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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Towards efficient light emitters via computational design of molecules with inverted singlet-triplet gaps

To move toward rational design of efficient organic light emitting diodes based on the radical idea of inverted singlet-triplet gap (INVEST) systems, we propose a set of novel quantum chemical approaches, predictive but low-cost, to unveil a set of structural-property relationships. We perform a computational study of a series of substituted molecules based on a small set of known INVEST molecules. Our study demonstrates a high degree of correlation between the intramolecular charge transfer and the singlet-triplet energy gap and hints towards the use of a quantitative estimate of charge transfer to predict and modulate these energy gaps. We aim to create a database of INVEST molecules that includes accurate benchmarks of singlet-triplet energy gaps. Furthermore, we aim to link structural features and molecular properties, enabling a control knob for rational design.

42 ENGINEERING↗
DOE OSTI2023

Stretchable and Self‐Healable Semiconductive Composites Based on Hydrogen Bonding Cross‐linked Elastomeric Matrix

Abstract Semiconductors with both high stretchability and self‐healing capability are highly desirable for various wearable devices. Much progress has been achieved in designing highly stretchable semiconductive polymers or composites. The demonstration of self‐healable semiconductive composite is still rare. Here, an extremely soft, highly stretchable, and self‐healable hydrogen bonding cross‐linked elastomer, amide functionalized‐polyisobutylene (PIB‐amide) is developed, to enable a self‐healable semiconductive composite through compounding with a high‐performance conjugated diketopyrrolopyrrole (DPP‐T) polymer. The composite, consisting of 20% DPP‐T and 80% PIB‐amide, shows record high crack‐onset strain (COS ≈1500%), extremely low elastic modulus (E≈1.6 MPa), and unique ability to spontaneously self‐heal atroom temperature within 5 min. Unlike previous works, these unique composite materials also show strain‐independent charge mobility. An in‐depth morphological study based on multi‐model techniques indicate that all composites show blending ratio‐ and stretching‐independent fibril‐like aggregation due to the strong hydrogen bond in elastomer to enable the unique stable charge mobility. This study provides a new direction to develop highly healable and electronically stable semiconductive composite and will enable new applications of stretchable electronics.

Chemistry↗
DOE OSTI2020

Conceptual Design of a Conduction-Cooled Superconducting Undulator

In superconducting undulators (SCUs) that are currently operating at the Advanced Photon Source, the undulator magnets are indirectly cooled with liquid helium penetrating through channels in the magnet cores. The liquid helium is stored in a tank that is cooled by cryocoolers. However, a cryogen-free version of a superconducting undulator in which the undulator magnets are conduction-cooled is also possible, as has been realized in the SCUs developed at ANKA. We are currently working on a conceptual design of a conduction-cooled planar superconducting undulator. The measured load lines of cryocoolers and the measured thermal conductance of various thermal links are used in the thermal analysis. Also, various materials for the undulator magnet cores are being considered. This paper reports the thermal design of a conduction-cooled planar SCU.

cryocooler↗
DOE OSTI2020

Determining flow directions in river channel networks using planform morphology and topology

Abstract. The abundance of global, remotely sensed surface water observations has accelerated efforts toward characterizing and modeling how water moves across the Earth's surface through complex channel networks. In particular, deltas and braided river channel networks may contain thousands of links that route water, sediment, and nutrients across landscapes. In order to model flows through channel networks and characterize network structure, the direction of flow for each link within the network must be known. In this work, we propose a rapid, automatic, and objective method to identify flow directions for all links of a channel network using only remotely sensed imagery and knowledge of the network's inlet and outlet locations. We designed a suite of direction-predicting algorithms (DPAs), each of which exploits a particular morphologic characteristic of the channel network to provide a prediction of a link's flow direction. DPAs were chained together to create “recipes”, or algorithms that set all the flow directions of a channel network. Separate recipes were built for deltas and braided rivers and applied to seven delta and two braided river channel networks. Across all nine channel networks, the recipe-predicted flow directions agreed with expert judgement for 97 % of all tested links, and most disagreements were attributed to unusual channel network topologies that can easily be accounted for by pre-seeding critical links with known flow directions. Our results highlight the (non)universality of process–form relationships across deltas and braided rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Full core LOCA safety analysis for a PWR containing high burnup fuel

For economic reasons, the US nuclear industry is renewing efforts to build a technical basis to extend peak rod average burnup limits above the current regulatory burnup limit of 62 GWd/MTU. The primary driver of these efforts is to economically increase pressurized water reactor (PWR) cycle lengths to 24 months, reduce the number of fresh fuel assemblies, increase time online, thereby reducing number of outages and their associated cost, higher fuel utilization, and possibly reduce core design constraints. In order for US nuclear utilities to leverage these economic efficiencies, the US Nuclear Regulatory Commission (NRC) will likely require nuclear power plants (NPPs) to analyze a number of potential operational occurrences and their potential consequences with each new core design prior to resuming normal operation. Potential operational occurrences can be divided into three primary regimes: (1) normal operation, (2) anticipated operation occurrences (AOOs), and (3) design basis accidents (DBAs). Normal plant operation is an operating regime in which the plant operates within specified operational limits until the end of the cycle, whereas AOOs are events that result in the NPP deviating outside the normal operating regime. A key attribute of an AOO is that the occurrence should be expected. However, by definition, the occurrence of an AOO does not result in significant impact to critical safety functions. The last potential operational occurrence is a DBA. From the fuel performance point-of-view, DBAs can be subdivided into two bounding categories: (1) loss of coolant accidents (LOCAs), and (2) reactivity insertion accidents (RIAs). Unlike AOOs, DBAs may result in fuel rod failure. The NRC imposes fundamental acceptance criteria to minimize radiological consequences to the public and onsite staff. Furthermore, safety criteria are typically linked to the fulfillment of other acceptance criteria related to reactor safety equipment designed to mitigate DBAs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Effect of the Nature of Both Cation and Anion Substitution on the Structural Symmetry of Li‐Rich 3 d ‐Metal Chalcogenide Electrodes

Abstract Li‐rich layered chalcogenides have recently led to better understanding of the anionic redox process and its associated high capacity while providing ways to overcome its practical limitations of voltage fade and irreversibility. This study reports on the feasibility of triggering anionic activity in Li 2 TiS 3 , through anionic substitution (Se for S) or cationic substitution (Fe for Ti). Herein, the chalcogenide chemical space is further explored to prepare mono‐substituted Li 1.7 Ti 0.85 Mn 0.45 Ch 3 (Ch = S/Se) and doubly substituted cationic and anionic phases (Li 1.7 Ti 0.85 Fe 0.45 S 3‐z Se z ) which crystallize either in the O3‐ or O1‐type structures depending upon substituents. All series show a bell‐shape capacity variation as function of the transition metal (TM) substitution degree with values up to 240 mAh g −1 . For specific compositions, a structural O3 to O1 phase transition is observed upon Li removal, which is not reversible upon Li re‐insertion due to kinetic limitations and negatively affects long‐term cycling performance. Density functional theory (DFT) calculations confirm the O3/O1 relative stability along the different series and point subtle electronic differences in the TM‐doping, rationalizing the structural and electrochemical behaviors of these phases upon cycling. These findings provide further insights into the link between structural and electronic stability, which is of key importance for designing chalcogenide‐based anionic redox compounds.

Chemistry↗
DOE OSTI2024

Implicit neural representations for experimental steering of advanced experiments

Scattering measurements using electrons, neutrons, or photons are essential for obtaining microscopic insights into materials. However, limited facility availability and high-dimensional scattering data necessitate more efficient experimental steering techniques. Here, we report a machine learning method that guides scattering data collection and facilitates real-time estimation of model parameters, given a reliable forward model to simulate experimental signals. We employ implicit neural representations as efficient surrogates that link model parameters with simulated spectroscopies. This enables a Bayesian optimal experimental design framework to estimate the probability distributions of parameters from high-dimensional scattering data. We demonstrate the proposed method using inelastic neutron scattering with simulated and real experimental data, highlighting the method’s ability to provide real-time parameter estimation with quantified uncertainties and to deliver informed experimental guidance that reduces experimental time while maximizing scientific output. This approach paves the way for accelerated discoveries in condensed matter through scattering measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Surface Segregation of Liquid Metal Plasma-Facing Component Alloys: A ReaxFF Investigation

Engineering liquid metal alloys offers a transformative pathway for plasma-facing components by enabling chemically tailored surfaces that can simultaneously optimize plasma-material interactions, reduce divertor heat flux, and enhance core plasma confinement, thereby advancing the commercial viability of nuclear fusion power plants. This study, employing an atomistic simulation approach, provides direct evidence that incorporating nonmetal surface-active agents (such as O and H, or their combination) enables strong surface segregation. This capability makes tin−aluminum (Sn−Al) and tin−lithium (Sn−Li) alloys, with suitable compositions, good candidates for PFC applications. Specifically, the presence of low-Z solutes (Li, Al) leads to preferential surface enrichment, which imparts low-Z sputtering characteristics, while the Sn solvent maintains thermophysical stability. To systematically examine this behavior, we developed a ReaxFF force field spanning the full Sn/Al/Li/O/H chemistry, validated it against formation energies and elastic constants, and applied it in reactive molecular dynamics simulations at fusion-relevant temperatures. We also introduced an overlapbased segregation index that captures interfacial compositional separation directly from atomistic density distributions. Here, this metric reveals a clear hierarchy of segregation regimes and provides a unified view across all systems studied. Together, these findings establish a mechanistic link between nonmetal chemistry and interfacial structure, providing a predictive framework for designing self-adaptive, low-sputtering liquid metal alloys for fusion applications.

Alloys↗
DOE OSTI2025

Supramolecular Control of Ionic Retention in Electrolyte-Gated Synaptic Transistors

Electrolyte-gated transistors with ion-trapping layers offer a promising platform for artificial synapses in neuromorphic computing, yet molecular mechanisms governing ionic retention remain poorly understood. Here, in this study, we present a supramolecular approach to modulate ion retention by incorporating a crown ether derivative-based polymer network as an ion-trapping layer on top of a semiconducting monolayer. We show that the balance between ion–host binding and ion–solvent interactions dictates the kinetics of ion capture and release, which in turn controls the memory characteristics of the device. By varying the solvent dielectric constant, we tune the ionic retention time from nearly permanent trapping to rapid relaxation. Intermediate solvent polarity enables programmable short- and long-term synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, and long-term potentiation and depression. These findings establish a direct link between supramolecular ion recognition and synaptic plasticity and provide a generalizable design strategy for ionic–electronic neuromorphic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cation-doped ZnS catalysts for polysulfide conversion in lithium–sulfur batteries

Catalytic conversion of polysulfides is regarded as a crucial approach to enhancing kinetics and suppressing the shuttle effect in lithium-sulfur (Li-S) batteries. However, the activity prediction of Li-S catalysts remains elusive owing to the lack of mechanistic understanding of activity descriptors. Here, we report a volcano-shaped relationship between polysulfide adsorption ability and catalytic activity. In conjunction with theoretical analysis, we distinguish catalytic and anchoring effects to delineate the role of adsorption and emphasize the passivation of catalysts. These findings enable us to develop a composite catalyst, Co0.125Zn0.875S, which shows higher performance than simple binary compounds. Such a fundamental understanding of the intrinsic link between polysulfide adsorption and catalytic activity offers a rational viewpoint for designing Li-S catalysts and tuning their activities.

Shen, Zihan↗
DOE OSTI2021

Griffin-m

Griffin-m is a MOOSE-based reactor multiphysics application that streamlines the analysis of a variety of nuclear multiphysics simulations, including steady-state and transient radiation transport, core performance, fuel depletion, criticality and decay heat calculations, reprocessing and post-irradiation examination. This streamlining is accomplished via enhanced flexibility of the tools, uniform syntax in the MOOSE framework, dynamic linking of all relevant physics and a single point of execution. The design for flexible multi-physics, multi-radiation, multi-scheme tasks demands and ultimately makes Griffin-m a highly extendable code system. A software quality assurance (SQA) procedure is enforced during Griffin-m development. The Griffin-m version will be the main production version and it includes the MCC3 code, which was originally developed at Argonne National Laboratory to prepare cross sections for fast reactors.

Ortensi, Javier [Idaho National Laboratory (INL), ↗
DOE OSTI2021

Griffin: A Moose-based Reactor Multiphysics Application For Radiation Transport And Depletion Simulations

Griffin is a MOOSE-based reactor multiphysics application that streamlines the analysis of a variety of nuclear multiphysics applications, including steady-state and transient radiation transport, core performance, fuel depletion, criticality and decay heat calculations, reprocessing and post-irradiation examination. This streamlining is accomplished via enhanced flexibility of the tools, uniform syntax in the MOOSE framework, dynamic linking of all relevant physics and a single point of execution. The design for flexible multi-physics, multi-radiation, multi-scheme tasks demands and ultimately makes Griffin a highly extendable code system. A software quality assurance (SQA) procedure is enforced during Griffin development.

DeHart, MarkD.↗
DOE OSTI2022

Stress Drivers of Glucose Dynamics during Ozone Exposure Measured Using Radiotelemetry in Rats

Inhaled irritant air pollutants may trigger stress-related metabolic dysfunction associated with altered circulating adrenal-derived hormones. We used implantable telemetry in rats to assess real-time changes in circulating glucose during and after exposure to ozone and mechanistically linked responses to neuroendocrine stress hormones. First, using a cross-over design, we monitored glucose during ozone exposures (0.0, 0.2, 0.4, and 0.8 ppm) and nonexposure periods in male Wistar Kyoto rats implanted with glucose telemeters. A second cohort of unimplanted rats was exposed to ozone (0.0, 0.4 or 0.8 ppm) for 30 min, 1 h, 2 h, or 4 h with hormones measured immediately post exposure. We assessed glucose metabolism in sham and adrenalectomized rats, with or without supplementation of adrenergic/glucocorticoid receptor agonists, and in a separate cohort, antagonists. Ozone (0.8 ppm) was associated with significantly higher blood glucose and lower core body temperature beginning 90 min into exposure, with reversal of effects 4–6 h post exposure. Glucose monitoring during four daily 4-h ozone exposures revealed duration of glucose increases, adaptation, and diurnal variations. Ozone-induced glucose changes were preceded by higher levels of adrenocorticotropic hormone, corticosterone, and epinephrine but lower levels of thyroid-stimulating hormone, prolactin, and luteinizing hormones. Higher glucose and glucose intolerance were inhibited in rats that were adrenalectomized or treated with adrenergic plus glucocorticoid receptor antagonists but exacerbated by agonists. We demonstrated the temporality of neuroendocrine-stress–mediated biological sequalae responsible for ozone-induced glucose metabolic dysfunction and mechanism in a rodent model.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Novel Results Visualization for Dynamic PSA and New Modeling Features in EMRALD

The Event Modeling Risk Assessment Linked Diagram (EMRALD) tool, developed at the Idaho National Laboratory (INL), was designed to simplify the creation of dynamic models and support various research projects. One of the primary goals of EMRALD was to provide visual methods for modeling. EMRALD consists of two main components: a web-based user interface for model development and a solve engine for running model simulations. Over time, it has evolved to meet the diverse needs of its users. Initially, EMRALD's results were simple text outputs with final key state percentages and uncertainty bounds. However, because EMRALD utilizes a three-phase discrete event simulation and tracks the paths of each simulation run leading to a key state, there is significant potential to analyze large sets of path results data, including state paths, events, and timing. Visualizing this data meaningfully posed a challenge. To address this, a novel time-based Sankey diagram was developed. EMRALD exports results data in a format that can be opened in this Sankey viewer, allowing users to visualize paths, occurrences, events, and probability data for the entire simulation run in a single diagram. Moreover, when EMRALD was first created, there were limited tools capable of meeting its graphical requirements, many of which are no longer supported. In 2024, a new web-based interface was developed using modern graphing tools, enabling additional modeling features. This paper discusses the new dynamic PSA results visualization capability and the enhanced modeling tools available in EMRALD.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Optimizing spin qubit coherence through materials codesign

The evolution of defect-based spin qubit systems is currently transitioning from fundamental studies and proof-of-concept demonstrations into applications in the burgeoning field of quantum technology. Within this context, new challenges emerge, in particular, the need to understand and engineer the fundamental materials that form the hardware building blocks critical for the scalability and wide-scale adoption of such technologies. While earlier discussions have often focused on qubits within idealized systems, major limitations on spin coherence and optical properties arise from effects imposed by the nonideality of the surrounding host matrix. Decoherence can stem from a variety of sources, including other qubits, nuclear spins, and parasitic point- and extended defects, which interact with the qubit via magnetic and electric fields, photons, phonons, and strain. In this article, we focus on the relevant sources and mechanisms through which decoherence occurs and provide potential mitigation strategies via the synergistic integration of first-principles simulations and materials synthesis and engineering. We aim to provide a tangible link between material properties and material functions thereby enabling materials-by-design.

ab initio simulations↗
DOE OSTI2020

SIMCCS2.0

SimCCS2.0 is an open-source software package for industry, researchers, and government to design carbon dioxide (CO2) capture, transport, and storage (CCS) infrastructure that optimally links CO2 sources (e.g., power plants) with CO2 sinks (e.g., saline aquifers, depleted oil fields) to reduce industry carbon footprints and increase energy production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2020

URBANopt: An Open-Source Software Development Kit for Community and Urban District Energy Modeling: Preprint

Urban building modeling tools are developing rapidly; these tools use emerging simulation workflows for specific urban environmental design tasks, such as assessing the impacts of energy efficiency technologies at a district scale. However, with the emergence of new environmental design tasks, addressing all possible use cases and tasks is challenging and cannot be covered by a single tool. Urban-scale analysis at this level of complexity often requires linking multiple emerging tools, rather than using a single tool, to adequately evaluate a variety of possible fields in urban environmental design. To achieve this, flexible platforms are needed to support multiple input formats (e.g., geometric and non-geometric building properties), enabling the mapping of such inputs to underlying simulation engines. This paper provides an overview of the open-source URBANopt Software Development Kit (SDK) for modeling high-performance buildings and energy systems at a district scale. URBANopt's flexible SDK is composed of several modules that can be customized to integrate with other tools and generate new workflows to perform urban environmental design tasks, such as capturing interactions between individual buildings, district energy systems, distributed energy resources, and the electric distribution grid. We describe the functionality of the core SDK modules in URBANopt (called Core Gem, GeoJSON Gem, and Scenario Gem) and discuss the flexibility of these modules as a means of integration with a variety of tools. We also document and demonstrate technical details of writing and combining new modules to create customized workflows. Finally, we present a case study that uses the URBANopt SDK to model a hypothetical mixed-use urban project and simulate various scenarios to meet district energy performance goals.

buildings↗