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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Self‐Assembly Methods Induce Different Individual‐Polymer‐Block Solvation Responses

An understanding of block-specific responses to stimuli in self-assembled copolymers is essential for the effective design of responsive materials. Here, materials formed from different post-reaction processing methods of the same ROMP block copolymer exhibit different block-specific solvent responses, as characterized in situ by fluorescence lifetime imaging microscopy (FLIM). FLIM data were acquired by tagging the polar or nonpolar block separately with a covalently incorporated viscosity-sensitive fluorescent molecular rotor to measure the changing tightness or looseness of assembly upon changes in solvent composition. Rapid precipitation from an insoluble solvent to form a film results in tighter assembly and less difference in tightness/looseness between the two blocks. Further, it interrupts individual block-solvent response behavior. Both results indicate an apparent disruption of the core–shell assembly. This model from FLIM is further supported by differential scanning calorimetry (DSC) and small-angle X-ray scattering (SAXS) data from isolated solids, which show tighter long-range interactions and more long-range order in these kinetically trapped states. Here, the experiments develop FLIM as a method for pinpointing stimuli-responsive behaviors from different processing methods to individual blocks. Together these outcomes provide a future handle for tailoring solvent-triggered assembly/disassembly behavior.

López, Pía A. [University of California, Irvine, C↗
DOE OSTI2024

ABC -Auxetics: An Implicit Design Approach for Negative Poisson's Ratio Materials

A novel methodology is introduced for designing auxetic (negative Poisson's ratio) structures based on topological principles and is demonstrated by investigating a new class of auxetics based on two-dimensional (2D) textile weave patterns. Conventional methodology for designing auxetic materials typically involves determining a single deformable block (a unit cell) of material whose shape results in auxetic behavior. Consequently, patterning such a unit cell in a 2D (or 3D) domain results in a larger structure that exhibits overall auxetic behavior. Such an approach naturally relies on some prior intuition and experience regarding which unit cells may be auxetic. Second, tuning the properties of the resulting structures is typically limited to parametric variations of the geometry of a specific type of unit cell. Thus, most of the currently known auxetic structures belong to a selected few classes of unit cell geometries that are explicitly defined in accordance with a specified topological (i.e., grid structure). Herein, a new class of auxetic structures is demonstrated that, while periodic, can be generated implicitly, i.e., without reference to a specific unit cell design. The approach leverages weave-based parameters (A–B–C), resulting in a rich design space for auxetics that is previously unexplored.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Design tradeoffs between traditional hexagonal and emerging cubic In X Ga (1–X) N/GaN-based green light-emitting diodes

Here, we report on the design tradeoffs between traditional hexagonal and emerging cubic In X Ga (1–X) N/GaN -based green (520 nm≤ λ ≤550 nm) light-emitting diodes with special emphasis on the electron blocking layer, number of quantum wells, and thicknesses of quantum wells and barriers. We identified three crucial design rules for cubic green light-emitting diodes: (1) no need for an electron blocking layer; (2) use of a wide quantum well; and (3) choice of thin quantum barriers in multi-quantum well light-emitting diode designs. These design rules increase the internal quantum efficiency of cubic green light-emitting diodes by ~30.5% under 100A/cm 2 injection with respect to traditional designs. Overall, the design rules of cubic light-emitting diodes and their differentiating nature from the traditional, hexagonal ones are crucial for the advent of next-generation cubic light-emitting diodes.

42 ENGINEERING↗
DOE OSTI2021

Conjugation- and Aggregation-Directed Design of Covalent Organic Frameworks as White-Light-Emitting Diodes

2D covalent organic frameworks (COFs) have emerged as a promising class of organic luminescent materials due to their structural diversity, which allows the systematic tuning of organic building blocks to optimize emitting properties. However, a significant knowledge gap exists between the design strategy and the fundamental understanding of the key structural parameters that determine their photophysical properties. Here, in this work, we report two highly emissive sp 2 -C-COFs and the direct correlation of the structure (conjugation and aggregation) with their light absorption/emission, charge transfer (CT), and exciton dynamics, the key properties that determine their function as luminescent materials. We show that white light can be obtained by simply coating COFs on an LED strip or mixing the two COFs. Using the combination of time-resolved absorption and emission spectroscopy as well as computational prediction, we show that the planarity, conjugation, orientation of the dipole moment, and interlayer aggregation not only determine the light-harvesting ability of COFs but also control the exciton relaxation pathway and photoluminescent quantum yield.

14 SOLAR ENERGY↗
DOE OSTI2022

Optics-Free Chip-Scale Intraoperative Imaging Using NIR-Excited Upconverting Nanoparticles

Here we present an optics-free CMOS image sensor that incorporates a novel time-gated dual-photodiode pixel design to allow filter- and lens-less image acquisition of near-infrared-excited (NIR-excited) upconverting nanoparticles. Recent biomedical advances have highlighted the benefits of NIR excitation, but NIR interaction with silicon has remained a challenge, even with high-performance optical blocking filters. Using a secondary diode and a dual-photodiode design, this sensor is able to remove the 100s of mV of NIR background on pixels and bring it down to single-digit mV level, nearing its noise floor of 2.2 mV rms, not achievable with any optical filter. Non-linear effects of background cancellation using the diode pair has been mitigated using an initial one-time pixel-level curve fitting and calibration in a post-processing setting. This imager comprises a highly linear 11 fF metal-oxide-metal (MOM) capacitor and includes integrated angle-selective gratings to reject oblique light and enhance sharpness. Each pixel also includes two distinct correlated double sampling schemes, to remove low frequency flicker noise and systematic offset in the datapath. We demonstrate the performance of this imager using pulsed NIR-excited upconverting nanoparticles on standard United-States-Air-Force (USAF) resolution targets and achieve an SNR of 15 dB, while keeping NIR background below 6 mV. This 36-by-80-pixel array measures only 2.3 mm by 4.8 mm and can be thinned down to 25 µm, allowing it to become surgically compatible with intraoperative instruments and equipment, while remaining optics-free.

42 ENGINEERING↗
DOE OSTI2024

Ferroelectric FET-based context-switching FPGA enabling dynamic reconfiguration for adaptive deep learning machines

Field programmable gate array (FPGA) is widely used in the acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the trade-off between chip area and reconfiguration latency, making efficient FPGA accelerations that require switching between multiple configurations still elusive. Here, we propose a ferroelectric field-effect transistor (FeFET)–based context-switching FPGA supporting dynamic reconfiguration to break this trade-off, enabling loading of arbitrary configuration without interrupting the active configuration execution. Leveraging the intrinsic structure and nonvolatility of FeFETs, compact FPGA primitives are proposed and experimentally verified. The evaluation results show our design shows a 63.0%/74.7% reduction in a look-up table (LUT)/connection block (CB) area and 82.7%/53.6% reduction in CB/switch box power consumption with a minimal penalty in the critical path delay (9.6%). Besides, our design yields significant time savings by 78.7 and 20.3% on average for context-switching and dynamic reconfiguration applications, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Block smoothers and generalized ideal interpolation in AMG (Final Report)

The Pennsylvania State University (“Subcontractor”) worked on developing new parallel algebraic multilevel methods suitable for solving PDEs. Specifically, work on the design of multigrid solvers for coupled systems of partial differential equations arising in numerical modeling of various applications was completed. A main emphasis was on the design of new ideal algebraic multigrid interpolation for problems such as Maxwell’s equations where block smoothers are needed and the standard form of ideal interpolation is not an effective choice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Tailored Fiber Placement for Complex Preforms

Tailored Fiber Placement (TFP) offers a novel approach to optimize fiber architecture for the fabrication of complex, structural parts not traditionally suitable for advanced composites. This technology not only offers new routes for weight reduction via metal substitution, it also offers cost reduction through minimization of material scrap and reduced labor. This reduction in component weight leads to increased fuel efficiency, and reduced production energy consumption, thereby, helping to achieve the stated IACMI technical goals. This technology leverages centuries of manufacturing development in support of the textile and embroidery industry. One major drawback to this technology is the lack of commercial or non- proprietary structural performance data and robust analytical tools used to optimize fiber architecture and predict performance. This project was structured to utilize common sub-element features to validate analytical performance tools, generate performance data, and gather cost and performance data on components of interest. This project was designed to give industry sponsors the confidence and ability to take full advantage of TFP to fabricate primary, highly loaded structure and integrate features such as metallic fasteners. The project focused principally on the use of high strength carbon fiber, such as T700, and the use of aerospace epoxy resin matrix to primarily support development of new composite applications in vehicle, aerospace, and industrial markets. This project applied previously developed analytical tools to predict the performance of TFP produced parts. This work focused on developing the pipeline to characterize material in order to accurately predict component performance when modifying the TFP print paths and stitch density. This focused on experimental characterization via standardized ASTM testing, alongside experimental testing of more representative service components by testing curved beam strength, beam shear performance, a large scale TFP lug, and ultimately designing a fully TFP clip bracket that reduced weight and cost compared to a traditional metallic component. The new knowledge gained from this program included: 1) development and demonstration of novel analytical tools applied to analysis of TFP preforms; 2) development and demonstration of a building block approach using coupons and sub-elements to optimize the design of a more complex component; 3) demonstration that optimized fiber orientation using TFP can exceed performance of conventional textile composite materials and can open new applications currently limited to metallic components; 4) Demonstration of performance and cost benefits of the TFP process as compared to metallic and conventional textile composites. Recommendations for follow-on work include development of design allowables to assess the impact of high temperature/moisture exposure or saturation during loading, tracking the impact of stitching needle wear on the performance of parts and ability to stitch thicker preforms, using TFP preforms as local reinforcement at areas of bearing or complex loading, and topology optimization of components by tow steering. The expertise developed during the course of this project can be leveraged to provide commercial engineering design and fabrication services using TFP. UDRI is in the process of formalizing their partnership with Spintech, who will serve as the commercialization partner for this technology and provide molding services and deliver finished components to the end user. UDRI will continue to produce the preforms until the economics allow Spintech to procure its own TFP equipment or lease UDRI equipment, at which point UDRI will step away from manufacture and serve as the engineering and design lead on product development.

36 MATERIALS SCIENCE↗
DOE OSTI2020

PARAMETRIC EVALUATION OF ALTNERNATIVE NUCLEAR PROPULSION CORES USING CURVED FUEL PLATES

Nuclear Thermal Propulsion (NTP) holds the potential of reducing travel times for deep space missions (e.g. to Mars). Previous reactor core designs considered by the Rover/NERVA program relied on highly enriched uranium (HEU) fuel contained within a hexagonal graphite matrix. An alternative layout is investigated in this paper. It consists of a circular assembly containing concentric curved plates of UN fuel. These fuel assemblies are placed within a beryllium block and reflector. Preliminary results indicate that many variations of this design are viable, with high power to mass ratio and outlet temperatures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Efficient, Thermally Stable, and Mechanically Robust All-Polymer Solar Cells Consisting of the Same Benzodithiophene Unit-Based Polymer Acceptor and Donor with High Molecular Compatibility

All-polymer solar cells (all-PSCs) are a highly attractive class of photovoltaics for wearable and portable electronics due to their excellent morphological and mechanical stabilities. Recently, new types of polymer acceptors (P A s) consisting of non-fullerene small molecule acceptors (NFSMAs) with strong light absorption have been proposed to enhance the power conversion efficiency (PCE) of all-PSCs. However, polymerization of NFSMAs often reduces entropy of mixing in PSC blends and prevents the formation of intermixed blend domains required for efficient charge generation and morphological stability. One approach to increase compatibility in these systems is to design P A s that contain the same building blocks as their polymer donor (P D ) counterparts. Here, a series of NFSMA-based P A s [P(BDT2BOY5-X), (X = H, F, Cl)] are reported, by copolymerizing NFSMA (Y5-2BO) with benzodithiophene (BDT), a common donating unit in high-performance P D s such as PBDB-T. All-PSC blends composed of PBDB-T PD and P(BDT2BOY5-X) P A show enhanced molecular compatibility, resulting in excellent morphological and electronic properties. Specifically, PBDB-T:P(BDT2BOY5-Cl) all-PSC has a PCE of 11.12%, which is significantly higher than previous PBDB-T:Y5-2BO (7.02%) and PBDB-T:P(NDI2OD-T2) (6.00%) PSCs. Additionally, the increased compatibility of these all-PSCs greatly improves their thermal stability and mechanical robustness. For example, the crack onset strain (COS) and toughness of the PBDB-T:P(BDT2BOY5-Cl) blend are 15.9% and 3.24 MJ m -3 , respectively, in comparison to the PBDB-T:Y5-2BO blends at 2.21% and 0.32 MJ m -3 .

14 SOLAR ENERGY↗
DOE OSTI2023

Precise Control of Dissipative Self-assembly by Light and Electricity

Nature-inspired synthetic dissipative self-assemblies have attracted much attention recently. However, it remains a major challenge to achieve precise control over dissipative supramolecular assembly structures and functions of self-contained systems. Here we combine light and electricity as two clean, and spatiotemporally addressable fuels to provide precise control over the morphology for dissipative self-assembly of a perylene bisimide glycine (PBIg) building block in a self-contained solution. In this design, electrochemical oxidation provides the positive fuel to activate PBIg self-assembly while photoreduction supplies the negative fuel to deactivate the system for disassembly. Further, through programming the two counteracting fuels, we demonstrated the control of PBIg self-assembly into a variety of assembly morphologies in a self-contained system. In addition, by exerting light and electrical dual fuels simultaneously, we could create an active homeostasis exhibiting dynamic instability, leading to morphological change to asymmetric assemblies with curvatures. Such precise control over self-assembly of self-contained systems may find future applications in programming complex active materials as well as formulating pharmaceutical reagents with desired morphologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Scalable preconditioning for the stabilized contact mechanics problem

We present a family of preconditioning strategies for the contact problem in fractured and faulted porous media. We combine low-order continuous finite elements to simulate the bulk deformation with piecewise constant Lagrange multipliers to impose the frictional contact constraints. This formulation is not uniformly inf-sup stable and requires stabilization. We improve previous work by Franceschini et al. (2020) by introducing a novel jump stabilization technique that requires only local geometrical and mechanical properties. We then design scalable preconditioning strategies that take advantage of the block structure of the Jacobian matrix using a physics-based partitioning of the unknowns by field type, namely displacement and Lagrange multipliers. The key to the success of the proposed preconditioners is a pseudo-Schur complement obtained by eliminating the Lagrange multiplier degrees of freedom, which can then be efficiently solved using an optimal multigrid method. Numerical results, including complex real-world problems, are presented to illustrate theoretical properties, scalability and robustness of the preconditioner. A comparison with other approaches available in the literature is also provided.

58 GEOSCIENCES↗
DOE OSTI2023

Revealing protonation states and tracking substrate in serine hydroxymethyltransferase with room-temperature X-ray and neutron crystallography

Pyridoxal 5’-phosphate (PLP)-dependent enzymes utilize a vitamin B 6 -derived cofactor to perform a myriad of chemical transformations on amino acids and other small molecules. Some PLP-dependent enzymes, such as serine hydroxymethyltransferase (SHMT), are promising drug targets for the design of small-molecule antimicrobials and anticancer therapeutics, while others have been used to synthesize pharmaceutical building blocks. Understanding PLP-dependent catalysis and the reaction specificity is crucial to advance structure-assisted drug design and enzyme engineering. Here we report the direct determination of the protonation states in the active site of Thermus thermophilus SHMT (TthSHMT) in the internal aldimine state using room-temperature joint X-ray/neutron crystallography. Conserved active site architecture of the model enzyme TthSHMT and of human mitochondrial SHMT (hSHMT2) were compared by obtaining a room-temperature X-ray structure of hSHMT2, suggesting identical protonation states in the human enzyme. The amino acid substrate serine pathway through the TthSHMT active site cavity was tracked, revealing the peripheral and cationic binding sites that correspond to the pre-Michaelis and pseudo-Michaelis complexes, respectively. At the peripheral binding site, the substrate is bound in the zwitterionic form. By analyzing the observed protonation states, Glu53, but not His residues, is proposed as the general base catalyst, orchestrating the retro-aldol transformation of L-serine into glycine.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

CLMM : a LSST-DESC cluster weak lensing mass modeling library for cosmology

ABSTRACT We present the v1.0 release of CLMM, an open source python library for the estimation of the weak lensing masses of clusters of galaxies. CLMM is designed as a stand-alone toolkit of building blocks to enable end-to-end analysis pipeline validation for upcoming cluster cosmology analyses such as the ones that will be performed by the Vera C. Rubin Legacy Survey of Space and Time-Dark Energy Science Collaboration (LSST-DESC). Its purpose is to serve as a flexible, easy-to-install, and easy-to-use interface for both weak lensing simulators and observers and can be applied to real and mock data to study the systematics affecting weak lensing mass reconstruction. At the core of CLMM are routines to model the weak lensing shear signal given the underlying mass distribution of galaxy clusters and a set of data operations to prepare the corresponding data vectors. The theoretical predictions rely on existing software, used as backends in the code, that have been thoroughly tested and cross-checked. Combined theoretical predictions and data can be used to constrain the mass distribution of galaxy clusters as demonstrated in a suite of example Jupyter Notebooks shipped with the software and also available in the extensive online documentation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Electron-Ion Collider - A machine that will unlock the secrets of the strongest force in nature!

The computers and smartphones we use every day depend on what we learned about the atom in the last century. All information technology – and much of our economy today – relies on understanding the electromagnetic force between the atomic nucleus and the electrons that orbit it. The science of that force is well understood, but we still know little about the microcosm within the protons and neutrons that make up the atomic nucleus. That’s where Brookhaven National Laboratory (BNL) comes in. Brookhaven National Laboratory (located in Suffolk County, NY, about 60 miles east of midtown Manhattan) was recently chosen as the building site for an Electron-Ion Collider (EIC), a one-of-a-kind nuclear physics research facility. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design will take advantage of the existing and highly optimized Relativistic Heavy Ion Collider (RHIC) that’s been operating at Brookhaven Lab since 2000. Beyond sparking scientific discoveries in a new frontier of fundamental physics, the Electron-Ion Collider will trigger technological breakthroughs that have broad-ranging impact on human health and national challenges.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

High-Performance Coal-Based Commercial Facade Panels and Architectural Components (Phase I Final Report)

Semplastics has developed, tested, and documented the viability for commercialization of a new class of composite architectural panel materials that use coal as the primary constituent. This project produced sample panels using these novel materials that comprise 55% coal by mass (71% carbon by mass) and are comparable in dimensions to commercially available materials, while displaying superior mechanical strength, significant weight savings, and better insulating ability at a competitive cost. Phase I moved this coal-based composite materials technology from a Technology Readiness Level (TRL) of 3 to TRL 5. Semplastics’ new coal-based composite materials display an array of high-performance characteristics, including light weight, mechanical durability, temperature stability, and water resistance. These new materials require less energy to produce than comparable commercial products and could be manufactured on existing conventional plastic resin processing equipment in commercial quantities. The coal particles are completely encapsulated in ceramic from a polymer-derived ceramic (PDC) precursor, then bonded together by another inorganic resin. The material can be molded and cured to produce fireproof components such as ceiling panels, facades, and extruded underlayment, blocking, and backer boards, as well as other architectural design components such as moldings. The project included the production of sample panels for proof of concept, testing of the panels to show their improved characteristics, and development of a scale-up strategy to demonstrate the viability of the coal-based panels as a commercial alternative to existing building materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reinforcement Learning for Load-balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.

Distributed and parallel particle tracing↗