Facile synthesis of Zn-based metal-organic framework in the presence of carboxymethyl cellulose: A safe carrier for ibuprofen

Carboxymethyl chitosan modification of cobalt-zinc bimetallic MOF for tetracycline hydrochloride removal: Exploration of the enhancement mechanism of the process

This study synthesized a carboxymethyl chitosan-modified bimetallic Co/Zn-ZIF (CZ@CMC) with strong hydrophilicity and adsorption performance via the one-pot method. Tetracycline hydrochloride (TCH) was used as the model contaminant to evaluate the adsorption and peroxymonosulfate (PMS) activation properties of CZ@CMC. Mechanism showed that the adsorption behavior occurred through pore filling, electrostatic attraction, surface complexation, hydrogen bonding, and π-π stacking. In addition, a CZ@CMC/PMS system was constructed, which had excellent catalytic performance. The hydrophilicity and selective adsorption properties of CMC conferred a greatly accelerated CZ@CMC in catalyzing the PMS process with kobs of 0.095 min-1, in which OH, 1O2, SO4-, O2-, and Co(III) were the main ROS which quenching tests, EPR, and chemical probe experiments verified. In addition, the degradation pathways of TCH were obtained utilizing DFT and HPLC-MS and analyzed to show that the system possessed a good detoxification capacity. This work is expected to provide a green, efficient, and stable strategy to enhance the adsorption properties of catalytic materials and subsequently their co-catalytic properties.

Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning

Metal-organic frameworks (MOFs) have been widely researched as drug delivery systems due to their intrinsic porous structures. Herein, machine learning (ML) technologies were applied for the screening of MOFs with high drug loading capacity. To achieve this, first, a comprehensive dataset was gathered, including 40 data points from more than 100 different publications. The organic linkers, metal ions, and the functional groups, as well as the surface area and the pore volume of the investigated MOFs, were chosen as the model’s inputs, and the output was the ibuprofen (IBU) loading capacity. Thereafter, various advanced and powerful machine learning algorithms, such as support vector regression (SVR), random forest (RF), adaptive boosting (AdaBoost), and categorical boosting (CatBoost), were employed to predict the ibuprofen loading capacity of MOFs. The coefficient of determination (R²) of 0.70, 0.72, 0.66, and 0.76 were obtained for the SVR, RF, AdaBoost, and CatBoost approaches, respectively. Among all the algorithms, CatBoost was the most reliable, exhibiting superior performance regarding the sparse matrices and categorical features. Shapley additive explanations (SHAP) analysis was employed to explore the impact of the eigenvalues of the model’s outputs. Our initial results indicate that this methodology is a well generalized, straightforward, and cost-effective method that can be applied not only for the prediction of IBU loading capacity, but also in many other biomaterials projects.