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Polyolefins such as polyethylenes and polypropylenes are the most‐produced plastic waste globally, yet are difficult to convert into useful products due to their unreactivity. Pyrolysis is a practical method for large‐scale treatment of mixed, contaminated plastic, allowing for their conversion into industrially‐relevant petrochemicals. Metal–organic frameworks (MOFs), despite their tremendous utility in heterogeneous catalysis, have been overlooked for polyolefin depolymerization due to their perceived thermal instabilities and inability of polyethylenes and polypropylenes to penetrate their pores. Herein, we demonstrate the viability of UiO‐66 MOFs containing coordinatively‐unsaturated zirconium nodes, as effective catalysts for pyrolysis that significantly enhances the yields of valuable liquid and gas hydrocarbons, whilst halving the amounts of residual solids produced. Reactions occur on the Lewis‐acidic UiO‐66 nodes, without the need for noble metals, and yield aliphatic product distributions distinctly different from the aromatic‐rich hydrocarbons that can be obtained from zeolite catalysis. We also demonstrate the first unambiguous characterization of polyolefin penetration into UiO‐66 pores at pyrolytic temperatures, allowing access to the abundant Zr‐oxo nodes within the MOF interior for efficient C−C cleavage. Our work highlights the potential of MOFs as highly‐designable heterogeneous catalysts for depolymerisation of plastics, which can complement conventional catalysts in reactivity. [ABSTRACT FROM AUTHOR]

Despite theoretical predictions of exceptional gravimetric energy densities in Li‐O2 batteries (LOBs), the current research forefront faces challenges, including high charge overpotential, cathode clogging, parasitic reactions, and Li anode corrosion. Herein, benzo‐crown ethers (BCEs) with varying cavity sizes are used as electrolyte additives to exploit their strong binding toward Li+ and promote the solution growth of Li2O2 with reduced particle size. Notably, the cell with benzo‐18‐crown‐6 ether (B18C6) enables the largest discharge capacity of 14948 mAh g−1. Upon charging, these additives accelerate Li2O2 oxidation through the strong binding with Li+ and the extended electrolyte/Li2O2 interface, resulting in improved reversibility, reduced charge overpotential, and prolonged cycle life. Besides, these additives also stabilize the Li anode by regulating Li+ migration and electron exchange, reducing dendritic growth and anode corrosion. This work presents insights into the rational design of BCEs as additives for high‐performance LOBs. [ABSTRACT FROM AUTHOR]

Climate change and land use change are the two main factors affecting the regional water cycle and water resources management. However, runoff studies in the karst basin based on future scenario projections are still lacking. To fill this gap, this study proposes a framework consisting of a future land use simulation model (FLUS), an automated statistical downscaling model (ASD), a soil and water assessment tool (SWAT) and a multi-point calibration strategy. This frameword was used to investigate runoff changes under future climate and land use changes in karst watersheds. The Chengbi River basin, a typical karst region in southwest China, was selected as the study area. The ASD method was developed for climate change projections based on the CanESM5 climate model. Future land use scenarios were projected using the FLUS model and historical land use data. Finally, the SWAT model was calibrated using a multi-site calibration strategy and was used to predict future runoff from 2025–2100. The results show that: (1) the developed SWAT model obtained a Nash efficiency coefficient of 0.83, which can adequately capture the spatial heterogeneity characteristics of karst hydro-climate; (2) land use changes significantly in all three future scenarios, with the main phenomena being the interconversion of farmland and grassland in SSPs1-2.6, the interconversion of grassland, farmland and artificial surfaces in SSPs2-4.5 and the interconversion of woodland, grassland and artificial surfaces in SSPs5-8.5; (3) the average annual temperature will show an upward trend in the future, and the average annual precipitation will increase by 11.53–14.43% and (4) the future annual runoff will show a significant upward trend, with monthly runoff mainly concentrated in July–September. The variability and uncertainty of future runoff during the main-flood period may increase compared to the historical situation. The findings will benefit future water resources management and water security in the karst basin. [ABSTRACT FROM AUTHOR]

Peroxodisulfate (PDS), peroxymonosulfate (PMS), and hydrogen peroxide (H2O2) might coexist in a persulfate system. It leads to the mutual interference in concentration determination due to their similar structures. Simultaneous detection of the three peroxides involves limited reporting. Herein, a multi-step iodometry was established to simultaneously determine the concentrations of PDS, PMS, and H2O2 coexisting in a solution. Firstly, molybdate–NaHCO3-buffered iodometry was proposed to uplift the overall detection of peroxides since the recovery rate of H2O2 was unexpectedly lower in the peroxide mixture than in the single H2O2 solution with reported NaHCO3-buffered iodometry. Then, multi-step iodometry was proposed based on the established molybdate–NaHCO3-buffered iodometry using the combination with catalase and revised acetate-buffered iodometry (pH 3). The multi-step iodometry determined the coexisting PMS, PDS, and H2O2 with the recovery rate of 95–105% and a standard deviation of ≤7% of two replicates at the individual centration of 13–500 μmol∙L−1. The recovery rates of peroxides were within 95–105% at pH 3–11 and within 90–110% in the presence of Cl− (0–150 mg∙L−1), F− (0–1.5 mg∙L−1), SO42− (0–150 mg∙L−1), or NO3− (0–20 mg∙L−1). The recovery rate of H2O2 was lowered down to 91% or 87% in the sample containing 100 mg/L Ca2+ or Mg2+, respectively, but was lifted up to 100% or 93% once pretreated by adding 0.11–1.06 g∙L−1 Na2CO3. In the background of tap water, surface water, and ground water, peroxides were all detected in 90–110%, which indicates the applicability of multi-step iodometry in real waters. [ABSTRACT FROM AUTHOR]

Magnesium metal anodes have attracted widespread attention for their high volumetric capacity and natural abundance, but are precluded from practical applications by poor rate capability and limited lifespan due to sluggish ion‐transfer kinetics and uneven deposition behavior. Herein, for the first time a grain‐boundary‐rich triphasic artificial hybrid interphase, consisting of Sb metal, Mg3Sb2 alloy, and MgCl2, is designed on Mg anode surface by a facile solution treatment method, enabling high‐rate and long‐cycle Mg plating/stripping behavior. The triphasic artificial hybrid interphase affords high magnesiophilicity and ionic conductivity to reduce the energy barriers for Mg2+ desolvation and deposition. Meanwhile, the abundant grain boundaries redistribute Mg2+ flux at the electrode‐electrolyte interface and guide uniform Mg deposition. Accordingly, the as‐designed Mg metal anode achieves ultralong cycling life of 350 h at a high current density of 5 mA cm−2 and a large areal capacity of 5 mAh cm−2, outperforming previously reported Mg metal anodes with artificial interphases. Full cells with Mo6 cathode also show extraordinary stability over a long lifespan of 8000 cycles at a high rate of 5 C. The rational artificial interphase design and the understanding of composition‐structure‐function relationships shed deep insights into the development of fast‐charging and long‐cycling Mg metal batteries. [ABSTRACT FROM AUTHOR]

The complex and unique topography of karst regions highlights the weaknesses of traditional hydrological models which fail to fully generalize them. The successive proposals of standard artificial intelligence (AI) models, pre-processing techniques, and post-processing methods have provided new opportunities to enhance the accuracy of runoff prediction in karst areas. In this study, first, the BP neural network model and the Elman neural network model were used for runoff prediction. Then, the performance of four coupled models—formed by combining two AI pre-processing techniques, Empirical Modal Decomposition (EMD) and Ensemble Empirical Modal Decomposition (EEMD), with the previously mentioned AI models—was investigated. Finally, the accuracy of triple-coupled models, formed by applying the post-processing method of quantile mapping (QM) to the previous coupled models, was estimated. The Nash–Sutcliffe efficiency (NSE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the peak percentage of threshold statistics (PPTS) were selected to evaluate and analyze the forecasting results of the above models. The results demonstrated that the BP model had the best prediction effect of the standard AI models, the coupled forecasting models had better prediction accuracy than the standard AI models, and the triple-coupled QM–EMD–Elman model had the best forecasting effect with an NSE value of 0.73, MAPE value of 0.75, RMSE value of 34.60, and PPTS value of 2.36. [ABSTRACT FROM AUTHOR]