Covalent organic frameworks@ZIF-67 derived novel nanocomposite catalyst effectively activated peroxymonosulfate to degrade organic pollutants

Boosting peroxymonosulfate activation for complete removal of gatifloxacin by a bead-chain zeolitic imidazolate framework composite

A bead-chain metal-organic framework composite was designed and synthesized by assembling a zeolitic imidazolate framework (ZIF) onto manganese dioxide (MnO2) nanowires. The prepared catalyst MnO2@ZIF-X (X = 1, 2 and 3) was used to facilitate gatifloxacin (GAT) degradation by using potassium peroxymonopulfate (PMS) as an activator. MnO2@ZIF-2 exhibited excellent catalytic performance, achieving 100 % degradation of GAT (10 mg/L) in the presence of PMS (1 mM) in 15 min, and the toxicity of the majority of degradation intermediates decreased. Furthermore, the removal efficiency was maintained above 90 % throughout a wide pH range (3-11) and in the coexistence of anions ( [Formula: see text] , Cl-, SO42-). The main mechanism of the MnO2@ZIF-2/PMS system involves the synergistic effect of radicals and non-radicals (single linear oxygen and electron-mediated transfer), making the system highly resistant to interference from environmental matrices. Moreover, the GAT degradation pathway was elucidated through intermediate analysis and theoretical calculations. In particular, MnO2@ZIF-2 was well dispersed on a microporous filter membrane to create an immobilized membrane reactor that displayed excellent catalytic performance for the continuous degradation of GAT for 300 min. This work offers an avenue for the design of catalysts with good catalytic activity, particularly for PMS activation in antibiotic wastewater remediation.

Cefixime removal via WO3/Co-ZIF nanocomposite using machine learning methods

In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L-1, 3.13, 117.65 min, and 0.19 g L-1) and (5 mg L-1, 3, 120 min, and 0.19 g L-1), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment.

Efficient decontamination of organic pollutants from wastewater by covalent organic framework-based materials

Covalent organic frameworks (COFs), assembling through covalent bonds, are a rising class of porous materials. Nowadays, various COFs are widely applied in organic pollutants decontamination due to the outstanding capabilities of large surface area, multiple functional groups, porous structure, excellent absorptivity, flexible design and so on. This review concentrates on the applications of COFs in different decontamination technologies such as solid-phase extraction, membrane filtration and sieving, adsorption, and catalysis reaction. The factors influencing water chemistry, such as pH, temperature, salt concentration and natural organic matter, are summarized in terms of their impact on decontamination performance and the extraction mechanisms for the diverse analytes. The interaction mechanisms between COFs and organic pollutants were hydrogen bonding, π-π stacking, hydrophilic, hydrophobic, and electrostatic interactions. Furthermore, a perspective on current obstacles and upcoming developments of COFs for organic pollutant removal has been provided. Due to their adaptable and versatile design as well as elaborate and diverse functionalization, COFs possess significant possibility in ameliorating environmental pollution.

Ultrasensitive detection and efficient removal of mercury ions based on covalent organic framework spheres with double active sites

In present work, a new spherical covalent organic framework (TFPB-APTU COF) with good photoelectric property and double active sites (secondary amine (-NH-) group and sulfur (S) atom) was prepared for ultrasensitive detection and efficient removal of mercury ions (Hg2+). The -NH- group and S atom can capture free Hg2+ by coordination and chelation interaction, and the related steric hindrance effect reduces the photocurrent signal of the TFPB-APTU COF, resulting in the highly sensitive photoelectrochemical analysis of Hg2+ with a wide linear response range (0.01-100000 nM) and low detection limit (0.006 nM). On the other hand, the developed TFPB-APTU COF has large removal capacity (2692 mg g-1), good regeneration capability, and high removal speed for Hg2+ removal based on the double active sites (-NH- group and S atom), large specific surface area and porous spherical structure. The developed TFPB-APTU COF spheres show great potential in monitoring and treatment of environmental pollution of Hg2+.

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.