Search for: All records

Climate extremes, such as hurricanes, combined with large-scale integration of environment-sensitive renewables, could exacerbate the risk of widespread power outages. We introduce a coupled climate-energy model for cascading power outages, which comprehensively captures the impacts of climate extremes on renewable generation, and transmission and distribution networks. The model is validated with the 2022 Puerto Rico catastrophic blackout during Hurricane Fiona – a unique system-wide blackout event with complete records of weather-induced outages. The model reveals a resilience pattern that was not captured by the previous models: early failure of certain critical components enhances overall system resilience. Sensitivity analysis on various scenarios of behind-the-meter solar integration demonstrates that lower integration levels (below 45%, including the current level) exhibit minimal impact on system resilience in this event. However, surpassing this critical level without pairing it with energy storage can exacerbate the probability of catastrophic blackouts. 

African Easterly Waves (AEWs) are synoptic-scale atmospheric disturbances that serve as precursors to tropical cyclones (TCs) in the North Atlantic and North Africa. As climate changes, TC activities are increasingly frequent, leading to exponentially growing socio-economic losses. So understanding the physical mechanisms governing the tropical cyclogenesis (TCG) of AEWs remains a crucial problem. Competing theoretical frameworks, including baroclinic instability, barotropic instability, and moisture-vortex instability (MVI) have been proposed, but their relative importance and temporal evolution during storm development remain unclear. In this study, machine learning algorithms are used to empirically analyze the governing mechanisms of AEW development based on 40 years of reanalysis data (1979-2018). We develop a computer vision framework utilizing convolutional neural networks (CNNs) and transformer architectures to identify developing AEWs (DAEWs) from non-developing AEWs (NDAEWs) based on wave-centered composites of key thermodynamic and dynamic variables for storm development. The model results suggest that the MVI framework is a critical factor for high classification accuracy in distinguishing developers from non-developers. 

A series of perovskite oxides (Ln = La, Pr, Nd, Gd; A = Ba, Sr) was investigated to understand the effects of A-site cation size on oxygen vacancy formation. Quasirandom mixed structures were generated using Alloy Theoretic Automated Toolkit (ATAT), followed by density functional theory (DFT) calculations. While mixing the orthorhombic structures with the hexagonal AMnO3 structures leads to lattices and global symmetries closer to cubic, the average volume generally increases with the average ionic size, and the local bond and angles exhibit more variations due to A-site mixing. DFT calculations and a statistical model were combined to predict oxygen reduction abilities. Thermogravimetric analysis (TGA) provided experimental validation of these predictions by measuring changes in oxygen non-stoichiometry under controlled conditions. Both indicated that larger A-site ionic size differences lead to greater, consistent with the larger variation in local structures, and enhanced redox capabilities. This combined computational-experimental approach highlights the importance of local structure variation, instead of average properties, in A-site cation engineering to optimize perovskite oxides for different devices relying on oxygen vacancy redox activity. 

Abstract Illegal trade in sharks and rays continues to undermine global conservation efforts, with enforcement often hampered by the inability to identify products to the species level. Here, we present a portable, cost-effective High-Resolution melt (HRM) assay for rapid DNA-based identification of elasmobranch species in trade. Using a reference library of 669 vouchered tissue samples collected from field operations and international market surveys, we validated the assay’s capacity to accurately differentiate at least 55 shark and ray species based on melt curve profiles, including 38 species listed under the Convention on International Trade in Endangered Species of Wild Fauna and Flora. Automated image classification enabled high-throughput identification with 99.2% accuracy. The assay yields results within two hours at a per-sample cost of $1.50, and is compatible with portable qPCR platforms, making it suitable for on-site applications. This approach represents a scalable molecular enforcement tool that can empower local authorities to monitor trade more effectively, support compliance with international regulations, and enhance global efforts to combat wildlife trafficking. 

Earth’s core-mantle segregation set the initial conditions for its subsequent evolution. However, the effect of water on core-mantle element partitioning remains poorly constrained. Using machine learning molecular dynamics simulations trained on quantum mechanical data, we show that increasing water content promotes magnesium partitioning into the metallic core, whereas silicon, iron, and hydrogen increasingly prefer the silicate mantle. On the basis of Earth’s core mass fraction and oxygen fugacity during core formation, a self-consistent hydrous core-mantle differentiation model yields a bulk Earth water content of ~0.23 weight % (equivalently ~10 ocean masses), a bulk Earth magnesium/silicon ratio of 1.16 ± 0.01, and a mantle magnesium/silicon ratio of 1.25 to 1.28. The initial core would contain 3.5 to 4.1 weight % silicon, 2.9 to 3.1 weight % oxygen, 0.11 to 0.14 weight % magnesium, and 0.04 to 0.10 weight % hydrogen, along with sulfur and carbon. We predict that super-Earths can retain large metallic cores even with several weight % water.