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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Phase engineering of layered anode materials during ion-intercalation in Van der Waal heterostructures

Transition metal dichalcogenides (TMDs) are a class of 2D materials demonstrating promising properties, such as high capacities and cycling stabilities, making them strong candidates to replace graphitic anodes in lithium-ion batteries. However, certain TMDs, for instance, MoS 2 , undergo a phase transformation from 2H to 1T during intercalation that can affect the mobility of the intercalating ions, the anode voltage, and the reversible capacity. In contrast, select TMDs, for instance, NbS 2 and VS 2 , resist this type of phase transformation during Li-ion intercalation. This manuscript uses density functional theory simulations to investigate the phase transformation of TMD heterostructures during Li-, Na-, and K-ion intercalation. The simulations suggest that while stacking MoS 2 layers with NbS 2 layers is unable to limit this 2H → 1T transformation in MoS 2 during Li-ion intercalation, the interfaces effectively stabilize the 2H phase of MoS 2 during Na- and K-ion intercalation. However, stacking MoS 2 layers with VS 2 is able to suppress the 2H → 1T transformation of MoS 2 during the intercalation of Li, Na, and K-ions. The creation of TMD heterostructures by stacking MoS 2 with layers of non-transforming TMDs also renders theoretical capacities and electrical conductivities that are higher than that of bulk MoS 2 .

25 ENERGY STORAGE↗
DOE OSTI2025

Filterbank Multicarrier Communications for High Mobility Underwater Acoustic Links

This paper presents a new method of designing prototype filters that are robust against time and frequency dispersive (i.e., doubly dispersive) underwater acoustic (UWA) channels. The proposed design uses a modified form of Slepian sequences as a basis set to arrive at a design that is maximally compact along both time and frequency/Doppler dimensions. This keeps the inter-symbol interference confined to a small number of adjacent data symbols, both across time and frequency dimensions. It also minimizes the ramp-up (at the beginning) and ramp-down (at the end) of each synthesized data packet. Here, we test our design by making use of the BELLHOP/VirTex channel simulator and compare its performance with that of the widely used orthogonal frequency division multiplexing (OFDM) method. Our emphasis is on scenarios where the channel impulse response extends to a few hundreds of milli-seconds and the relative speed of communicating vehicles can be as large as 10 m/s. We find that while OFDM fails in majority of these scenarios, the proposed design continues to work well in all the scenarios that we simulate.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to investigate performance of a Hopfield Neural Network (HNN) in in detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign. One data set contained a 137 Cs source, and another dataset contained a 131 I source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Modeling and Simulation of MC-Small Design

In order to design a lightweight, highly portable neutron multiplicity counter for use in higher risk regions by mobile field teams, a redesign of the MC-15 is being performed, with the goal of producing a detector below 30 lbs for ease of transport. To achieve this, multiple MCNP® simulations were run, evaluating the number and positioning of the He-3 tubes in the detector as well as the amount of high-density polyethylene (HDPE) surrounding the tubes. Based on these simulations, the proposed redesign of the MC-15 (aka MC-Small) possesses 11 tubes and is 13 in wide, 3.21 in thick and 13 in tall. Including the changes to the positioning of the tubes and the removal of HDPE from less sensitive regions of the detectors (i.e. bases of tubes, back of detector), this resulted in an efficiency of 0.56% when measuring a bare Cf-252 source 19.69 in away, only 28% less efficient (relatively) than the MC-15 while weighing 18 lbs, 12 lbs less than the weight of the MC-15.

42 ENGINEERING↗
DOE OSTI2023

Agilent CRADA (Abstract)

The CRADA between Agilent Technologies Inc. and Battelle will focus on five software components as listed below: Prototype 4D Feature Finding functionality with a particular focus on recovering low level features and extending the bottom end dynamic range of IM-MS technology. Compare and contrast developments to current 4D Feature Finding capabilities. Highlight important algorithmic aspects employed. Implement the PNNL saturation correction algorithm. Agilent will give PNNL the needed data file access API and assistance in understanding it implementation and any needed instrumental aspects. Supported high resolution products to include Agilent’s TOF, QTOF and IM-QTOF mass spectrometers. PNNL will then work with Agilent to benchmark performance. Implementation of the PNNL Hadamard de-multiplexing algorithm. Agilent will give provide PNNL the needed date file access API access and as needed assistance in understanding the current Agilent multiplexed IM offering. PNNL will then work with Agilent on benchmark performance. Add ion mobility collision cross sections to existing and new metabolomic libraries for data analysis with Agilent’s informatics program MPP/ID Browser. PNNL will work with Agilent to create a software pipeline that takes data from chemical and metabolic standards and properly formats it for inclusion in MPP accessible libraries, using the collision cross section as a new separation dimension. Improvements of MPP multidimensional matching to identify metabolomic features using multiple characteristics beyond retention time and accurate mass. Most significantly matching will include analyte collision cross section with proposed support for sample fraction or RapidFire cartridge and fragmentation spectra. PNNL will work with Agilent to modify and improve the current MPP analysis pipeline to allow for creating, aligning, and identifying MS features defined by accurate mass, collision cross section and chromatographic retention time. As additional criteria such as fraction or RapidFire cartridge type are supported in the identification process, then they also will become part of the automation workflow. This includes the automation of said system to work with command line program (i.e. not a GUI) sufficient for programmatic execution in a pipeline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Aerial Lidar and Geomorphic Change Detection Software- Tools for Monitoring Canyon Sediments at LANL - 18668

The Los Alamos National Laboratory Environmental Remediation program has conducted extensive geomorphic surveys of sediment deposits in the canyon bottoms within and surrounding the facility. The goal of the geomorphic surveys was to provide basic information needed to characterize chemical inventories of the sediments downstream of historical release sites. Sediment units were mapped based on depositional setting and age. Representative characterization samples were collected for each unit and submitted for chemical analysis. Chemical inventories were estimated by calculating the total volume of each depositional unit and multiplying by average chemical concentration. This method of characterization provides a spatial distribution of chemical data based on fluvial processes. In general, for the canyon bottom sediments, the higher concentrations of industrial chemicals were associated with fine-grained floodplain deposits closest to the source. Since initial characterization, many of the reaches have undergone changes in response to post-wildfire floods and severe rain events. These events impacted the channel and floodplain deposits. Much of the elevated concentrations are confined to the over-bank deposits distal from the primary channel. In order to ensure that the sediment units containing elevated chemical concentrations are stable, repeat aerial light detection and ranging (LiDAR) surveys are conducted and changes are assessed using the Geomorphic Change Detection (GCD) plug-in for ArcGIS. These tools provide high confidence uncertainty estimates and extensive areal coverage. This is a valuable method for long-term monitoring of surface sediments with low-level but widespread contaminant distributions. All of the monitored canyon bottom reaches pass risk assessment and present technically infeasible sediment removal options. Following the 2014 monsoon season, aerial LiDAR data has been collected annually and the resulting digital elevation models (DEMs) processed using the GCD method. Changes are identified using GCD and are verified by field walk downs. If erosion is identified in areas which are vulnerable to mobilizing contaminated sediment, enhanced controls can be installed to stabilize banks or other areas with high erosion potential. Annual sediment monitoring samples are collected where there is evidence for flow and deposition within the past year. Since 2014, no severe floods have been catalogued using the LiDAR surveys and GCD software. The recent rainfall/runoff events have not caused major changes to the canyon bottom channels and floodplains. Geomorphic processes identified in the monitoring results include channel aggradation and incision that over the course of a monsoon season results in minor changes to the system. Other active processes contributing to observed changes are minor slides, flows, slumps, and falls of unconsolidated sediment on steep bedrock or soil surfaces. A comprehensive baseline has been established by geomorphic mapping and characterization sampling. Periodic monitoring consists of LiDAR surveys and GCD analysis which offers an effective and defensible methodology for documenting stability of surface sediments. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Implementing Mixed Reality Tools to Support Mission Delivery at Hanford - 20520

Mission Support Alliance's (MSA) Public Works and Information Systems organizations have been working with Microsoft Corporation to develop and implement mixed reality operational solutions using their HoloLens technology. This collaborative effort is intended to drive innovative solutions and significantly improve MSA's efficiency in performing work at the Hanford Site. HoloLens, a mixed reality tool, is a commercial off-the-shelf technology that combines a head-mounted viewing screen with applications to help people and organizations learn, communicate, and collaborate more effectively through the use of mixed reality. https://microsoft.com/en-us/hololens MSA's initial pilot successfully demonstrated its ability to use mixed reality utilizing HoloLens' advanced features in a variety of ways, illustrating the potential efficiency gains previously stated as goals of this pilot. These achievements include the development of proprietary technology that allows synchronization between a HoloLens device and a mobile device or alternate global positioning system (GPS) device. This synchronization enables the HoloLens device to access real time GPS location data anywhere on the Hanford Site. This real time location data, combined with MSA's improved Hanford Geographical Information System (GIS) data, enables the mixed reality pilot application to identify underground utility systems as well as related utility attributes. This ability will aid utility workers in several areas, including excavation activities, the placement of large cranes, identifying interactions and conflicts with future underground utility placements and, eventually, recognizing and de-conflicting tank waste transfer system valve alignments. The HoloLens mixed reality application is also able to provide full or small scale holographic images of a facility's digital twin. This feature will allow MSA's Water and Sewer Utilities team to train and validate procedures associated with the new water treatment facility virtually, before it is constructed. This reduces the amount of time it takes to complete the training and procedure validation, and accelerates the overall construction schedule. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Materials for Ultra‐Coherent, Mobile, Electron‐Spin Qubits

This research project has had the goal of gaining a better understanding of the physics of electrons bound to the surface of superfluid helium from both experimental and theoretical perspectives. It has particularly been aimed at two areas which had not been well studied: the relaxation and decoherence of the spin of the electrons on the helium surface and how the properties of underlying metallic layers affect the behavior of the electrons when the helium covering the metal is thin. This work is motivated in part by interest in using the spin of these electrons as a quantum bit, or qubit. Low levels of decoherence are advantageous for qubits, and moving the electrons, as one might do in a quantum processor, will be easiest if thin helium films can be employed. It had been suggested that spin decoherence should be very weak for electrons bound to superfluid He, but before this work there have been no quantitative studies of spin relaxation and decoherence. It is especially important to know how moving the electrons across the helium surface would affect their spin coherence. Calculations performed as part of this project show that the Rashba effective magnetic field, the mechanism which limits the spin coherence of mobile electrons in silicon-based devices (an actively pursued qubit technology), is exceptionally weak for electrons bound to helium. This project has identified other decoherence mechanisms which are stronger, but still weak compared to analogous silicon-based structures. Calculated spin coherence times for mobile electrons approach one day, as compared to microseconds in silicon. With coherence times of this magnitude, the spin qubit errors on helium will be completely dominated by errors in the quantum gates. In related work, the possibility of using an artificial spin-orbit interaction (a gradient magnetic field) for quantum operations on the electrons spins was considered. The calculations show that a moderate gradient field, small enough to be generated by a narrow superconducting wire, will enable high-fidelity quantum operations on electrons held in lithographically-defined quantum dots by driving them with a microwave electric field. The spin and motional coherence of the electrons is sufficient to allow high-fidelity 2-qubit quantum operations between electrons in neighboring quantum dots. As an outgrowth of experiments aiming to measure electron spin coherence it was discovered that very high densities of electrons can be stably supported on thin helium films coating ultra-smooth amorphous metallic layers. The measured densities are high enough that the electron system has almost certainly transitioned from an ordered array of electrons, known as a Wigner crystal (ordered by the electrons’ mutual repulsion), to a quantum fluid known as a Fermi liquid. This transition has been a subject of intense interest for over 40 years, since the electron Wigner crystal was first observed with electrons bound to superfluid helium, but it has never been unambiguously observed. Experiments are still underway in these new structures to definitively determine whether true quantum melting of the Wigner crystal has been demonstrated. This work has also catalyzed the development of a new approach for measuring the transport of electrons across very thin helium films, as will be needed for some of the quantum computing applications. The high electron density experiments as well as experiments with electrons bound in quantum dots have led to new techniques which may enable spin coherence measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements

Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges, including intermittent observations of the dynamical process, loss of communication links due to mobility and packet drops, and the potential for malicious or faulty behavior by some of the agents. The main contribution of this paper is the development of resilient, fully-distributed, and provably correct state estimation algorithms that simultaneously account for each of the above considerations, and in turn, offer a general framework for reasoning about state estimation problems in dynamic, failure-prone and adversarial environments. Specifically, we develop a simple switched linear observer for dealing with the issue of time-varying measurement models, and resilient filtering techniques for dealing with worst-case adversarial behavior subject to time-varying communication patterns among the agents. Here, our approach considers both communication patterns that recur in a deterministic manner, and patterns that are induced by random packet drops. For each scenario, we identify conditions on the dynamical system, the patrols, the nominal communication network topology, and the failure models that guarantee applicability of our proposed techniques. Finally, we complement our theoretical results with detailed simulations that illustrate the efficacy of our algorithms in the presence of the technical challenges described above.

42 ENGINEERING↗
DOE OSTI2022

liionpack: A Python package for simulating packs of batteries with PyBaMM

Electrification of transport and other energy intensive activities is of growing importance as it provides an underpinning method to reduce carbon emissions. With an increase in reliance on renewable sources of energy and a reduction in the use of more predictable fossil fuels in both stationary and mobile applications, energy storage will play a pivotal role and batteries are currently the most widely adopted and versatile form. Therefore, understanding how batteries work, how they degrade, and how to optimize and manage their operation at large scales is critical to achieving emission reduction targets. The electric vehicle (EV) industry requires a considerable number of batteries even for a single vehicle, sometimes numbering in the thousands if smaller cells are used, and the dynamics and degradation of these systems, as well as large stationary power systems, is not that well understood. As increases in the efficiency of a single battery become diminishing for standard commercially available chemistries, gains made at the system level become more important and can potentially be realised more quickly compared with developing new chemistries. Mathematical models and simulations provide a way to address these challenging questions and can aid the engineer and designers of batteries and battery management systems to provide longer lasting and more efficient energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2018

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recommender systems and autonomous mobility. There also have been applications to the search for new materials that draw upon big-data classification problems. Though, when it comes to physical systems governed by conservation laws, the role of machine learning has been more limited. Here, we present our recent work in exploring the role of machine learning methods in discovering, or aiding, the search for physics. Specifically, we focus on using machine learning algorithms to represent high-dimensional free energy surfaces with the goal of identifying precipitate morphologies in alloy systems. Traditionally, this problem has been approached by combining phase field models, which impose first-order dynamics, with elasticity, to traverse a free energy landscape in search of minima. Equilibrium precipitate morphologies occur at these minima. Here, we exploit the machine learning methods to represent high-dimensional data, combined with surrogate optimization, sensitivity analysis and multifidelity modeling as an alternate framework to explore phenomena controlled by energy extremization. This combination of data-driven methods offers an alternative to the imposition of first-order dynamics via phase field methods, and represents one aspect of applying machine learning to materials physics.

42 ENGINEERING↗
DOE OSTI2025

Design, Preparation, and Execution of the 100-AV Field Test for the CIRCLES Consortium: Methodology and Implementation of the Largest Mobile Traffic Control Experiment to Date

This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The execution of the MVT experiment required a coordinated effort from multiple teams. This article details the meticulous planning process, the coordinated efforts of multiple teams, and the innovative use of a dynamic agent-based simulation framework for traffic evaluation. Here, the contributions of this work include demonstrating and providing a detailed roadmap for large-scale live traffic experiments, illustrating the lessons learned from the MVT experiment, and introducing the other articles in this issue and their complementary relationship in the MVT experiment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

The organic redox transistor for neuromorphic computing

Inspired by the in-memory computing architectures of biological systems, neuromorphic computing using crossbar arrays of artificial synapses based on non-volatile memory (NVM) devices with variable conductances has emerged as a new paradigm to enable massively parallel and ultra-low power computing hardware for data centric applications. Although inference has been demonstrated successfully using crossbars based on a variety of NMV technologies, efficient learning and scaling to large arrays (>10 6 elements) remains a challenge due to the synaptic elements' non-ideal electrical characteristics which degrades ANN accuracy. A further challenge is that in the conductive state memristors draw large currents >μA resulting in significant voltage drops in the interconnect wires and increased probability of failure in scaled arrays. We suggest the organic polymer redox transistor (RT) is an alternate approach that could solve many of these challenges, enabling both inference and parallel outer product updates, as recently demonstrated by Fuller et al. An RT consists of redox-active channel and gate electrodes in contact with a liquid or solid electrolyte. lon insertion through the electrolyte controls the channel electronic conductivity, while electron transfer through an external circuit maintains overall charge neutrality. Unlike a rechargeable battery, in the RT the voltage built-up across the electrolyte is kept to a minimum (typically <100 mV) by using the same material for the gate and channel. Elimination of the voltage offset simplifies integration of the RT into programmable arrays by enabling the use of various selectors. RTs based on inorganic and organic materials have been recently demonstrated with conductance tuning occurring at potentials of just a few mV and hundreds to thousands of linearly and symmetrically programmable conductance states, enabling near ideal accuracy in neural network simulations. Introduced in the 1980's, redox transistors with metallic gate electrodes and organic channel materials, also known as organic electrochemical transistors (OECTs), have been explored for a variety of applications such as chem- and bio-sensing, neural interfaces, and low cost printed circuits. A typical channel material for OECTs is the conducting polymer poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS). PEDOT is a p-type semiconducting polymer with mobile positively charged polarons that hop chain-to-chain.

Talin, Albert Alec↗
DOE OSTI2026

Cyber Labeling for Energy Industrial IoT

The U.S. Department of Energy’s (DOE) Office of Cybersecurity, Energy Security and Emergency Response (CESER), at the request of the Deputy National Security Advisor for Cyber and Emerging Technologies, Anne Neuberger, initiated research in 2023 to develop a cybersecurity labeling proof-of-concept for energy products to expand on the Federal Communications Commission’s (FCC) proposed U.S. Cyber Trust Mark program. DOE mobilized researchers from six National Laboratories to develop and gather feedback on a proof-of concept label for solar inverters and smart meters, which serve as representative products for market-facing energy sector Industrial Internet of Things (IIoT). This report details the research team’s process across two phases and the resulting findings, which include challenges facing cyber labeling programs and recommendations to implement an expanded IIoT cyber labeling program in the U.S.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2025

Evaluating Direct and Indirect Influence on EV Charging Stations Across the US

The adoption of new technology for electric vehicles (EV) and mobility applications can bring underappreciated vulnerabilities to the power grid. One area of potential fraud and adversarial influence is through the business ecosystem of startups that own and deploy EV technology. Yet, there are no models or analyses that map the network of organizations and people that have direct and indirect influence over technologies currently deployed in the grid. To fill this gap, we develop a multilayer network model to measure direct and indirect influence on EV charging stations. First, we create and adversarial socio-technical network (ASTN) model via a data fusion pipeline for different US regions of interest (ROI). Then, we develop an integrated ASTN for Chicago, Los Angeles, New York, and Philadelphia. We rank EV charging companies direct influence within each geographic region as well as indirect influence via social network analysis. While some companies have strong direct and indirect influence (i.e., ChargePoint) others show a mismatch between their influence over charging stations and their position within the social network. For example, Tesla has strong direct influence on stations and weak indirect influence over competitors. In contrast, 7Charge has weak direct influence over stations, but strong indirect influence over competitors.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2020

New candidates for regulated gene integrity revealed through precise mapping of integrative genetic elements

Abstract Integrative genetic elements (IGEs) are mobile multigene DNA units that integrate into and excise from host bacterial genomes. Each IGE usually targets a specific site within a conserved host gene, integrating in a manner that preserves target gene function. However, a small number of bacterial genes are known to be inactivated upon IGE integration and reactivated upon excision, regulating phenotypes of virulence, mutation rate, and terminal differentiation in multicellular bacteria. The list of regulated gene integrity (RGI) cases has been slow-growing because IGEs have been challenging to precisely and comprehensively locate in genomes. We present software (TIGER) that maps IGEs with unprecedented precision and without attB site bias. TIGER uses a comparative genomic, ping-pong BLAST approach, based on the principle that the IGE integration module (i.e. its int-attP region) is cohesive. The resultant IGEs from 2168 genomes, along with integrase phylogenetic analysis and gene inactivation tests, revealed 19 new cases of genes whose integrity is regulated by IGEs (including dut, eccCa1, gntT, hrpB, merA, ompN, prkA, tqsA, traG, yifB, yfaT and ynfE), as well as recovering previously known cases (in sigK, spsM, comK, mlrA and hlb genes). It also recovered known clades of site-promiscuous integrases and identified possible new ones.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A Conceptual Design for a Desktop Application to Support Inventory Reconciliation Activities

This paper describes desktop software being developed at Oak Ridge National Laboratory (ORNL) to empower users to reconcile observations about items at nuclear facilities more accurately, reliably, and quickly. Inventory activities at nuclear facilities are often conducted using pen and paper, which can be time-consuming, tedious, and susceptible to reading or transcription errors. The proposed inventory assistant would be a replacement for the paper-based process that could be used by International Atomic Energy Agency (IAEA) inspectors, nuclear facility operators, or treaty verification monitors to conduct an inventory of nuclear and non-nuclear items in a more timely and accurate manner. Garner, McGirl, and Whitaker previously reported their work about a conceptual design for a mobile app to assist users in the field. In general, the inventory assistant would ingest an inventory list, distribute assigned items from the inventory list to one or more mobile devices, enable inventory teams to record their observations in the field, and then enable an inventory lead to integrate and reconcile the observations to produce a final report. The assistant consists of two software components - one for the inventory teams to record observations in the field (In-Field Observations App [IFOA]) and one for the inventory lead to (1) distribute the inventory list to each team, (2) integrate the observations from each team, and (3) reconcile the inventory list with observations (Distribution, Integration, and Reconciliation Application [DIRA]). This paper reviews the inventory assistant workflow and describes the DIRA user experience in more detail. As an example, the authors have chosen to follow the use case of IAEA inspectors conducting item counting and tag checking activities of UF 6 cylinders at a gas centrifuge enrichment plant with a large number of UF 6 cylinders (e.g., thousands). These activities can currently require 30 - 40 person-days of inspection to complete. The authors believe an inventory assistant could allow the IAEA to complete item counting and tag checking using the global identifier or the operator’s barcode in 8 - 10 person-days of inspection. We would expect other users (e.g., facility operators or treaty verification monitors) to also benefit from significant time savings.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗