Efficient adsorption and facile photocatalytic degradation of organic dyes over H-bonded proton-transfer complex: An experimental and theoretical approach

DFT and comparative adsorption study of NiO, MnO, and Mn2NiO4 nanomaterials for the removal of amaranth dye from synthetic water

In the current study, NiO nanoparticles, MnO nanoparticles, and Mn2NiO4 nanocomposites (Ni-NPs, Mn-NPs and MN-NCs, respectively) were synthesized using a facile hydrothermal method, and their performance in the removal of amaranth (AM) dye from synthetic wastewater was compared. XRD, FTIR spectroscopy, SEM, BET analysis, and TGA were performed to characterize the produced catalysts. The effect of pertinent parameters, including pH, dosage of catalysts, temperature, and shaking speed on the uptake of AM was investigated through batch experiments. The MN-NCs showed ultrafast and high efficiency for AM removal compared to their counter parts Mn-NPs and Ni-NPs. Under ideal conditions, the highest adsorption efficiencies of AM onto Ni-NPs, Mn-NPs, and MN-NCs were calculated to be 80.50%, 93.85%, and 98.50%, respectively. The Langmuir isotherm fitted the experimental data of AM removal better as shown by the higher values of r 2, compared to the Freundlich isotherm, indicating monolayer type adsorption of AM. According to kinetic analyses, the adsorption of AM was best described by the pseudo-second-order kinetic model. Further, regeneration/recycling studies showed that MN-NCs retained 79% adsorption efficiency after four cycles. DFT experiments were also conducted to gain a deeper understanding of the process and behavior of AM adsorption. In conclusion, as Ni-NPs, Mn-NPs, and MN-NCs adsorb AM predominantly via electrostatic interaction, they can be applied for the removal of both cationic and anionic dyes by controlling the pH factor.

Molecular simulation-based insights into dye pollutant adsorption: A perspective review

Growing concerns about environmental pollution have highlighted the need for efficient and sustainable methods to remove dye contamination from various ecosystems. In this context, computational methods such as molecular dynamics (MD), Monte Carlo (MC) simulations, quantum mechanics (QM) calculations, and machine learning (ML) methods are powerful tools used to study and predict the adsorption processes of dyes on various adsorbents. These methods provide detailed insights into the molecular interactions and mechanisms involved, which can be crucial for designing efficient adsorption systems. MD simulations, detailing molecular arrangements, predict dyes' adsorption behaviour and interaction energies with adsorbents. They simulate the entire adsorption process, including surface diffusion, solvent layer penetration, and physisorption. QM calculations, especially density functional theory (DFT), determine molecular structures and reactivity descriptors, aiding in understanding adsorption mechanisms. They identify stable adsorption configurations and interactions like hydrogen bonding and electrostatic forces. MC simulations predict equilibrium properties and adsorption energies by sampling molecular configurations. ML methods have proven highly effective in predicting and optimizing dye adsorption processes. These models offer significant advantages over traditional methods, including higher accuracy and the ability to handle complex datasets. These methods optimize adsorption conditions, clarify adsorbent functionalization roles, and predict dye removal efficiency under various conditions. This research explores MD, MC, QM, and ML approaches to connect molecular interactions with macroscopic adsorption phenomena. Probing these techniques provides insights into the dynamics and energetics of dye pollutants on adsorption surfaces. The findings will aid in developing and optimizing new materials for dye removal. This review has significant implications for environmental remediation, offering a comprehensive understanding of adsorption at various scales. Merging microscopic data with macroscopic observations enhances knowledge of dye pollutant adsorption, laying the groundwork for efficient, sustainable removal technologies. Addressing the growing challenges of ecosystem protection, this study contributes to a cleaner, more sustainable future.

Bifunctional Cu(II)-based 2D coordination polymer and its composite for high-performance photocatalysis and electrochemical energy storage

Coordination polymers (CPs) have been widely proven as sacrificial electrode materials for energy storage applications because of their high porosity, specific surface area and tunable structural topology. In this work, a new 2D Cu(II)-based CP, formulated as [Cu2(btc)(μ-Cl)2(H2O)4]n (CP-1) (H3btc = benzene-1,3,5-tricarboxylic acid), fabrication of copper oxide nanoparticles (CuO NPs) and its composite (CuO@CP-1) were successfully synthesized using solvothermal, precipitation and mechanochemical grinding approaches. Single-crystal X-ray analysis authenticated a two-dimensional (2D) layered network of CP-1. Further, CP-1, CuO NPs and composite were characterized by diffraction (Powder-XRD), spectroscopic (FTIR), microscopic (SEM), and thermal (TGA) techniques. The porosity and surface behavior of CP-1 and the composite were demonstrated using BET analyzer. Topological simplification of CP-1 shows a 3-c connected hcb periodic net. The photocatalytic behavior of CP-1 was examined over methyl red (MR) dye in the presence of sunlight and showed a promising degradation efficiency of 96.80%. The electrochemical energy storage properties of CP-1, CuO NPs and composite were investigated using cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) analysis under aqueous 1 M H2SO4 electrolyte. The electrochemical results show better charge storage performance of CP-1 with a specific capacitance of 602.25 F g-1 at 1 A g-1 current density by maintaining a retention of up to 84.51% after 5000 cycles at 10 A g-1 current density. Comparative electrochemical studies reveal that CP-1 is a promising electrode material for energy storage.

Adsorption of amphetamine on deep eutectic solvents functionalized graphene oxide/metal-organic framework nanocomposite: Elucidation of hydrogen bonding and DFT studies

The efficient and selective removal of amphetamine (AMP) from water bodies is significant for environmental remediation. In this study, a novel strategy for screening deep eutectic solvent (DES) functional monomers was proposed based on density functional theory (DFT) calculations. Using magnetic GO/ZIF-67 (ZMG) as substrates, three DES-functionalized adsorbents (ZMG-BA, ZMG-FA, and ZMG-PA) were successfully synthesized. The isothermal results showed that the DES-functionalized materials introduced more adsorption sites and mainly contributed to the formation of hydrogen bonds. The order of the maximum adsorption capacity (Q_m) was as follows: ZMG-BA (732.110 μg⋅g^-1) > ZMG-FA (636.518 μg⋅g^-1) > ZMG-PA (564.618 μg⋅g^-1) > ZMG (489.913 μg⋅g^-1). The adsorption rate of AMP on ZMG-BA was the highest (98.1%) at pH 11, which could be explained by the less protonation of -NH₂ from AMP being more favorable for forming hydrogen bonds with the -COOH of ZMG-BA. The strongest affinity of the -COOH of ZMG-BA for AMP was reflected in the most hydrogen bonds and the shortest bond length. The hydrogen bonding adsorption mechanism was fully explained by experimental characterization (FT-IR, XPS) and DFT calculations. Frontier Molecular Orbital (FMO) calculations showed that ZMG-BA had the lowest HOMO-LUMO energy gap (E_gap), the highest chemical activity and the best adsorption capability. The experimental results agreed with the results of theoretical calculations, proving the validity of the functional monomer screening method. This research offered fresh suggestions for the functionalized modification of carbon nanomaterials to achieve effective and selective adsorption for psychoactive substances.

Construction of a 3D Metal-Organic Framework and Its Composite for Water Remediation via Selective Adsorption and Photocatalytic Degradation of Hazardous Dye

In this work, a new bimetallic Na(I)-Zn(II) metal-organic framework (MOF), formulated as [Na₂Zn₃(btc)₂(μ-HCOO)₂(μ-H₂O)₈] _n (1) (H₃btc = benzene tricarboxylic acid), and its composite (ZnO@1) have been successfully synthesized using solvothermal and mechanochemical solid grinding methods. 1 and ZnO@1 were characterized by diffraction [single-crystal X-ray diffraction (XRD) and powder XRD], spectroscopic (ultraviolet-visible diffuse reflectance spectroscopy and Fourier transform infrared spectroscopy), microscopic (transmission electron microscopy), and thermal (thermogravimetric analysis) methods. The surface area and porosity of 1 were determined using a Brunauer-Emmett-Teller analyzer. Single-crystal diffraction of 1 confirms that Na1 and Zn2 have octahedral coordination environments, whereas Zn1 has a tetrahedral coordination geometry. Topological simplification of 1 shows a 3,6-connected kgd net. Na(I)-Zn(II) MOF (1) is crystallized with slight porosity and exhibits good tendency toward the encapsulation of zinc oxide nanoparticles (ZnO NPs). The photocatalytic behaviors of 1 and its composite (ZnO@1) were investigated over MB dye under sunlight illumination with promising degradation efficiencies of 93.69% for 1 and 97.53% for ZnO@1 in 80 min.