Publications

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A well-posed physics-based compact model for a three-terminal silicon–oxide–nitride–oxide–silicon (SONOS) synaptic circuit element is presented for use by neuromorphic circuit/system engineers. Based on technology computer aided design (TCAD) simulations of a SONOS device, the model contains a nonvolatile memristor with the state variable QM representing the memristor charge under the gate of the three-terminal element. By incorporating the exponential dependence of the memristance on QM and the applied bias V for the gate, the compact model agrees quantitatively with the results from TCAD simulations as well as experimental measurements for the drain current. The compact model is implemented through VerilogA in the circuit simulation package Cadence Spectre and reproduces the experimental training behavior for the source–drain conductance of a SONOS device after applying writing pulses ranging from –12 V to +11 V, with an accuracy higher than 90%.

Precise temperature determination is a significant challenge in extreme environments of dynamic compression studies. How can radiance measurements taken in high-pressure shock experiments constrain temperature in a meaningful and physically consistent way? Experiments maintaining sample compression against a transparent window can be tailored to present a uniform measurement area with uncertain spectral emissivity. We compare several methods to analyze radiance collected at multiple wavelengths, applying statistical methods and physical principles to improve temperature inference. With proper radiance collection and analysis, dynamic temperature uncertainties become comparable to thermomechanical ambiguities of the emitting surface.

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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.

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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