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This communication describes a strategy to synthesize stable deep blue FAPbBr3 quantum dots (QDs) by constructing a matrix structure. Amorphous Ni2+-based metal halide matrices can stabilize QDs from both chemical and physical factors, and Ni2+ doping can further enhance their structural stability due to lattice shrinking. Such deep blue QD films exhibit stable X-ray diffraction patterns and photoluminescence even after 245 days of storage. [ABSTRACT FROM AUTHOR]

Development of high‐performance and stable pure red perovskite light‐emitting devices (LEDs) promotes commercialization of perovskite. Benefiting from the quantum confinement effect, pure red CsPbI3 quantum dots (QDs) can avoid the color drift issue for mixed halide systems. Herein, a facile temperature‐dependent hot‐injection method combined with low temperature gradient centrifugation is employed to prepare 5.60 ± 0.05 nm pure red QDs. The QDs exhibit 642 nm photoluminescence (PL) with 37 nm full width at half maximum, and their Commission Internationale de l'Eclairage (CIE) coordinates located at (0.708, 0.292) match well with standard pure red for Rec. 2020. The PL half‐lifetime for pure red QDs solution is 210 days under ambient conditions, and both the X and Y coordinates of CIE only fluctuate within (0.002, 0.002). Finally, a simple NH4I post‐treatment to decrease ligand length further boosts the QDs conductivity, and the pure red LED exhibits 9.4% maximum external quantum efficiency. The electroluminescence spectra exhibits a sharp peak at 645 nm, and their CIE coordinates are located at (0.707, 0.290). Furthermore, the pure red LED exhibits both good working and color stability. Its half‐lifetime is 25.1 min and color drifts are only (0.002, 0.002) after 30.0 min of continuous working at 60 cd m−2 with a constant current density of 3.5 mA cm−2. [ABSTRACT FROM AUTHOR]

Most terrestrial models synchronously calculate net primary productivity (NPP) using the input climate variable, without the consideration of time-lag effects, which may increase the uncertainty of NPP simulation. Based on Normalized Difference Vegetation Index (NDVI) and climate data, we used the time lag cross-correlation method to investigate the time-lag effects of temperature, precipitation, and solar radiation in different seasons on NDVI values. Then, we selected the Carnegie–Ames–Stanford approach (CASA) model to estimate the NPP of China from 2002 to 2017. The results showed that the response of vegetation growth to climate factors had an obvious lag effect, with the longest time lag in solar radiation and the shortest time lag in temperature. The time lag of vegetation to the climate variable showed great tempo-spatial heterogeneities among vegetation types, climate types, and vegetation growth periods. Based on the validation using eddy covariance data, the results showed that the simulation accuracy of the CASA model considering the time-lag effects was effectively improved. By considering the time-lag effects, the average total amount of NPP modeled by CASA during 2001–2017 in China was 3.977 PgC a−1, which is 11.37% higher than that of the original model. This study highlights the importance of considering the time lag for the simulation of vegetation growth, and provides a useful tool for the improvement of the vegetation productivity model. [ABSTRACT FROM AUTHOR]