Metalated-bipyridine-based porous hybrid polymers with POSS-derived Si-OH groups for synergistic catalytic CO2 fixation.

Herein, we construct a new series of N-heterocyclic ligand bipyridine-based porous hybrid polymers (denoted Bpy-PHPs) from the Heck reaction of a rigid building unit octavinylsilsesquioxane (VPOSS) and 5,5'-dibromo-2,2'-bipyridine. Surprisingly, the typical sample Bpy-PHP-4 was found to be a metal-/halogen-free heterogeneous catalyst in the cycloaddition reaction of CO2 with a few epoxides under atmospheric pressure. After coordination with ZnBr2, the resultant ZnBr2@Bpy-PHP-4 afforded largely enhanced heterogeneous catalytic activities upon the conversion of carbon dioxide (CO2) and various epoxides into cyclic carbonates without using any co-catalysts under mild conditions. The moderate catalytic activities of Bpy-PHP-4 may be due to the presence of hydrogen bond donors (HBDs), i.e., polyhedral oligomeric silsesquioxane (POSS)-derived Si-OH groups and N active sites from Bpy linkers. In comparison, the high catalytic efficiency of ZnBr2@Bpy-PHP-4 should be attributed to the synergistic catalysis of Si-OH groups, N active atoms, and Bpy-coordinated ZnBr2. Moreover, the catalyst ZnBr2@Bpy-PHP-4 can be easily recovered and reused ten times without any significant loss of catalytic activities. This work affords an efficient metal-based porous hybrid polymer heterogeneous catalyst for the cycloaddition reaction of CO2 and epoxides under mild and co-catalyst-free conditions.

Rapid monitoring approaches for concentration process of lanqin oral solution by near-infrared spectroscopy and chemometric models.

Qualitative and quantitative detection methods based on near-infrared spectroscopy (NIRs) have been proposed in the process analysis of traditional Chinese medicine in recent years. In this study, rapid monitoring methods were developed for quality control of concentration process of lanqin oral solution (LOS). Partial least squares regression (PLSR) method was adopted to construct quantitative models for epigoitrin, geniposide, baicalin, berberine hydrochloride and density. Simultaneously, the genetic algorithm joint extreme learning machine (GA-ELM) was first applied in qualitative analysis of NIRs to distinguish end point of concentration process. Results of PLSR models were satisfactory with the relative standard error of calibration valued at 3.80%, 3.75%, 3.79%, 11.5% and 1.22% for epigoitrin, geniposide, baicalin, berberine hydrochloride and density respectively, and the residual predictive deviation values were higher than 3. For qualitative analysis, the GA-ELM model obtained 100% prediction accuracy. The PLSR quantitative models and the end point discrimination model constructed by GA-ELM correspond with the requirements of practical applications. The results indicate that NIRs in combination with chemometrics has great potential in improving the efficiency in production.