, we demonstrate their viscosity depends on the component ratios. Rheological control of the silver nanoparticle-laden ink has become paramount for uniform printing on textiles. A specific formulation (3 wt.% AgNPs, 20 wt.% Na-alginate, 40 wt.% PEG, and 40 wt.% solvent) exhibits the optimal rheology, enabling the printing of 0.1 mm thick conductive lines with a low resistivity (8 × 10-3 Ω/cm). Our findings pave the way for designing eco-friendly ink formulations that are suitable for inkjet printing flexible antennas and other electronic circuits onto textiles, opening up exciting possibilities for the next generation of E-textiles.

Green Organo-Photooxidative Method for the Degradation of Methylene Blue Dye

This study used an organophoto-oxidative material to degrade the toxic azo dye, methylene blue (MB), due to its hazardous effects on aquatic life and humans. MB is traditionally degraded using metal-based catalysts, resulting in high costs. Several organic acids were screened for organo-photooxidative applications against various azo dyes, and ascorbic acid (AA), also known as vitamin C, was found to be best for degradation due to its high photooxidative activity. It is an eco-friendly, edible, and efficient photooxidative material. A photocatalytic box has been developed for the study of organo-photooxidative activity. It was found that when AA was added, degradation efficiency increased from 42 to 95% within 240 min. Different characterization techniques, such as HPLC and GC-MS, were used after degradation for the structural elucidation of degraded products. DFT study was done for the investigation of the mechanistic study behind the degradation process. A statistical tool, RSM, was used for the optimization of parameters (concentration of dye, catalyst, and time). This study develops sustainable and effective solutions for wastewater treatment.

Removal of methylene blue by using sodium alginate-based hydrogel; validation of experimental findings via DFT calculations

Removal of commonly used dyes from water bodies has recently gained great interest from the scientific community. Presence of the methylene blue (MB) dye in drinking water poses harmful effects on the human health. The large-scale removal of MB is achievable through highly efficient, inexpensive, renewable, and biodegradable adsorbents. Our research group has recently synthesized a sodium alginate-based hydrogel and explored its application towards the removal of MB. Previous results have shown that the synthesized hydrogel exhibits a high adsorption capacity of 51.34 mg/g under basic conditions. Herein, we employed the density functional theory (DFT) calculations to explore the mechanism of MB removal by using sodium alginate hydrogel at various pH levels. Results of this study have shown that under acidic/neutral conditions the removal of MB is endergonic (ΔG_int = 6.10 kcal/mol). Whereas under basic conditions it is highly exergonic (ΔG_int = -97.58 kcal/mol). Moreover, the QTAIM and NCI analyses have shown that the MB dye is chemisorbed to the absorbent via strong covalent-like interactions between the polymer's carboxylate groups and the hydrogens in MB. Furthermore, preferability of basic conditions have been confirmed by the large charge transfer (0.104 |e|), as compared to no charge being transferred in the acidic/neutral conditions.

Green synthesis, characterization, and application of Fe3O4 nanoparticles for methylene blue removal: RSM optimization, kinetic, isothermal studies, and molecular simulation

Methylene Blue (MB) is a cationic dye causing various health problems such as asthma, heartbeat, eye and skin irritation, nausea, and distress during prolonged exposure. In this regard, the green magnetite nanoparticle was synthesized using the extract of Prosopis farcta. The synthesized Fe₃O₄nanoparticle was characterized by X-ray powder diffraction (XRD), Field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), Fourier transforms Infrared spectroscopy (FTIR), vibrating sample magnetometer (VSM), and Brunauer-Emmett-Teller (BET). The corresponding parameters, including the primary concentration of MB (5-65 mg/L), the dose of synthesized nanoparticle (0.025-0.925 g/L), solution pH (3-11), and contact time (20-60 min), were considered. Also, central composite design (CCD), as one of the response surface methodologies (RSM), was used for the related modelling and optimization. The particle size of the adsorbent was between 5 and 70 nm, and the nanoparticle has 206.75 m²/g of a specific surface, 6.1 nm of average pore size, and 0.3188 cm³/g of the total pore volume. The optimal conditions for MB removal by the nanoparticle were found to follow an initial MB concentration of 20 mg/L, 0.7 g/L of the nanoparticle dose, pH = 9, and a contact time of 50 min. The pseudo-second-order (PSO) and Freundlich models were the best kinetic and isothermal models for MB removal by the synthesized nanoparticle. Molecular modelling was used to optimize the MB molecular configuration and compute HOMO-LUMO energies, quantum-chemical descriptors, and molecular electrostatic potential to evaluate the nature reactivity of the MB molecule.

Interlayer adsorption of cationic dye on cationic surfactant-modified and unmodified montmorillonite

Water pollution by toxic organic dyes is one of the most critical health and environmental problems worldwide. By means of molecular dynamics method, the present work aims to evaluate the applicability of montmorillonite (Mt) modified by hexadecyltrimethylammonium cations (HDTMA⁺) compared to unmodified Na-Mt for the adsorption of cationic methylene blue (MB) dye. The results showed that the adsorption energy of MB on both HDTMA-Mt and Na-Mt absorbent ranged from - 100 to - 250 kJ/mol, indicating the effectiveness of two types of adsorbents in dye water treatment. The highest adsorption energy was found at w = 50% in each adsorbent system. Adsorption mechanisms of MB depend on molecular orientations, which is influenced by the surfactant and water content. The adsorption mechanism of MB is chemisorption dominated by strong electrostatic interaction between CH₃ groups of MB and oxygen atoms of Mt surfaces. Besides, physisorption also plays a minor role in MB orientations. It is found that the existence of cationic surfactants can slightly improve the adsorption capacity of MB only at higher water content through enlarging the interlayer space of Mt and reducing mobility of MB. However, there will be a negative impact on the reduction of adsorption sites for dyes especially at low water content. Our results provide a possible application for swelling clay minerals being a promising adsorbent for dyes-surfactants co-existing wastewater treatment.

The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning

During the last few years, important advances have been made in big data exploration, complex pattern recognition and prediction of complex variables. Machine learning (ML) algorithms can efficiently analyze voluminous data, identify complex patterns and extract conclusions. In chemical engineering, the application of machine learning approaches has become highly attractive due to the growing complexity of this field. Machine learning allows computers to solve problems by learning from large data sets and provides researchers with an excellent opportunity to enhance the quality of predictions for the output variables of a chemical process. Its performance has been increasingly exploited to overcome a wide range of challenges in chemistry and chemical engineering, including improving computational chemistry, planning materials synthesis and modeling pollutant removal processes. In this review, we introduce this discipline in terms of its accessible to chemistry and highlight studies that illustrate in-depth the exploitation of machine learning. The main aim of the review paper is to answer these questions by analyzing physicochemical processes that exploit machine learning in organic and inorganic pollutants removal. In general, the purpose of this review is both to provide a summary of research related to the removal of various contaminants performed by ML models and to present future research needs in ML for contaminant removal.