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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 739 records · Page 41

Process-Structure-Property Relationships for 316L Stainless Steel Fabricated by Additive Manufacturing and Its Implication for Component Engineering

We investigate the process-structure-property relationships for 316L stainless steel prototyping utilizing 3-D laser engineered net shaping (LENS), a commercial direct energy deposition additive manufacturing process. Our study concluded that the resultant physical metallurgy of 3-D LENS 316L prototypes is dictated by the interactive metallurgical reactions, during instantaneous powder feeding/melting, molten metal flow and liquid metal solidification. This study also showed 3-D LENS manufacturing is capable of building high strength and ductile 316L prototypes due to its fine cellular spacing from fast solidification cooling, and the well-fused epitaxial interfaces at metal flow trails and interpass boundaries. However, without further LENS process control and optimization, the deposits are vulnerable to localized hardness variation attributed to heterogeneous microstructure, i.e., the interpass heat-affected zone (HAZ) from repetitive thermal heating during successive layer depositions. Most significantly, the current deposits exhibit anisotropic tensile behavior, i.e., lower strain and/or premature interpass delamination parallel to build direction (axial). This anisotropic behavior is attributed to the presence of interpass HAZ, which coexists with flying feedstock inclusions and porosity from incomplete molten metal fusion. Our current observations and findings contribute to the scientific basis for future process control and optimization necessary for material property control and defect mitigation.

36 MATERIALS SCIENCE↗

Synthetic Accessibility and Sodium Ion Conductivity of the Na8–x A x P2O9 (NAP) High-Temperature Sodium Superionic Conductor Framework

Advancement of solid-state electrolytes (SSEs) for all solid-state batteries typically focuses on modification of a known structural framework to improve conductivity, e.g., cation substitution for an immobile ion or varying the concentration of the mobile ions. Novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, thereby unlocking optimal conductors with different properties. Herein, we perform a high-throughput survey of a structural framework for sodium ion conduction, Na8–x A x P2O9 (NAP), to understand the family’s thermodynamic stability, synthesizability, and ionic conduction. We show that the parent phase Na4TiP2O9 (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons arising from pseudo-Jahn–Teller mode in the 1D titanium chains. Screening compounds in which Ti is substituted by other metals computationally reveal a number of candidates that are predicted to be low in formation energy and have high predicted ionic conductivities. High-throughput experimental and subsequent methodology optimization trials deliver one new compound, Na4SnP2O9 (NSP). X-ray diffraction (XRD), microscopy, and spectroscopy characterization indicate that the room-temperature structure of NSP is similar to the high-temperature, orthorhombic NTP phase but with some small unresolved structural differences. These uncharacterized structural details are speculated to limit the ion conductivity. Temperature-dependent XRD and electrochemical impedance spectroscopy indicate multiple coupled conductivity–structure transitions at a high temperature. We demonstrate the challenges with synthesis development and a priori identification of promising SSE phases as a major bottleneck in new (energy) materials development.

Chemical reactions↗

Thermodynamic evaluation and modeling of the CeF 3 -LaF 3 -LiF-NaF system

The thermodynamic properties of the LiF-NaF-CeF 3 -LaF 3 pseudo-quaternary system have been evaluated, and models for the relevant phases have been revised or newly developed. The solid solubility of NaF in LaF 3 and CeF 3 was described in the model based on reported phase-equilibria observations and results from density functional theory calculations. The Gibbs energy functions for the NaCeF 4 and NaLaF 4 phases have been updated with recently reported thermochemical measurements. The Gibbs energy functions and model relations for the LiF-NaF-LaF 3 and LiF-NaF-CeF 3 pseudo-ternary systems were successfully optimized based on thermochemical properties and phase equilibria observations.

CALPHAD↗

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗

Tensile and fatigue characterization of multifunctional composites

This research is part of a larger effort to develop advanced self-sensing multifunctional polymer composites that are both lightweight and high-strength, while also enabling structural damage detection, fatigue cycle monitoring, and service life prediction. These multifunctional composites are particularly sought after in the automotive industry for their potential to significantly reduce vehicle weight and simultaneously provide additional functionality like condition monitoring to enhance safety. This study examines the tensile and fatigue properties of a composite material composed of acrylonitrile butadiene styrene (ABS) polymer embedded with piezoelectric barium titanate (BaTiO3) nanoparticles. The integration of BaTiO3 nanoparticles not only supplies the material with self-sensing capabilities but also influences its mechanical properties. While a high content of BaTiO3 nanoparticles is desired to enhance sensing capacity, the brittle nature of such materials causes concerns of decreased strength characteristics. To explore this, various composite samples were fabricated with nanoparticle contents ranging from 0 wt% to 20 wt%. These samples underwent tensile testing to measure their ultimate tensile strengths and Young’s moduli. Following this, fatigue tests were conducted to generate S-N curves, which are essential for understanding the material's durability under cyclic loading. The findings from these tests assess the impact of nanoparticle content on the composite’s tensile strength and fatigue life, providing essential insights that can guide the optimization and design of future self-sensing multifunctional composites. The results suggest that 5 wt% BaTiO3 provides an optimal balance between mechanical properties and nanoparticle concentration, making it a promising composition for semi-structural applications.

Bowland, Christopher [ORNL] (ORCID:000000021229431↗

The Binary–Host Connection: Astrophysics of Gravitational-Wave Binaries from Host Galaxy Properties

Gravitational waves from the merger of binary neutron stars (BNSs) are accompanied by electromagnetic counterparts, making it possible to identify the associated host galaxy. In this work, we explore how properties of the hosts relate to the astrophysical processes leading to the mergers. It is thought that the BNS merger rate within a galaxy at a given epoch depends primarily on the galaxy's star formation history, as well as the underlying merger time-delay distribution of the binary systems. The stellar history of a galaxy, meanwhile, depends on the cosmological evolution of the galaxy through time, and is tied to the growth of structure in the universe. We study the hosts of BNS mergers in the context of structure formation by populating the UniverseMachine simulations with gravitational wave (GW) events, based on a simple time-delay model. We find that different time-delay distributions predict different properties of the associated host galaxies, including the distributions of stellar mass, star formation rate, halo mass, and local and large-scale clustering of hosts. Moreover, BNSs merging today with short delay times occur preferentially in hosts with high star formation rates, while those with long delay times live in dense regions within massive halos that have low star formation. Here, we show that with ${ \mathcal O }(10)$ events from current GW detector networks, it is possible to make preliminary distinctions between formation channels which trace stellar mass, halo mass, or star formation rate. We also find that strategies to follow-up GW events with electromagnetic telescopes can be significantly optimized using the clustering properties of their hosts.

79 ASTRONOMY AND ASTROPHYSICS↗

Hydrothermal synthesis of chiral carbon dots

Abstract Nanocolloids that are cumulatively referred to as nanocarbons, attracted significant attention during the last decade because of facile synthesis methods, water solubility, tunable photoluminescence, easy surface modification, and high biocompatibility. Among the latest development in this reserach area are chiral nanocarbons exemplified by chiral carbon dots (CDots). They are expected to have applications in sensing, catalysis, imaging, and nanomedicine. However, the current methods of CDots synthesis show often contradictory chemical/optical properties and structural information that required a systematic study with careful structural evaluation. Here, we investigate and optimize chiroptical activity and photoluminescence of L‐ and D‐ CDots obtained by hydrothermal carbonization of L‐ and D‐ cysteine, respectively. Nuclear magnetic resonance spectroscopy demonstrates that they are formed via gradual dehydrogenation and condensation reactions of the starting amino acid leading to particles with a wide spectrum of functional groups including aromatic cycles. We found that the chiroptical activity of CDots has an inverse correlation with the synthesis duration and temperature, whereas the photoluminescence intensity has a direct one, which is associated with degree of carbonization. Also, our studies show that the hydrothermal synthesis of cysteine in the presence of boric acid leads to the formation of CDots rather than boron nitride nanoparticles as was previously proposed in several reports. These results can be used to design chiral carbon‐based nanoparticles with optimal chemical, chiroptical, and photoluminescent properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Harnessing Structure-Property Relationships for Poly(alkyl thiophene)-Fullerene Derivative Thin Filmsto Optimize Performance in Photovoltaic Devices

Nanoscale bulk heterojunction (BHJ) systems, consisting of fullerenes dispersed in conjugated polymers as the active component, have been actively studied over the last decades in order to produce high performance organic photovoltaics (OPVs). A significant role in device efficiency is played by the active layer morphology, but despite considerable study, a full understanding of the exact role that morphology plays and therefore a definitive method to produce and control an ideal morphology is lacking. In order to understand the BHJ phase behavior and associated morphology in these devices, we have used neutron reflection, together with grazing incidence X-ray and neutron scattering and X-ray photoelectron spectroscopy (XPS) to determine the morphology of the BHJ active layer in functional devices. We have studied nine model BHJ systems based on mixtures of three poly(3-alkyl thiophenes, P3AT) (A=butyl, hexyl, octyl) blended with three different fullerene derivatives, which provides variations in crystallinity and miscibility within the BHJ composite. In studying properties of functional devices, we show a direct correlation between the observed morphology within the BHJ layer and the device performance metrics, i.e., the short-circuit current (J SC ), fill factor (FF), open-circuit voltage (VOC) and overall power conversion efficiency (PCE). Using these model systems, the effect of typical thermal annealing processes on the BHJ morphology through the film thickness as a function of the polythiophene-fullerene mixtures and different electron transport layer interfaces has been determined. It is shown that fullerene enrichment occurs at both the electrode interfaces after annealing. The degree of fullerene enrichment is found to strongly correlate with J SC and to a lesser degree with FF. Finally, based on these findings we demonstrate that by deliberately adding a fullerene layer at the electron transport layer interface, J SC can be increased by up to 20%, resulting in an overall increase in PCE of 5%.

36 MATERIALS SCIENCE↗

Scaling of silicon nanoparticle growth in low temperature flowing plasmas

Low temperature plasmas are an emerging method to synthesize high quality nanoparticles (NPs). An established and successful technique to produce NPs is using a capacitively coupled plasma (CCP) in cylindrical geometry. Although a robust synthesis technique, optimizing or specifying NP properties using CCPs, is challenging. In this paper, results from a computational investigation for the growth of silicon NPs in flowing inductively coupled plasmas (ICPs) using Ar/SiH 4 gas mixtures of up to a few Torr are discussed. ICPs produce more locally constrained and quiescent plasma potentials. These positive plasma potentials produce an electrostatic trap for negatively charged NPs, which can significantly extend the residence time of NPs in the plasma, which in turn provides a controllable period for particle growth. The computational platforms used in this study consist of a two-dimensional plasma hydrodynamics model, a three-dimensional nanoparticle growth and trajectory tracking model, and a molecular dynamics simulation for deriving reactive sticking coefficients of silane radicals on Si NPs. Trends for the nanoparticle growth as a function of SiH 4 inlet fraction, gas residence time, energy deposition per particle, pressure, and reactor diameter are discussed. The general path for particle synthesis is the trapping of small NPs in the positive electrostatic potential, followed by entrainment in the gas flow upon reaching a critical particle size. Optimizing or controlling NP synthesis then depends on the spatial distribution of plasma potential, the density of growth species, and the relative time that particles spend in the electrostatic trap and flowing through higher densities of growth species upon leaving the trap.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Optimized Gen-II FeCrAl cladding production in large quantity for campaign testing

There are two major objectives in this report; (1) to optimize microstructure control of ATF FeCrAl alloys during tube drawing processes, and (2) to provide an update on the progress of ATF FeCrAl tube production via commercial manufacturers. Experimental efforts have been made to optimize the process parameters balancing the tube fabricability, especially for tube drawing processes, and microstructure control of the final tube products. Lab-scale sheet materials of Gen II FeCrAl alloys (Mo-containing and Nb-containing FeCrAl alloys) were used in the study, combined with a stepwise warm-rolling process and intermediate annealing, aiming to simulate the tube drawing process in a commercial tube manufacturer. The intermediate annealing at 650ºC for 1h was suggested for the tube-drawing process of Mo-containing FeCrAl alloys because it successfully softened the material by recovering the work hardening introduced through the rolling step, without inducing grain coarsening due to recrystallization. The final tube product is expected to have stabilized deformed microstructure providing the improved tensile properties with sufficient ductility. Optimization efforts on Nb-containing FeCrAl alloys focused on the effect of alloying additions and annealing conditions on the stability of deformed microstructure. Relationships between the second-phase precipitates (Fe 2 Nb-Laves phase) and microstructure stability are discussed. FeCrAl tube production through commercial tube manufacturers is currently in progress. Three different manufacturers, Century Tubes, Inc. (CTI), Rhenium Alloys, Inc. (RAI), and Superior Tube Company, Inc. (STC), are providing capabilities for cold-drawing, warm-drawing, and HPTR cold-pilgering, respectively. The first two companies are currently working on large quantity tube production (expected 250 ft length) of Gen I model FeCrAl alloy (B136Y3, at CTI) and Gen II (C35M4, at RAI), with the process parameters obtained from the experimental efforts. The expected delivery dates are at the end of July, 2016, and the middle of June, 2016, respectively. Tube production at STC would be the first attempt to apply cold-pilgering to the FeCrAl alloys. Communication has been initiated, and the materials have been machined for the cold-pilgering process.

36 MATERIALS SCIENCE↗

Optimization of well design and CO 2 injection strategy for risk reduction in Class VI geological carbon sequestration wells

The safety and durability of Class VI wells are critical for geological carbon sequestration (GCS). However, current GCS operations face unique challenges: unlike traditional Class II wells, Class VI CO 2 injection wells operate at rates up to 100 times higher, dramatically increasing the risk of wellbore leakage and structural compromise due to severe temperature drops and associated mechanical stresses. Despite existing guidelines on material selection, there remains a substantial gap in understanding how rapid CO 2 injection rates, low surface temperatures, and variable reservoir conditions interact to threaten long-term well integrity. This study presents a comprehensive, original workflow integrating advanced analytical and numerical models for both well flow and well integrity analysis. By systematically simulating a wide range of field-relevant scenarios—including variations in injection rate, CO 2 temperature, and reservoir pressure—this work provides the first cross-validated assessment of cooling effects on wellbore. The results reveal that extreme temperature drops, up to 60 °C, can occur under high injection rates, particularly in depleted reservoirs, significantly increasing the risk of cement failure. Building on these insights, the study proposes innovative, practical well design and operational strategies, including ductile cement formulations, pre-stressing techniques, advanced insulation coatings, and proactive management of injection rates. The safety of Class VI well extends beyond simply using CO 2 resistant materials. Cement materials should possess optimal thermo-hydraulic-mechanical-chemical properties for effective performance. This work provides a scientific basis for optimizing Class VI well designs, with direct benefits for minimizing environmental risk, lowering operational costs, and enhancing the long-term reliability of GCS.

25 ENERGY STORAGE↗

A process optimization framework for laser direct energy deposition: Densification, microstructure, and mechanical properties of an Fe-Cr alloy

Laser Direct Energy Deposition (DED) is a metal additive manufacturing technique with the ability to fabricate large and complex parts through deposition of metal powders. However, achieving high-density parts and targeted build heights using DED can be challenging due to the large number of highly sensitive process variables. This work proposes a robust fabrication parameter optimization framework to generate process maps for primary parameters in DED, including laser power, scan speed, mass flow rate, hatch spacing, and layer height. Simple single-track experiments were utilized to map out the parameter space, and a combination of geometric criteria for hatch spacing and layer height were proposed to determine parameter sets that achieve both targeted build heights and mitigate porosity formation. Using this framework, specimens with >99 % density and consistent mechanical properties were successfully fabricated over a wide range of process parameters for an Fe-9wt.%Cr (Fe9Cr) alloy, a surrogate for radiation damage-resistant reduced activation ferritic/martensitic (RAFM) steels. Processing these materials using DED is of particular interest in the development of plasma facing components for nuclear fusion applications. The microstructure and mechanical properties of as-printed Fe9Cr were characterized using optical and electron microscopy, X-ray diffraction, and uniaxial tensile tests. As-printed Fe9Cr displayed ~25 % elongation and ultimate tensile strengths of up to 475 MPa which is comparable to similar wrought alloys. Finally, the proposed framework will allow for accelerated DED parameter optimization for novel alloy systems, as well as open the possibility for local microstructure control while simultaneously mitigating defect formation.

42 ENGINEERING↗

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

36 MATERIALS SCIENCE↗

Synergy and destructive interferences between local magnetic anisotropies in binuclear complexes

Magnetic anisotropy is responsible for the single molecule magnet behavior of transition metal complexes. This behavior is characterized by a slow relaxation of the magnetization for low enough temperatures, and thus for a possible blocking of the magnetization. This bistable behavior can lead to possible technological applications in the domain of data storage or quantum computing. Therefore, the understanding of the microscopic origin of magnetic anisotropy has received a considerable interest during the last two decades. The presentation focuses on the determination of the anisotropy parameters of both mono-nuclear and bi-nuclear types of complexes and on the control and optimization of the anisotropic properties. The validity of the model Hamiltonians commonly used to characterize such complexes has been questioned and it is shown that neither the standard multispin Hamiltonian nor the giant spin Hamiltonian are appropriate for weakly coupled ions. Alternative models have been proposed and used to properly extract the relevant parameters. Rationalizations of the magnitude and nature of both local anisotropies of single ions and the molecular anisotropy of polynuclear complexes are provided. The synergy and interference effects between local magnetic anisotropies are studied in a series of binuclear complexes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Bayesian Optimization of Catalysis with In-Context Learning

Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning (ICL), allowing the model to observe query-relevant examples at inference time and eliminating the need for additional weight updates to generalize beyond its original training data. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-4o, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing materials as synthesis and testing procedures for use in natural language prompts. This Bayesian, design-first approach prioritizes optimization toward target material properties before detailed characterization, in contrast to conventional experimental workflows that often emphasize characterization of suboptimal materials. On benchmarks like aqueous solubility and oxidative coupling of methane (OCM), BO-ICL matches or outperforms Gaussian processes. In live experiments on the reverse water–gas shift (RWGS) reaction, BO-ICL identifies multimetallic catalysts that approach equilibrium CO yield within 6 and 10 iterations from a pool of 3,700 and 360,000 candidates, respectively. Our method redefines materials representation and accelerates discovery, with broad applications across catalysis, materials science, and AI.

Calibration↗

Validation of the Electrical Properties of the ITER ICRF Antenna using Reduced-Scale Mock-Ups

Experimental measurements on reduced-scale mock-ups allow validating the electrical properties and RF numerical optimization of the ITER ICRF antenna. Frequency response in the different regions of the antenna is described and key parameters for performance improvement are given. Coupling is improved by acting on the front-face geometry (strap width, antenna box depth and vertical septa recess). The 4-port junction acts as a frequency filter and together with the service stub performs pre-matching in the whole frequency band. Influence of the Faraday screen on coupling is limited. The effect of voltage limitation on the maximum total radiated power is given. The importance of a good decoupling network and of grounding is emphasized. Finally the control of the antenna wave spectrum is performed by implementing feedback controlled load-resilient matching and decoupling options and control algorithms are tested.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Enhanced ferroelectric photoelectrochemical properties of polycrystalline BiFeO{sub 3} film by decorating with Ag nanoparticles

Polycrystalline BiFeO{sub 3} (BFO) films are fabricated on Pt/Ti/SiO{sub 2}/Si(100) substrate as photoelectrode using sol-gel method. The microstructure, optical, and photoelectrochemical (PEC) properties of the films are characterized and optimized by controlling the film thickness. Moreover, the PEC properties of the BFO films are dependent on ferroelectric polarization, which is mainly ascribed to the modulation of band structure at the BFO/electrolyte interface by the polarization. Further enhancement of PEC properties is obtained by decorating the samples with appropriate amounts of Ag nanoparticles, which is attributed to the reduced electron-hole recombination, and localized surface plasmon resonance effect of Ag nanoparticles.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Molecular packing control enables excellent performance and mechanical property of blade-cast all-polymer solar cells

All-polymer solar cells (all-PSCs) are the most promising power generators for flexible and portable devices due to excellent morphology stability and outstanding mechanical property. Prior work suggests high crystallinity is beneficial to device performance but detrimental to mechanical property, therefore identifying the optimized ratio between crystalline and amorphous domains becomes important. In this work, we demonstrated highly efficient and mechanically robust all-PSCs by blade-coating technology in ambient environment based on PTzBI:N2200 system. By controlling the aggregation in solution state and ultrafast film formation process, a weakly ordered molecular packing morphology as well as small phase separation is obtained, which leads to not only the good photovoltaic performance (8.36%-one of the best blade-cast device in air) but also prominent mechanical characteristic. The controlled film reflects a remarkable elongation with the crack onset strain of 15.6%, which is the highest result in organic solar cells without adding elastomers. These observations indicate the great promise of the developed all-PSCs for practical applications toward large-area processing technology.

42 ENGINEERING↗