Publications

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Lithium-sulfur is a "beyond-Li-ion" battery chemistry attractive for its high energy density coupled with low-cost sulfur. Expanding to the MWh required for grid scale energy storage, however, requires a different approach for reasons of safety, scalability, and cost. Here we demonstrate the marriage of the redox-targeting scheme to the engineered Li solid electrolyte interphase (SEI), enabling a scalable, high efficiency, membrane-less Li-S redox flow battery. In this hybrid flow battery architecture, the Li anode is housed in the electrochemical cell, while the solid sulfur is safely kept in a separate catholyte reservoir and electrolyte is pumped over the sulfur and into the electrochemical cell. Electrochemically facile decamethylferrocene and cobaltocene are chosen as redox mediators to kick-start the initial reduction of solid S into soluble polysulfides and final reduction of polysulfides into solid Li2S, precluding the need for conductive carbons. On the anode side, a LiI and LiNO3pretreatment strategy encourages a stable SEI and lessens capacity fade, avoiding use of ion-selective separators. Complementary materials characterization confirms the uniform distribution of LiI in the SEI, while SEM confirms the presence of lower surface area globular Li deposition and UV-vis corroborates evolution of the polysulfide species. Equivalent areal loadings of up to 50 mgScm-2(84 mAh cm-2) are demonstrated, with high capacity and voltage efficiency at 1-2 mgScm-2(973 mAh gS-1and 81.3% VE in static cells and 1142 mAh gS-1and 86.9% VE in flow cells). These results imply that the fundamental Li-S chemistry and SEI engineering strategies can be adapted to the hybrid redox flow battery architecture, obviating the need for ion-selective membranes or flowing carbon additives, and offering a potential pathway for inexpensive, scalable, and safe MWh scale Li-S energy storage.

Recent high profile cyber attacks on critical infrastructures have raised awareness about the severe and widespread impacts that these attacks can have on everyday life. This awareness has spurred research into making industrial control systems and other cyber-physical systems more resilient. A plethora of cyber resilience metrics and frameworks have been proposed for cyber resilience assessments, but these approaches typically assume that data required to populate the metrics is readily available, an assumption that is frequently not valid. This paper describes a new cyber experimentation platform that can be used to generate relevant data and to calculate resilience metrics that quantify how resilient specified industrial control systems are to specified threats. Demonstration of the platform and analysis process are illustrated through a use case involving the control system for a pressurized water reactor.

Recent high profile cyber attacks on critical infrastructures have raised awareness about the severe and widespread impacts that these attacks can have on everyday life. This awareness has spurred research into making industrial control systems and other cyber-physical systems more resilient. A plethora of cyber resilience metrics and frameworks have been proposed for cyber resilience assessments, but these approaches typically assume that data required to populate the metrics is readily available, an assumption that is frequently not valid. This paper describes a new cyber experimentation platform that can be used to generate relevant data and to calculate resilience metrics that quantify how resilient specified industrial control systems are to specified threats. Demonstration of the platform and analysis process are illustrated through a use case involving the control system for a pressurized water reactor.

A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.

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The purpose of this report is to document updates to the simulation of commercial vacuum drying procedures at the Nuclear Energy Work Complex at Sandia National Laboratories. Validation of the extent of water removal in a dry spent nuclear fuel storage system based on drying procedures used at nuclear power plants is needed to close existing technical gaps. Operational conditions leading to incomplete drying may have potential impacts on the fuel, cladding, and other components in the system. A general lack of data suitable for model validation of commercial nuclear canister drying processes necessitates additional, well-designed investigations of drying process efficacy and water retention. Scaled tests that incorporate relevant physics and well-controlled boundary conditions are essential to provide insight and guidance to the simulation of prototypic systems undergoing drying processes. This report documents testing updates for the Dashpot Drying Apparatus (DDA), an apparatus constructed at a reduced scale with multiple Pressurized Water Reactor (PWR) fuel rod surrogates and a single guide tube dashpot. This apparatus is fashioned from a truncated 5×5 section of a prototypic 17×17 PWR fuel skeleton and includes the lowest segment of a single guide tube, often referred to as the dashpot region. The guide tube in this assembly is open and allows for insertion of a poison rod (neutron absorber) surrogate.

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There has been ever-growing interest and engagement regarding net-zero and carbon neutrality goals, with many nations committing to steep emissions reductions by mid-century. Although water plays critical roles in various sectors, there has been a distinct gap in discussions to date about the role of water in the transition to a carbon neutral future. To address this need, a webinar was convened in April 2022 to gain insights into how water can support or influence active strategies for addressing emissions activities across energy, industrial, and carbon sectors. The webinar presentations and discussions highlighted various nuances of direct and indirect water use both within and across technology sectors (Figure ES-1). For example, hydrogen and concrete production, water for mining, and inland waterways transportation are all heavily influenced by the energy sources used (fossil fuels vs. renewable sources) as well as local resource availabilities. Algal biomass, on the other hand, can be produced across diverse geographies (terrestrial to sea) in a range of source water qualities, including wastewater and could also support pollution remediation through nutrient and metals recovery. Finally, water also influences carbon dynamics and cycling within natural systems across terrestrial, aquatic, and geologic systems. These dynamics underscore not only the critical role of water within the energy-water nexus, but also the extension into the energy-watercarbon nexus.

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