A two-step strategy for the transition-metalfree CÀ H functionalization of arenes using unsymmetrical iodonium salts as versatile synthetic linchpins is presented. The key to the success of this strategy is the identification of the 3,5-dimethyl-4-isoxazolyl (DMIX) group as a superior dummy ligand, which enables not only site-selective CÀ H functionalization to afford unsymmetrical iodonium salts, but also highly selective aryl transfer during the subsequent metal-free coupling reaction. Both electron-rich and moderately electrondeficient arenes can be converted into the iodonium salts through CÀ H functionalization, allowing for diverse structural elaboration by metal-free CÀ N, CÀ C, CÀ S, and CÀ O coupling.

The merging of good crystallinity and high dispersibility into two-dimensional (2D) layered crystalline polymers (CPs) still represents a challenge because a high crystallinity is often accompanied by intimate interlayer interactions that are detrimental to the material processibility. We herein report a strategy to address this dilemma using rationally designed three-dimensional (3D) monomers and regioisomerism-based morphology control. The as-synthesized CPs possess layered 2D structures, where the assembly of layers is stabilized by relatively weak van der Waals interactions between C–H bonds other than the usual π–π stackings. The morphology and dispersibility of the CPs are finely tuned via regioisomerism. These findings shed light on how to modulate the crystallinity, morphology, and ultimate function of crystalline polymers using the spatial arrangements of linking groups.

Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS). We selected the Pearson’s correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling.