Quantum-Confined Perovskite Nanocrystals Enabled by Negative Catalyst Strategy for Efficient Light-Emitting Diodes.

The perovskite nanocrystals (PeNCs) are emerging as a promising emitter for light-emitting diodes (LEDs) due to their excellent optical and electrical properties. However, the ultrafast growth of PeNCs often results in large sizes exceeding the Bohr diameter, leading to low exciton binding energy and susceptibility to nonradiative recombination, while small-sized PeNCs exhibit a large specific surface area, contributing to an increased defect density. Herein, Zn2+ ions as a negative catalyst to realize quantum-confined FAPbBr3 PeNCs with high photoluminescence quantum yields (PL QY) over 90%. Zn2+ ions exhibit robust coordination with Br- ions is introduced, effectively retarding the participation of Br- ions in the perovskite crystallization process and thus facilitating PeNCs size control. Notably, Zn2+ ions neither incorporate into the perovskite lattice nor are absorbed on the surface of PeNCs. And the reduced growth rate also promotes sufficient octahedral coordination of PeNC that reduces defect density. The LEDs based on these optimized PeNCs exhibits an external quantum efficiency (EQE) of 21.7%, significantly surpassing that of the pristine PeNCs (15.2%). Furthermore, the device lifetime is also extended by twofold. This research presents a novel approach to achieving high-performance optoelectronic devices.

Asymmetric Solvents Regulated Crystallization-Limited Electrolytes for All-Climate Lithium Metal Batteries.

Electrolytes that can keep liquid state are one of the most important physical metrics to ensure the ions transfer with stable operation of rechargeable lithium-based batteries at a wide temperature window. It is generally accepted that strong polar solvents with high melting points favor the safe operation of batteries above room temperatures but are susceptible to crystallization at low temperatures (≤-40 °C). Here, a crystallization limitation strategy was proposed to handle this issue. We demonstrate that, although the high melting points of ethylene sulfite (ES, -17 °C) and fluoroethylene carbonate (FEC, ≈23 °C), their mixtures can avoid crystallization at low temperatures, which can be attributed to low intermolecular interactions and altered molecular motion dynamics. A suitable ES/FEC ratio (10 % FEC) can balance the bulk and interface transport of ions, enabling LiNi0.8 Mn0.1 Co0.1 O2 ||lithium (NCM811||Li) full cells to deliver excellent temperature resilience and cycling stability over a wide temperature range from -50 °C to +70 °C. More than 66 % of the capacity retention was achieved at -50 °C compared to room temperature. The NCM811||Li pouch cells exhibit high cycling stability under realistic conditions (electrolyte weight to cathode capacity ratio (E/C)≤3.5 g Ah-1 , negative to positive electrode capacity ratio (N/P)≤1.09) at different temperatures.

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs.

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.

Investigating long-term changes in surface water temperature of Dongting Lake using Landsat imagery, China.

Lake surface water temperature (LSWT) plays a crucial role in assessing the health of aquatic ecosystems. Variations in LSWT can significantly impact the physical, chemical, and biological processes within lakes. This study investigates the long-term changes in surface water temperature of the Dongting Lake, China. The LSWT is retrieved using Landsat thermal infrared imageries from 1988 to 2022 and validated with in situ observations, and the change characteristics of LSWT and near-surface air temperature (NSAT) as well as the spatial distribution characteristics of LSWT are analyzed. Additionally, the contribution rates of different meteorological factors to LSWT are quantified. The results show that the accuracy assessment of satellite-derived temperatures indicates a Nash-Sutcliffe efficiency coefficient (NSE) of 0.961, suggesting an accurate retrieval of water temperature. From 1988 to 2022, both the annual average LSWT and NSAT of Dongting Lake exhibit an increasing trend, with similar rates of warming. They both undergo a mutation in 1997 and have the main periods on the 11-year and 4-year time scales. The changes in NSAT emerge as one of the important factors contributing to variations in LSWT. Among the multiple meteorological factors, NSAT exhibits a significant correlation with LSWT (R = 0.822, α < 0.01). Furthermore, NSAT accounts for the highest contribution rate to LSWT, amounting to 67.5%. The distribution of LSWT within Dongting Lake exhibits spatial variations, with higher LSWT observed on the west part compared to the east part during summer, while lower LSWT occurs on the west part during winter. The findings of this study can provide a scientific understanding for the long-term thermal regimes of lakes and help advance sustainable lake management.

Realization of monolayer ZrTe5 topological insulators with wide band gaps.

Two-dimensional topological insulators hosting the quantum spin Hall effect have application potential in dissipationless electronics. To observe the quantum spin Hall effect at elevated temperatures, a wide band gap is indispensable to efficiently suppress bulk conduction. Yet, most candidate materials exhibit narrow or even negative band gaps. Here, via elegant control of van der Waals epitaxy, we have successfully grown monolayer ZrTe5 on a bilayer graphene/SiC substrate. The epitaxial ZrTe5 monolayer crystalizes in two allotrope isomers with different intralayer alignments of ZrTe3 prisms. Our scanning tunneling microscopy/spectroscopy characterization unveils an intrinsic full band gap as large as 254 meV and one-dimensional edge states localized along the periphery of the ZrTe5 monolayer. First-principles calculations further confirm that the large band gap originates from strong spin-orbit coupling, and the edge states are topologically nontrivial. These findings thus provide a highly desirable material platform for the exploration of the high-temperature quantum spin Hall effect.