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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Defense against phytopathogens relies on efficient antimicrobial protein secretion mediated by the microtubule-binding protein TGNap1

Plant immunity depends on the secretion of antimicrobial proteins, which occurs through yet-largely unknown mechanisms. The trans-Golgi network (TGN), a hub for intracellular and extracellular trafficking pathways, and the cytoskeleton, which is required for antimicrobial protein secretion, are emerging as pathogen targets to dampen plant immunity. In this work, we demonstrate that tgnap1-2, a loss-of-function mutant of Arabidopsis TGNap1, a TGN-associated and microtubule (MT)-binding protein, is susceptible to Pseudomonas syringae (Pst DC3000). Pst DC3000 infected tgnap1-2 is capable of mobilizing defense pathways, accumulating salicylic acid (SA), and expressing antimicrobial proteins. The susceptibility of tgnap1-2 is due to a failure to efficiently transport antimicrobial proteins to the apoplast in a partially MT-dependent pathway but independent from SA and is additive to the pathogen-antagonizing MIN7, a TGN-associated ARF-GEF protein. Therefore, our data demonstrate that plant immunity relies on TGNap1 for secretion of antimicrobial proteins, and that TGNap1 is a key immunity element that functionally links secretion and cytoskeleton in SA-independent pathogen responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Additive-Induced Morphology Change of Polymer Film Enables Enhanced Charge Mobility and Faster Organic Electrochemical Transistor Switching

Conjugated polymers (CPs) play an important role in organic electrochemical transistors (OECTs) for bioelectronics and related applications, where they serve as channel materials. Currently, most successful polymers for CPs are re-engineered from traditional CPs by replacing hydrophobic alkyl side chains with hydrophilic ethylene glycol or ionic groups. Frustratingly, the enhanced ion transport often compromises the charge mobility of the original CP. In this work, we present an additive-mediated method to construct a modified poly(3-hexylthiophene) (P3HT) film to enable efficient ion migration. The additive is designed with a cleavable diazo group that releases nitrogen and 2-methoxyethanol, a volatile compound, to alter the P3HT film morphology. OECTs based on the film exhibit improved response times. Interestingly, the process also enhances the crystallinity of P3HT, leading to higher hole mobility compared with pristine P3HT. This study proposes an in situ strategy to achieve the functionality of the OMIEC via morphological regulation, offering a promising route to simultaneously enhance both ion accessibility and charge mobility.

Organic polymers↗
DOE OSTI2021

Direct observation of charge separation in an organic light harvesting system by femtosecond time-resolved XPS

The ultrafast dynamics of photon-to-charge conversion in an organic light-harvesting system is studied by femtosecond time-resolved X-ray photoemission spectroscopy (TR-XPS) at the free-electron laser FLASH. This novel experimental technique provides site-specific information about charge separation and enables the monitoring of free charge carrier generation dynamics on their natural timescale, here applied to the model donor-acceptor system CuPc:C 60 . A previously unobserved channel for exciton dissociation into mobile charge carriers is identified, providing the first direct, real-time characterization of the timescale and efficiency of charge generation from low-energy charge-transfer states in an organic heterojunction. The findings give strong support to the emerging realization that charge separation even from energetically disfavored excitonic states is contributing significantly, indicating new options for light harvesting in organic heterojunctions.

14 SOLAR ENERGY↗
DOE OSTI2021

Structural insights into Lewis acid- and F4TCNQ-doped conjugated polymers by solid-state magnetic resonance spectroscopy

Molecular doping strategies facilitate orders of magnitude enhancement in the charge carrier mobility of organic semiconductors (OSCs). Understanding the different doping mechanisms and molecular-level constraints on doping efficiency related to the material energy levels is crucial to develop versatile dopants for OSCs. Given the compositional and structural heterogeneities associated with OSC thin films, insight into dopant–polymer interactions by long-range techniques such as X-ray scattering and electron microscopy is exceedingly challenging to obtain. This study employs short-range probes, solid-state (ss)NMR and EPR spectroscopy, to resolve local structures and intermolecular interactions between dopants such as F4TCNQ (2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane), Lewis acid BCF (tris[pentafluorophenyl] borane) and Lewis base conjugated polymer, PCPDTBT (P4) (poly[2,6-(4,4-bis(2-hexadecyl)-4H-cyclopenta[2,1-b;3,4-b']dithiophene)-alt-4,7(2,1,3-benzothiadiazole)]). Analysis of 1 H and 13 C ssNMR spectra of P4, P4 : F4TCNQ and P4 : BCF blends indicates that the addition of dopants induces local structural changes in the P4 polymer, and causes paramagnetism-induced signal broadening and intensity losses. The hyperfine interactions in P4 : BCF and P4 : F4TCNQ are characterized by two-dimensional pulsed EPR spectroscopy. For P4 : F4TCNQ, 19 F ssNMR analysis indicates that the F4TCNQ molecules are distributed and aggregated into different local chemical environments. By comparison, BCF molecules are intermixed with the P4 polymer and interact with traces of water molecules to form BCF–water complexes that serve as Brønsted acid sites, as revealed by 11 B ssNMR spectroscopy. These results indicate that the P4–dopant blends exhibit complex morphology with different distributions of dopants, whereby the combined use of ssNMR and EPR provides essential insights into how higher doping efficiency is observed with BCF and a mediocre efficiency is associated with F4TCNQ molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Dynamic Hierarchical Attention Framework for Multimodal Malware Detection

The increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller (DFC). Our methodology consistently classifies and processes modalities as either sequential or structural, facilitating content-adaptive weighting and resilient cross-modal representation learning. We advance the implementation of cutting-edge time series techniques, such as MiniRocket, for malware detection, hence creating new opportunities for temporal analysis in cybersecurity. Comprehensive experimental assessment shows that our framework performs exceptionally well, with 99.46% classification accuracy and 97.15% detection accuracy, significantly outperforming existing approaches through effective multimodal integration and hierarchical attention mechanisms.

Nazmin, Tamanna↗
DOE OSTI2024

Positron acceleration in plasma wakefields

Plasma acceleration has emerged as a promising technology for future particle accelerators, particularly linear colliders. Significant progress has been made in recent decades toward high-efficiency and high-quality acceleration of electrons in plasmas. However, this progress does not generalize to the acceleration of positrons, as plasmas are inherently charge asymmetric. Here, we present a comprehensive review of historical and current efforts to accelerate positrons using plasma wakefields. Proposed schemes that aim to increase energy efficiency and beam quality are summarized and quantitatively compared. A dimensionless metric that scales with the luminosity-per-beam power is introduced, indicating that positron-acceleration schemes are currently below the ultimate requirement for colliders. The primary issue is electron motion; the high mobility of plasma electrons compared to plasma ions, which leads to nonuniform accelerating and focusing fields that degrade the beam quality of the positron bunch, particularly for high efficiency acceleration. Finally, we discuss possible mitigation strategies and directions for future research.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Detection efficiency of adult Pacific lamprey passage counts at Leaburg Dam and upstream distribution in the McKenzie River (Oregon, USA)

Abstract Adult Pacific lamprey ( Entosphenus tridentatus ) were counted using consistent methodology since 2005 with a video monitoring system as they passed Leaburg Dam (McKenzie River, Oregon, USA) en route to upstream spawning areas. In this study we evaluated the detection efficiency of the video system and upstream distribution of Pacific lamprey using the video counts of lamprey passage (herein, “dam counts”), passive integrated transponder (PIT) tags, and radio telemetry. In 2019–2020 we collected, tagged and tracked 32 adult lamprey (4 from the McKenzie River and 28 that were translocated from Willamette Falls). All fish were tagged and released into the tailrace of Leaburg Dam in June 2019. Oregon Department of Fish and Wildlife employees and volunteers from the local community conducted mobile radiotracking above and below the dam in drift boats (114 mainstem river kilometers) and on foot (several tributaries). The estimated detection efficiency for dam counts was 92% (95% confidence interval: 67%–99%). Fifty percent (16 of 32) of the tagged lamprey passed the dam, including 13 of 28 that were translocated. Thirty‐seven percent (6 of 16) of lamprey that passed Leaburg Dam were detected in a restored reach of the South Fork McKenzie, 32.6 river kilometers upstream of the dam.

Romer, Jeremy D.↗
DOE OSTI2026

Heat Transfer Fluids as Co‐Diluents in Localized High‐Concentration Electrolytes for High‐Rate Lithium Metal Batteries With Enhanced Safety

Localized high-concentration electrolytes (LHCEs) have been identified as promising electrolyte formulations for lithium metal batteries, due to their effective interphase formation and promotion of compact Li deposition, yet their practical implementation is often limited by reduced ion transport kinetics. In this study, two industrially established fluorinated ethers are identified for the first time in battery research as effective co-diluents as they combine a broad electrochemical stability window with a low viscosity and intrinsic non-flammability. Incorporating these components, commonly used as heat transfer fluids, yields safer, less flammable electrolyte formulations with enhanced ion mobilities. In particular, the ternary co-diluent formulation shows improved ion mobility by reducing the electrolyte's viscosity while limiting excessive ion clustering. Based on the improved electrolyte transport kinetics, lower overvoltages and higher Coulombic efficiencies at current densities ≥ 1 mA cm −2 are achieved with the ternary co-diluent blend, resulting in markedly extended cycle life in an application-oriented zero-excess pouch cell compared with the baseline system. Complementary electrochemical and ex situ analysis of harvested electrodes at moderate current densities reveals no discernible differences in interphase morphology and composition, suggesting enhanced ion mobility as the primary cause of the improved high-rate performance.

electrolyte diluent↗
DOE OSTI2022

Benzotriazole‐Based Nonfused Ring Acceptors for Efficient and Thermally Stable Organic Solar Cells

Abstract Nonfused ring acceptors (NFRAs) have attracted significant attention for nonfullerene organic solar cells (OSCs) owing to their chemical tunability and facile synthesis. In this study, a benzotriazole‐based NFRA with chlorinated end groups (Triazole‐4Cl) is developed to realize highly efficient and thermally stable NFRA‐based OSCs; an analogous NFRA with nonchlorinated end groups (Triazole‐H) is synthesized for comparison. Triazole‐4Cl film exhibits the high‐order packing structure and the near‐infrared absorption capability, which are advantageous in charge transport and light harvesting of the resulting OSCs. In particular, the strong crystalline behavior of Triazole‐4Cl results in enhanced self‐aggregation, leading to high charge carrier mobility. Owing to these properties, a PBDB‐T (polymer donor):Triazole‐4Cl OSC demonstrates a high short‐circuit current, fill factor, and power conversion efficiency (PCE = 10.46%), outperforming a PBDB‐T:Triazole‐H OSC (PCE = 7.65%). In addition, the thermal stability of a PBDB‐T:Triazole‐4Cl OSC at an elevated temperature of 120 °C exceeds that of a PBDB‐T:Triazole‐H OSC. This is mainly attributed to the significantly higher cold crystallization temperature of Triazole‐4Cl (205.9 °C). This work provides useful guidelines for the design of NFRAs to achieve efficient and thermally stable NFRA‐based OSCs.

Polymer Science↗
DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Electron‐Deficient and Quinoid Central Unit Engineering for Unfused Ring‐Based A 1 –D–A 2 –D–A 1 ‐Type Acceptor Enables High Performance Nonfullerene Polymer Solar Cells with High V oc and PCE Simultaneously

Abstract Here, a pair of A 1 –D–A 2 –D–A 1 unfused ring core‐based nonfullerene small molecule acceptors (NF‐SMAs), BO2FIDT‐4Cl and BT2FIDT‐4Cl is synthesized, which possess the same terminals (A 1 ) and indacenodithiophene unit (D), coupling with different fluorinated electron‐deficient central unit (difluorobenzoxadiazole or difluorobenzothiadiazole) (A 2 ). BT2FIDT‐4Cl exhibits a slightly smaller optical bandgap of 1.56 eV, upshifted highest occupied molecular orbital energy levels, much higher electron mobility, and slightly enhanced molecular packing order in neat thin films than that of BO2FIDT‐4Cl . The polymer solar cells (PSCs) based on BT2FIDT‐4Cl:PM7 yield the best power conversion efficiency (PCE) of 12.5% with a V oc of 0.97 V, which is higher than that of BO2FIDT‐4Cl ‐based devices (PCE of 10.4%). The results demonstrate that the subtle modification of A 2 unit would result in lower trap‐assisted recombination, more favorable morphology features, and more balanced electron and hole mobility in the PM7:BT2FIDT‐4Cl blend films. It is worth mentioning that the PCE of 12.5% is the highest value in nonfused ring NF‐SMA‐based binary PSCs with high V oc over 0.90 V. These results suggest that appropriate modulation of the quinoid electron‐deficient central unit is an effective approach to construct highly efficient unfused ring NF‐SMAs to boost PCE and V oc simultaneously.

Zhang, Chao↗
DOE OSTI2021

Correlating conductivity and Seebeck coefficient to doping within crystalline and amorphous domains in poly(3-(methoxyethoxyethoxy)thiophene)

Molecular doping of conjugated polymers (CPs) plays a vital role in optimizing organic electronic and energy applications. For the case of organic thermoelectrics, it is commonly believed that doping CPs with a strong dopant could result in higher conductivity (σ) and thus better power factor (PF). Herein, by investigating thermoelectric performance of a polar side-chain bearing CP, poly(3- (methoxyethoxyethoxy)thiophene) (P3MEET), vapor doped with fluorinatedderivative of tetracyanoquinodimethane FnTCNQ (n = 1, 2, 4), we show that using strong dopants can in fact have detrimental effects on the thermoelectric performance of CPs. Despite possessing higher electron affinity, doping P3MEET with F4TCNQ only results in a σ (27.0 S/cm) comparable to samples doped with other two weaker dopants F2TCNQ and F1TCNQ (26.4 and 20.1 S/cm). Interestingly, F4TCNQ-doped samples display a marked reduction in the Seebeck coefficient (α) compared to F1TCNQ- and F2TCNQ-doped samples from 42 to 13 μV/ K, leading to an undesirable suppression of the PF. Additionally, structural characterizations coupled with Kang-Snyder modeling of the α–σ relation show that the reduction of α in F4TCNQ-doped P3MEET samples originates from the generation of low mobility carrier within P3MEET's amorphous domain. Our results demonstrate that factors such as dopant distribution and doping efficiency within the crystalline and amorphous domains of CPs should play a crucial role in advancing rational design for organic thermoelectrics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High Throughput Computational Framework of Materials Properties for Extreme Environments

This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).

, Ni-based superalloys↗
DOE OSTI2020

Estimating vehicle fuel economy from overhead camera imagery and application for traffic control

In this work, we explore the ability to estimate vehicle fuel consumption using imagery from overhead fisheye lens cameras deployed as traffic sensors. We utilize this information to simulate vision-based control of a traffic intersection, with a goal of improving fuel economy with minimal impact to mobility. We introduce the ORNL Overhead Vehicle Dataset (OOVD), consisting of a data set of paired, labeled vehicle images from a ground-based camera and an overhead fisheye lens traffic camera. The data set includes segmentation masks based on Gaussian mixture models for vehicle detection. We show the dataset utility through three applications: the estimate of fuel consumption based on segmentation bounding boxes, vehicle discrimination for those vehicles with largest bounding boxes, and a fine-grained classification on a limited number of vehicle makes and models using a pre-trained set of convolutional neural network models. We compare these results with estimates based on a large open-source data set based on web-scraped imagery. Finally, we show the utility of the approach using reinforcement learning in a traffic simulator using the open source Simulation of Urban Mobility (SUMO) package. Our results show the feasibility of the approach for controlling traffic lights for better fuel efficiency based solely on visual vehicle estimates from commercial, fisheye lens cameras.

Karnowski, Thomas↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement: Preprint

Distribution system resilience enhancement is an important topic to ensure customers have access to the power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecasts. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗