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Abstract Sulfide solid-state electrolytes (SSEs) are promising candidates to realize all solid-state batteries (ASSBs) due to their superior ionic conductivity and excellent ductility. However, their hypersensitivity to moisture requires processing environments that are not compatible with today’s lithium-ion battery manufacturing infrastructure. Herein, we present a reversible surface modification strategy that enables the processability of sulfide SSEs (e. g., Li₆PS₅Cl) under humid ambient air. We demonstrate that a long chain alkyl thiol, 1-undecanethiol, is chemically compatible with the electrolyte with negligible impact on its ion conductivity. Importantly, the thiol modification extends the amount of time that the sulfide SSE can be exposed to air with 33% relative humidity (33% RH) with limited degradation of its structure while retaining a conductivity of above 1 mS cm^-1for up to 2 days, a more than 100-fold improvement in protection time over competing approaches. Experimental and computational results reveal that the thiol group anchors to the SSE surface, while the hydrophobic hydrocarbon tail provides protection by repelling water. The modified Li₆PS₅Cl SSE maintains its function after exposure to ambient humidity when implemented in a Li_0.5In | |LiNi_0.8Co_0.1Mn_0.1O₂ASSB. The proposed protection strategy based on surface molecular interactions represents a major step forward towards cost-competitive and energy-efficient sulfide SSE manufacturing for ASSB applications. 

In this paper, we present a novel open-source electricity systems optimization tool--the Holistic Optimization Program for Electricity (HOPE)--to assess emerging generation technology, inform policy design, and support planning. With a highly transparent, interpretable and compact model design, HOPE easily allows user access and modification, serving its main goal to benefit users beyond engineer communities and facilitate collaboration across the science-policy boundary. By activating different modes, the current version of HOPE (v1.0) offers flexibility in serving as either a Generation and Transmission Expansion Planning tool (GTEP) or a Production Cost Modelling tool (PCM). It includes modelling features such as long-term resource investments, short-term system operations, and a detailed representation of policies across various levels of regulated institutions. This paper outlines the building blocks of the model and its software structure. Case study results from using HOPE for the state of Maryland as well as Pennsylvania-New Jersey-Maryland (PJM) footprint are also provided. 

National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia−Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions. 

Abstract The concept of employing highly concentrated electrolytes has been widely incorporated into electrolyte design, due to their enhanced Li‐metal passivation and oxidative stability compared to their diluted counterparts. However, issues such as high viscosity and sub‐optimal wettability, compromise their suitability for commercialization. In this study, we present a highly concentrated dimethyl ether‐based electrolyte that appears as a liquid phase at ambient conditions via Li⁺‐ solvents ion‐dipole interactions (Coulombic condensation). Unlike conventional high salt concentration ether‐based electrolytes, it demonstrates enhanced transport properties and fluidity. The anion‐rich solvation structure also contributes to the formation of a LiF‐rich salt‐derived solid electrolyte interphase, facilitating stable Li metal cycling for over 1000 cycles at 0.5 mA cm⁻², 1 mAh cm⁻²condition. When combined with a sulfurized polyacrylonitrile (SPAN) electrode, the electrolyte effectively reduces the polysulfide shuttling effect and ensures stable performance across a range of charging currents, up to 6 mA cm⁻². This research underscores a promising strategy for developing an anion‐rich, high concentration ether electrolyte with decreased viscosity, which supports a Li metal anode with exceptional temperature durability and rapid charging capabilities. 

In Proceedings of the 33rd USENIX Security Symposium (USENIX Security), Philadelphia, PA, USA, August 14-16, 2024. 

Computational fluid dynamics (CFD) simulations are broadly used in many engineering and physics fields. CFD requires the solution of the Navier–Stokes (N-S) equations under complex flow and boundary conditions. However, applications of CFD simulations are computationally limited by the availability, speed, and parallelism of high-performance computing. To address this, machine learning techniques have been employed to create data-driven approximations for CFD to accelerate computational efficiency. Unfortunately, these methods predominantly depend on large labeled CFD datasets, which are costly to procure at the scale required for robust model development. In response, we introduce a weakly supervised approach that, through a multichannel input capturing boundary and geometric conditions, solves steady-state N-S equations. Our method achieves state-of-the-art results without relying on labeled simulation data, instead using a custom data-driven and physics-informed loss function and small-scale solutions to prime the model for solving the N-S equations. By training stacked models, we enhance resolution and predictability, yielding high-quality numerical solutions to N-S equations without hefty computational demands. Remarkably, our model, being highly adaptable, produces solutions on a 512 × 512 domain in a swift 7 ms, outpacing traditional CFD solvers by a factor of 1,000. This paves the way for real-time predictions on consumer hardware and Internet of Things devices, thereby boosting the scope, speed, and cost-efficiency of solving boundary-value fluid problems.