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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗
DOE OSTI2025

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Truck Platooning Performance with ADAS and Onboard Camera Data Describing Traffic Interactions

This project was part of the Characterizing Behaviors and Capabilities for Emerging Connected and Automated Vehicle Technologies, Sensors, and Connectivity project. The National Laboratory of the Rockies partnered with Cummins Inc. to collect data from Class 8 tractor trailer combinations in platoon (cooperative adaptive cruise control) operations on public roads in southern Indiana. Data collected include J1939 CAN bus, radar, intervehicle position, and video data. The video data could not be shared in the raw form, so they were processed to extract information on the other vehicles on the road, their relative positions, and intrusion events. This information was then columnized for modeling use and further enhanced by appending road information including road type, speed limit, altitude, and grade. The test route included free-flowing traffic, highway interchanges, and construction zones, as well as low-, medium-, and high-grade sections. Individual test conditions varied by day, with advanced driver-assistance system (ADAS) features engaged or disengaged and different combined vehicle masses tested in addition to uncontrolled variables such as weather and traffic interactions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Dynamic Response of a Semiactive Suspension System with Hysteretic Nonlinear Energy Sink Based on Random Excitation by means of Computer Simulation

This paper aims to investigate the property and behavior of the hysteretic nonlinear energy sink (HNES) coupled to a half vehicle system which is a nine-degree-of-freedom, nonlinear, and semiactive suspension system in order to improve the ride comfort and increase the stability in shock mitigation by using the computer simulation method. The HNES model is a semiactive suspension device, which comprises the famous Bouc–Wen (B-W) model employed to describe the force produced by both the purely hysteretic spring and linear elastic spring of potentially negative stiffness connected in parallel, for the half vehicle system. Nine nonlinear motion equations of the half vehicle system are derived in terms of the seven displacements and the two dimensionless hysteretic variables, which are integrated numerically by employing the direct time integration method for studying both the variables of vertical displacements, velocities, accelerations, chassis pitch angle, and the ride comfort and driver safety, respectively, based on the bump and random road inputs of the pseudoexcitation method as excitation signal. Simulation results show that, compared with the HNES model and the magnetorheological (MR) model coupled to the half vehicle system, the ride comfort and stability have been evidently improved. A successful validation process has been performed, which indicated that both the ride comfort and driver safety properties of the HNES model coupled to half vehicle significantly improved.

Chen, Hui↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Instantaneous mesh load factor ( K γ ) measurements in a wind turbine gearbox using fiber-optic strain sensors

The mesh load factor, K γ , describes how loads are shared between planet gears and has become one of the key design challenges in modern wind turbine gearboxes. Planet load sharing directly impacts tooth root stresses, a critical driver of torque density and gearbox reliability. Experimental evaluation of K γ is typically performed from sun gear tooth root strain gauge measurements, which are complex. Furthermore, such measurements can only provide an average value of load sharing. The present study describes an alternative method to evaluate the mesh load factor in wind turbine gearboxes based on fiber-optic strain sensors installed on the outer surface of the fixed ring gear. We present the results of an extensive measurement campaign to evaluate this novel sensing solution installed on the input planetary stage of a 2-MW wind turbine gearbox at the National Renewable Energy Laboratory's Flatirons Campus (Colorado, USA). The number of strain sensors on the ring gear was selected as an integer multiple of the number of planets, which has enabled an instantaneous evaluation of the mesh load factor. The effect of operating conditions on the planet load-sharing behavior of the gearbox has been investigated. The mesh load factor measured for operating conditions close to rated was below 1.05, well below IEC 61400-4 standard requirements.

17 WIND ENERGY↗
DOE OSTI2020

Patterned anodes with sub-millimeter spatial resolution for large-area MCP-based photodetector systems

Micro-channel-plate-based photo-detectors are unique in being capable of covering very large areas such as those required in elementary particle and nuclear physics, while providing sub-millimeter space resolution, time resolutions of less than 10 picoseconds for charged particles, and time resolutions of 30 psec–50 psec for single photons. In such systems the electronic channel count is a major cost driver. Incorporating a capacitively-coupled anode allows the use of external pickup electrodes with patterns of individual channels optimized for occupancy, rate, and time/space resolution. The signal pickup antenna can be economically implemented as a printed circuit card with a 2-dimensional array of pads for high-occupancy/high-rate applications such as in particle colliders and medical imaging, or a 1-dimensional array of strips for a lower channel count in low-occupancy/low-rate applications such as large neutrino detectors. In this paper we present pad patterns that enhance signal-sharing between pads to lower the channel count per unit area in large-area systems by factors up to 4, while maintaining spatial resolutions of approximately 100 to 200 μ m for charged particles and 400 to 1000 μ m for single photons. Patterns that use multiple signal layers in the signal-pickup board can lower the channel count even further, moving the scaling behavior in the number of pads versus total area from quadratic to linear.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

Soil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1-D diffusion-reaction model to calculate depth-resolved CO 2 production rates from soil CO 2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016-2018. While soil respiration correlated with temperature on diel timescales (p<0.05), seasonal variability was dominated by soil moisture and plant phenology (p<0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore-summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Lastly, we observed significant late-season CO 2 fluxes from the deep sub-soil (>165 cm) that support growing evidence for the importance of sub-soil processes in driving integrated respiration fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Emergence of Diverse Failure Patterns in Weathering‐Induced Landslides: Insights From Particle Finite Element Simulations

Weathering is a fundamental driver of landslide evolution over geological timescales. Despite its ubiquity and importance, quantifying how weathering drives the progressive destabilization of rock slopes remains challenging. In this work, we develop a unified computational framework based on the particle finite element method to investigate the evolution of weathering‐induced landslides, from long‐term weathering to short‐term slope failure and runout dynamics. The framework integrates key processes, including weathering front propagation, time‐dependent strength degradation, rupture surface development, and post‐failure runout dynamics. Through numerical simulation experiments, we elucidate how interactions among weathering characteristics (type, intensity, and rate law), bedrock strength, fracture distribution, and slope geometry govern the failure modes and kinematics of weathering‐induced landslides. Simulations show that matrix‐dominated weathering leads to shallow translational failures, whereas fracture‐dominated weathering produces deep‐seated rotational and compound landslides. Pre‐existing fractures and slope morphology also strongly influence the movement of destabilized landmasses, affecting the failure pattern (e.g., kinematic mode and rupture surface geometry) and post‐failure behavior (e.g., runout velocity). We further demonstrate that the failure time and volume of weathered slopes are governed by the competition between gravitational driving forces and cohesive resisting forces during progressive destabilization. These findings provide new insights into the fundamental mechanisms that drive the emergence of diverse failure patterns of weathering‐induced landslides with important implications for landslide hazard assessment.

Wang, Liang [Eidgenoessische Technische Hochschule↗
DOE OSTI2020

Evaluation of Methods for Causal Discovery in Hydrometeorological Systems

Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph-based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Anatomy of an agricultural antagonist: Feeding complex structure and function of three xylem sap‐feeding insects illuminated with synchrotron‐based 3D imaging

Abstract Many insects feed on xylem or phloem sap of vascular plants. Although physical damage to the plant is minimal, the process of insect feeding can transmit lethal viruses and bacterial pathogens. Disparities between insect‐mediated pathogen transmission efficiency have been identified among xylem sap‐feeding insects; however, the mechanistic drivers of these trends are unclear. Identifying and understanding the structural factors and associated integrated functional components that may ultimately determine these disparities are critical for managing plant diseases. Here, we applied synchrotron‐based X‐ray microcomputed tomography to digitally reconstruct the morphology of three xylem sap‐feeding insect vectors of plant pathogens: Graphocephala atropunctata (blue‐green sharpshooter; Hemiptera, Cicadellidae) and Homalodisca vitripennis (glassy‐winged sharpshooter; Hemiptera, Cicadellidae), and the spittlebug Philaenus spumarius (meadow spittlebug; Hemiptera, Aphrophoridae). The application of this technique revealed previously undescribed anatomical features of these organisms, such as key components of the salivary complex. The visualization of the 3D structure of the precibarial valve led to new insights into the mechanism of how this structure functions. Morphological disparities with functional implications between taxa were highlighted as well, including the morphology and volume of the cibarial dilator musculature responsible for extracting xylem sap, which has implications for force application capabilities. These morphological insights will be used to target analyses illuminating functional differences in feeding behavior.

3D imaging↗
DOE OSTI2021

Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035

From daily commutes to cross-country road trips, millions of light-duty vehicles are on the road every day. The transportation sector is one of the United States’ largest sources of greenhouse gas emissions, and fuel is an important cost for drivers. The period from 2025-2035 could bring the most fundamental transformation in the 100-plus year history of the automobile. Battery electric vehicle costs are likely to fall and reach parity with internal combustion engine vehicles. New generations of fuel cell vehicles will be produced. Connected and automated vehicle technologies will become more common, including likely deployment of some fully automated vehicles. These new categories of vehicles will for the first time assume a major portion of new vehicle sales, while internal combustion engine vehicles with improved powertrain, design, and aerodynamics will continue to be an important part of new vehicle sales and fuel economy improvement.This study is a technical evaluation of the potential for internal combustion engine, hybrid, battery electric, fuel cell, nonpowertrain, and connected and automated vehicle technologies to contribute to efficiency in 2025-2035. In addition to making findings and recommendations related to technology cost and capabilities, Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy - 2025-2035 considers the impacts of changes in consumer behavior and regulatory regimes.

MARCHANT, GARY↗
DOE OSTI2020

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this work, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers' income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. Here, the results suggest that our method will reduce empty mileage per trip by 54-70% under optimal matching strategy, and a 25-32% reduction can also be achieved by following stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Thermal properties of U-Mo alloys irradiated under high fission power density

A variety of physical and thermal property measurements made on irradiated U-Mo alloy monolithic fuel samples with a Zr diffusion barrier and clad in Al alloy 6061 are reported. These properties form the basis for additional investigations in thermal behavior of U-Mo fuel under high performance research reactor irradiation conditions. Measurements were performed on samples harvested from fuel plates irradiated under high fission power density and surface heat flux and complement previous measurements on samples subjected to moderate fission power density and surface heat flux. Specific heat capacity of irradiated monolithic U-Mo increases with increasing temperature and fission density, but carries large uncertainties associated with thickness measurements used to determine the mass fraction of the layer constituents for extraction of the U-Mo specific heat capacity, specifically the very thin and nonuniform Zr diffusion barrier. Density of irradiated monolithic U-Mo decreases with increasing fission density resulting from fission gas swelling and porosity formation. Thermal diffusivity of irradiated monolithic U-Mo increased with increasing temperature and decreased with increasing fission density. Thermal conductivity of irradiated monolithic U-Mo increased with increasing temperature, and in general, appeared to decrease and become less sensitive to temperature with increasing fission density. Thermal conductivity measurements compared to a semi-empirical model agreed within ±30%. It is suspected that the main driver in deviation between model calculations and measurements is the fission gas swelling determination, which does not account for fission power density or irradiation temperature.

uranium (U), molybdenum, nuclear fuel, thermal con↗
DOE OSTI2020

Collaborative Research: Natural Organic Matter and Microbial Controls on Mobilization/Immobilization of I and Pu in Soils and Waters Affected by Radionuclide Releases in USA and Japan

In this project, the relationship between natural organic matter (NOM) and two radioactive elements that are relevant to nuclear waste disposal were studied: Plutonium (Pu) and Iodine. The human and environmental risks associated with Pu stem mainly from the very long half-lives of several of its isotopes ( 238 Pu, 88 yr; 239 Pu, 24,100 yr; 240 Pu, 6560 yr) and its radiotoxicity. Understanding Pu biogeochemical behavior in both near-field (>10-11M) and far-field scales (<10-11M) is imperative to the development of approaches for reprocessing Pu, remediation of Pu contamination and accurate assessment of risks posed by disposal practices for Pu-bearing wastes. The environmental mobility of Pu can be affected by redox potential, pH, adsorption, precipitation, complexation, colloid formation, and microbial activity, of which the first characteristic has the most profound influence. Numerous studies have shown high affinity of Pu towards NOM, as well as to mineral phases. NOM is ubiquitous in the environment, e.g., both fulvic and humic acids are able to reduce Pu(V,VI) to Pu(IV) and the redox potential of NOM is positively related to the abundance of phenolic/acidic OH groups. NOM can either facilitate or limit actinide migration, depending on specific biogeochemical conditions including pH, mineral and organic matter characteristics, etc. The other radionuclide of interest is radioiodine ( 129 I). 129I is a major by-product of nuclear fission and of serious concern to the Department of Energy (DOE) as it is among the top risk drivers at existing and potential radiowaste-contaminated sites. The risk of 129 I stems largely from its high bioconcentration factor (90% of the body’s iodine is accumulated in the thyroid), a high inventory at source terms, a very long-half life (16M years), and rapid mobility in the subsurface environment. As a consequence, 129 I has the lowest drinking water standard (1 pCi/L) among all radionuclides in the Federal Register. With a novel and sensitive gas chromatography-mass spectrometry (GC-MS) method developed in our lab, it is possible to quickly and simultaneously determine the distribution of 129 I and stable 127 I forms in environments, as low as 2 pCi/L for 129 I. This method was subsequently validated using accelerator mass spectroscopy, AMS. IO 3 - and organo-I were determined as major species in the groundwater of SRS and the Hanford Site, contrary to thermodynamic predictions that I- should be the dominant species at these sites. Mobility of 129 I was also demonstrated to depend greatly on the I species and its concentration, sediment pH, and redox state, with times to achieve equilibrium taking up to 12 weeks. Along the groundwater pathway in the F-Area of SRS, 129 I- supplied from the seepage basins was transformed to 129 IO 3 - and organo- 129 I with increasing iodine sediment sorption, causing the lower total 127 I and 129 I concentrations along the gradient transect of the waste plume. By contrast, groundwater 129 I concentrations in the wetlands (as high as 1617.3 pCi/L) were greatly elevated with respect to the source term (159.3 pCi/L). While the NOM promoted the uptake of 129 I to the wetland sediment, it also promoted the formation of soluble organic fraction. A small fraction of NOM that is bound to iodine can behave as a mobile organo-I source. Iodide was enzymatically incorporated into NOM, whereas both iodide and iodate were abiotically bound to NOM, under certain conditions. Iodate removal from the mobile aqueous phase can also occur through incorporation into carbonate (e.g., at the Hanford Site, USA). Thus immobilization and re-mobilization of iodine species were influenced by pH, Eh and the presence of NOM and metal oxides, which adds to the complexity of site remediation action. A ground-breaking result was to elucidate the products (i.e. organo-iodine moieties formed via enzymatic and non-enzymatic processes) at the molecular level by nuclear magnetic resonance (NMR) and electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICRMS). We found that iodine-NOM interactions may be influenced by NOM hydrophobic aliphatic moieties. From the perspective of ESI-FTICRMS, organo-iodine formulas were ascribed to the groups of unsaturated hydrocarbons, lignins and proteins. Iodate is likely abiotically reduced to reactive iodine species by lignin- and tannin-like compounds or carboxylic-rich alicyclic molecules (CRAM). We also investigated microbial mechanisms in iodine incorporation into NOM. We established that soil bacteria isolated from F-Area of SRS did not accumulate significant amounts of I- (0.2-2%). Intracellular uptake of I- decreases with increasing pH when pH ranged from 4 to 6. In contrast, 44 out of 84 strains isolated from the F-Area of SRS can transform I- to IO 3 - and organo-iodine. In some cases, oxidation was facilitated in the presence of H 2 O 2 . Microbes can also excrete organic acids that enhance I- oxidation by lowering the ambient pH and reacting with H 2 O 2 to form peroxy carboxylic acids. At lower pH values (≤5), H 2 O 2 hydrolysis was the driving force for iodide-oxidation; whereas, at pH ≥ 6, spontaneous decomposition of peroxy carboxylic acids, originating from H 2 O 2 and organic acids were the primary cause of iodide oxidation. Lastly, it was determined that microbial processes involved in Mn (II) are capable of directly oxidizing I- via enzymatic catalysis (i.e., multicopper oxidases), or indirectly through the formation of reactive oxygen species (ROS) and/or biogenic manganese oxides. ROS-mediated oxidation of I- was found to predominate at pH >5, whereas the enzymatic and Mn oxide pathways were more active at pH < 5. Together, this project has resulted in 9 publications in high-impact journals, and the training of 1 Ph.D and 4 undergraduate students.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Molecular interactions that drive morphological and mechanical stabilities in organic solar cells

Morphological and mechanical stabilities of organic solar cells (OSCs) are of paramount importance to ensure long-lived devices. However, the fundamental drivers of these stability metrics and their competing relationship have yet to be well defined. Here, in this work, several high-performance polymers and small molecule acceptors (SMAs) are considered to assist in the development of a comprehensive view of the molecular drivers of, and interrelationships between, morphological and mechanical stabilities. We find that the SMAs drive much of the embrittlement and diffusion characteristics in the blend films. However, the heterointeraction of the SMA and polymer, probed through dynamic mechanical analysis, is a key contributing factor to the film toughness. The heterointeraction energy is ideally maximally negative (i.e., repulsive), deviating from the geometric mean of the homointeraction energy. These findings assist in introducing a framework to understand the active layer stability and highlight material properties that lead to morphologically stable and physically robust OSCs.

14 SOLAR ENERGY↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗
DOE OSTI2024

Stomatal closure as a driver of minimum leaf conductance declines at high temperature and vapor pressure deficit in Quercus

Abstract Rising global temperatures and vapor pressure deficits (VPDs) are increasing plant water demand and becoming major drivers of large-scale plant mortality. Controlling transient leaf water loss after stomatal closure (minimum stomatal conductance [gmin]) is recognized as a key trait determining how long plants survive during soil drought. Yet, substantial uncertainty remains regarding how gmin responds to elevated temperatures and VPD and the underlying mechanisms. We measured gmin in 24 Quercus species from temperate and Mediterranean climates to determine whether gmin was sensitive to a coupled temperature and VPD increase. We also explored mechanistic links to phenology, climate, evolutionary history, and leaf anatomy. We found that gmin in all species exhibited a nonlinear negative temperature and VPD dependence. At 25 °C (VPD = 2.2 kPa), gmin varied from 1.19 to 8.09 mmol m−2 s−1 across species but converged to 0.57 ± 0.06 mmol m−2 s−1 at 45 °C (VPD = 6.6 kPa). In a subset of species, the effect of temperature and VPD on gmin was reversible and linked to the degree of stomatal closure, which was greater at 45 °C than at 25 °C. Our results show that gmin is dependent on temperature and VPD, is highly conserved in Quercus species, and is linked to leaf anatomy and stomatal behavior.

Zailaa, Joseph (ORCID:000000019103190X)↗