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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 127 records · Page 7

The effect of rare-earth substitution on the Debye temperature of inorganic phosphors

In the quest to predict photoluminescent efficiency in rare-earth substituted inorganic phosphors, research has shown that materials consisting of ordered, rigid crystal structures tend to possess the highest photoluminescent quantum yields. A compound's Debye temperature (Θ D ), which can be calculated using ab initio calculations, is an ideal proxy for quantitatively comparing structural rigidity among different inorganic compounds, allowing potentially efficient phosphors to be selected from large crystal structure databases. However, the high computational cost of these calculations limits estimating Θ D for unsubstituted host crystal structures only. It is assumed that the low substitution concentration of the rare-earth luminescent center does not significantly influence a material's Debye temperature. This work evaluates the validity of this approximation by examining the effect of luminescent center substitution on a host structure's Θ D . Two well-known phosphors, (Y 1– x Ce x ) 3 Al 5 O 12 ( x = 0 – 0.05) and Ba 1-δ Eu δ MgAl 10 O 17 (δ = 0 – 0.15), were synthesized with varying rare-earth concentrations, while Θ D was computationally estimated and then determined by ultrasonic pulse-echo speed-of-sound and low-temperature heat capacity measurements. Overall, the ensuing results provide key implications for using Θ D as a proxy for structural rigidity in substituted inorganic compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices

Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices.

59 BASIC BIOLOGICAL SCIENCES↗

Ultra-Structural Imaging Provides 3D Organization of 46 Chromosomes of a Human Lymphocyte Prophase Nucleus

Three dimensional (3D) ultra-structural imaging is an important tool for unraveling the organizational structure of individual chromosomes at various stages of the cell cycle. Performing hitherto uninvestigated ultra-structural analysis of the human genome at prophase, we used serial block-face scanning electron microscopy (SBFSEM) to understand chromosomal architectural organization within 3D nuclear space. Acquired images allowed us to segment, reconstruct, and extract quantitative 3D structural information about the prophase nucleus and the preserved, intact individual chromosomes within it. Our data demonstrate that each chromosome can be identified with its homolog and classified into respective cytogenetic groups. Thereby, we present the first 3D karyotype built from the compact axial structure seen on the core of all prophase chromosomes. The chromosomes display parallel-aligned sister chromatids with familiar chromosome morphologies with no crossovers. Furthermore, the spatial positions of all 46 chromosomes revealed a pattern showing a gene density-based correlation and a neighborhood map of individual chromosomes based on their relative spatial positioning. A comprehensive picture of 3D chromosomal organization at the nanometer level in a single human lymphocyte cell is presented.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Correlating Rate-Dependent Transition Metal Dissolution between Structure Degradation in Li-Rich Layered Oxides

Understanding the mechanism of the rate-dependent electrochemical performance degradation in cathodes is crucial to developing fast charging/discharging cathodes for Li-ion batteries. Here, in this work, taking Li-rich layered oxide Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 as the model cathode, the mechanisms of performance degradation at low and high rates are comparatively investigated from two aspects, the transition metal (TM) dissolution and the structure change. Quantitative analyses combining spatial-resolved synchrotron X-ray fluorescence (XRF) imaging, synchrotron X–ray diffraction (XRD) and transmission electron microscopy (TEM) techniques reveal that low-rate cycling leads to gradient TM dissolution and severe bulk structure degradation within the individual secondary particles, and especially the latter causes lots of microcracks within secondary particles, and becomes the main reason for the fast capacity and voltage decay. In contrast, high-rate cycling leads to more TM dissolution than low-rate cycling, which concentrates at the particle surface and directly induces the more severe surface structure degradation to the electrochemically inactive rock-salt phase, eventually causing a faster capacity and voltage decay than low-rate cycling. These findings highlight the protection of the surface structure for developing fast charging/discharging cathodes for Li-ion batteries.

25 ENERGY STORAGE↗

Sensitivity of Space Shuttle Weight and Cost to Structure Subsystem Weights

Quantitative relationships between changes in space shuttle weights and costs with changes in weight of various portions of space shuttle structural subsystems are investigated. These sensitivity relationships, as they apply at each of three points in the development program (preliminary design phase, detail design phase, and test/operational phase) have been established for five typical space shuttle designs, each of which was responsive to the missions in the NASA Shuttle RFP, and one design was that selected by NASA.

Wedge, T. E.↗

Benchmark microgravity experiments and computations for 3D dendritic-array stability in directional solidification

In this study, we present a comprehensive quantitative analysis of stability bands for dendritic arrays during directional solidification of a transparent succinonitrile-0.46 wt % camphor alloy, spanning a broad range of pulling velocities. Taking advantage of the microgravity environment aboard the International Space Station where most convection effects are suppressed, we obtain unique measurements that quantify the stable primary spacing range of spatially extended three-dimensional dendritic array structures under purely diffusive growth conditions. Through carefully designed velocity jump experiments and detailed examination of sub-grain boundary dynamics, we characterize key instabilities, including elimination and tertiary branching, shedding new light on the mechanisms governing dynamic dendritic spacing selection in extended 3D arrays. Phase field simulations are performed to characterize the stability limits of dendritic array structures for quantitative comparison with the flight experiments. Although the simulations capture general trends, significant deviations are noted at the upper stability boundary, indicating the influence of additional, unexplored factors. These findings contribute to a deeper understanding of dendritic growth dynamics and offer valuable benchmark data that could aid in refining predictive models and improving control of dendritic microstructures in metallurgical applications.

36 MATERIALS SCIENCE↗

Probing Sulfur Chemical and Electronic Structure with Experimental Observation and Quantitative Theoretical Prediction of Kα and Valence-to-Core Kβ X-ray Emission Spectroscopy

An extensive experimental and theoretical study of the Kα and Kβ high-resolution X-ray emission spectroscopy (XES) of sulfur-bearing systems is presented here. This study encompasses a wide range of organic and inorganic compounds, including numerous experimental spectra from both prior published work and new measurements. Employing a linear-response time-dependent density functional theory (LR-TDDFT) approach, strong quantitative agreement is found in the calculation of energy shifts of the core-to-core Kα as well as the full range of spectral features in the valence-to-core Kβ spectrum. The ability to accurately calculate the sulfur Kα energy shift supports the use of sulfur Kα XES as a bulk-sensitive tool for assessing sulfur speciation. The fine structure of the sulfur Kβ spectrum, in conjunction with the theoretical results, is shown to be sensitive to the local electronic structure including effects of symmetry, ligand type and number, and, in the case of organosulfur compounds, to the nature of the bonded organic moiety. This agreement between theory and experiment, augmented by the potential for high-access XES measurements with the latest generation of laboratory-based spectrometers, demonstrates the possibility of broad analytical use of XES for sulfur and nearby third-row elements. The effective solution of the forward problem, i.e., successful prediction of detailed spectra from known molecular structure, also suggests future use of supervised machine learning approaches to experimental inference, as has seen recent interest for interpretation of X-ray absorption near-edge structure (XANES).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Kβ X-ray Emission Spectra Analysis Using Bayesian Optimization

The Kβ X-ray emission spectrum of 3 d transition metals is rich with electronic and structural information due to strong exchange interactions with the valence shell of the metal, and has become crucial for understanding their spin and oxidation states. The spectrum is commonly treated using crystal-field multiplet theory, a semi-empirical theory that uses tunable parameters to control the strength of the effects present in X-ray emission spectroscopy (XES). However, determining the experimental values of these parameters remains a challenge. We present a methodology that applies Bayesian optimization to crystal-field multiplet theory to determine parameter values. The algorithm is tested on the X-ray emission spectra of a collection of Mn, Co, and Ni oxides. We are able to find optimal values for the four most impactful parameters: Slater−Condon reduction factors F dd , F pd , and G pd , and crystal field splitting 10 Dq . The algorithm produces significantly improved accuracy compared to current analysis methods, and probes interparameter dependencies by modeling the error landscape. This advancement enhances XES analysis by offering an approach of obtaining quantitative electronic structural information on 3 d transition metal valence shells, facilitating applications across various scientific fields.

Bayesian optimization↗

Formation of PAHs, phenol, benzofuran, and dibenzofuran in a flow reactor from the oxidation of ethylene, toluene, and n-decane

The necessity to reveal the formation mechanism of not only polycyclic aromatic hydrocarbons (PAHs) but also oxygenated PAHs (OPAHs) during combustion is increasing. Although many studies on PAHs have been conducted, fundamental studies investigating OPAH formation are still limited. Phenol, benzofuran, and dibenzofuran were selected as OPAHs in this study. We experimentally investigated fuel-rich oxidation in a flow reactor at atmospheric pressure, mean gas temperatures from 1050 to 1350 K, residence times from 0.2 to 1.5 s, and equivalence ratios from 3.0 to 12.0. Ethylene, toluene, and n-decane were used as fuels. Three kinds of OPAHs as well as 23 kinds of PAHs including monocyclic structures were quantitatively measured through a direct sampling using gas chromatography mass spectrometry. The results showed that concentrations of PAHs and OPAHs were strongly affected by the fuel type, with increasing concentrations in the following order: ethylene < n-decane < toluene. A chemical kinetic model for OPAH formation was developed based on a recent PAH growth model. The model showed that the predicted concentrations of OPAHs and PAHs were in reasonable agreement with the measured data in this study. Some modifications were made to the previous model based on recent literature studies and on the comparison of the simulated and measured results. The effect of the fuel type on the formation of PAHs and OPAHs was investigated through kinetic analysis using the model to discuss their reaction pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Understanding Asteroid 16 Psyche’s composition through 3D impact crater modeling

Asteroid 16 Psyche is the largest M-type (metallic) Main Belt Asteroid (MBA). Radar albedo data indicate Psyche’s surface is rich in metallic content, but estimates for Psyche’s bulk structure vary widely, with bulk density estimates ranging from 1.4 ± 0.3 g/cm 3 to 4.5 ± 1.4 g/cm 3 and porosity estimates ranging from 30% to 70%. A forthcoming NASA discovery mission will visit Psyche. Narrowing the range of estimated compositions of Psyche prior to mission launch can help to determine the necessary tools for asteroid analysis. Psyche has two large impact structures in its Southern hemisphere, with estimated diameters from 50 km to 70 km and estimated depths up to 6.4 km. In this work, we present results from 2D and 3D hydrocode simulations of the largest of these impact structures. Through quantitative comparison of the simulated crater dimensions with measured values, our models suggest that Psyche is largely composed of porous, metallic material. In addition, to match the crater dimensions, our simulations indicate that the angle of impact was significantly altered from vertical by 45–60 degrees.

79 ASTRONOMY AND ASTROPHYSICS↗

The influence of packing structure and interparticle forces on ultrasound transmission in granular media

Ultrasound propagation through externally stressed, disordered granular materials was experimentally and numerically investigated. Experiments employed piezoelectric transducers to excite and detect longitudinal ultrasound waves of various frequencies traveling through randomly packed sapphire spheres subjected to uniaxial compression. The experiments featured in situ X-ray tomography and diffraction measurements of contact fabric, particle kinematics, average per-particle stress tensors, and interparticle forces. The experimentally measured packing configuration and inferred interparticle forces at different sample stresses were used to construct spring networks characterized by Hessian and damping matrices. The ultrasound responses of these network were simulated to investigate the origins of wave velocity, acoustic paths, dispersion, and attenuation. Results revealed that both packing structure and interparticle force heterogeneity played an important role in controlling wave velocity and dispersion, while packing structure alone quantitatively explained most of the observed wave attenuation. This research provides insight into time- and frequency-domain features of wave propagation in randomly packed granular materials, shedding light on the fundamental mechanisms controlling wave velocities, dispersion, and attenuation in such systems.

36 MATERIALS SCIENCE↗

Magnetic reconnection in 3D fusion devices: non-linear reduced equations and linear current-driven instabilities

Abstract Magnetic reconnection in 3D fusion devices is investigated. With the use of Boozer co-ordinates, we reduce the non-linear resistive magnetohydrodynamic equations in the limit of large aspect ratio and finite pressure fluctuations, to obtain a set of non-linear equations suitable for magnetic reconnection studies in stellarators. Magnetic flux unfreezing due to a finite electron mass is also considered. Equations that govern the linear regime and some of their general properties are given. We emphasise the role of magnetic geometry and identify how some aspects of stellarator optimisation could have an impact on reconnecting instabilities, in particular by exacerbating those enabled by electron inertia. The effect of 3D coupling on the linear reconnection rates and the mode structure is quantitatively addressed in the case in which the equilibrium rotational transform has one specific resonant location for which one mode can reconnect while coupled to an arbitrary number of non-resonant harmonics. The full problem is rigorously reduced to an equivalent cylindrical one, by introducing some geometrically modified plasma inertial and dissipative scales. The 3D scalings for the growth rates of reconnection instabilities and their destabilisation criteria are given.

Physics↗

Development of efficient solar cells using combination of QSPR and DFT approaches

The awarded research project aimed to design and determine the effective organic dye-sensitizers from the ensemble of chromophores for the dye-sensitized solar cell (DSSC) and fullerene-derivatives (FDs) as acceptor for polymer solar cell (PSC) followed by explore the electron transfer mechanism using the combined quantitative structure-property relationship (QSPR) analysis in conjunction with density functional theory (DFT) and time-dependent DFT (TDDFT)-based calculations. Based on our proposal we have employed different in silico approaches to design photo-efficient organic dye-sensitizers for DSSCs and FDs as acceptor for PSCs with exhaustive screening methodology.

14 SOLAR ENERGY↗

DOE STTR Phase I Final Report Report: Machine-learning Based Prediction of Thermal Limits for Conjugated Organic Materials

The SCANN-DT Phase I STTR project led by NLM Photonics and partnering with the National Renewable Energy Laboratory (NREL) sought to apply machine learning techniques based on graph neural networks (GNNs) towards the prediction of decomposition temperatures (Td) of organic semiconductors, based on prior work on bond dissociation energy (BDE) prediction as implemented in NREL’s ALFABET prediction tool. Using a curated set of experimental decomposition energies, the project examined GNN-based, classical quantitative structure-property relationship (QSPR) based on DFT calculations, and combinations of both methods to predict Td. While the best MAEs in Td achieved were near 40°C, below project targets, the project led to improvements in the ALFABET model, improvements in cloud-based implementations of NWChem software, and a substantial dataset of calculations on medium-sized conjugated organic molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.

59 BASIC BIOLOGICAL SCIENCES↗

Prediction of Research/Motor Octane Number and Octane Sensitivity Using Artificial Neural Networks

Octane sensitivity (OS), dfined as the research octane number (RON) minus the motor octane number (MON) of a given fuel, has gained interest among researchers due to its apparent ffect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially with respect to advanced compression ignition engines. RON/MON must be experimentally tested to determine OS; however, the experimental methods utilized require a substantial amount of time, a significant monetary investment, and specialized equipment. To this end, predictive computational models trained with existing experimental data and molecular properties would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artficial neural networks (ANNs) trained with quantitative structure-property relationship (QSPR) descriptors to predict RON and MON individually to compute OS from RON/MON predictions (derived octane sensitivity, dOS), and using an ANN trained with QSPR descriptors to directly predict OS. ANNs trained to predict RON and MON achieved test set root-mean-square errors (RMSEs) of 10.499 and 7.551 respectively. dOS calculations were found to have a test set RMSE of 6.432 while predicting OS directly resulted in a test set RMSE of 7.019, showing it is more bene cial to obtain OS from RON/MON predictions than predicting it directly. Furthermore, relationships between individual QSPR descriptors and RON/MON/OS are discussed, highlighting correlations between specfic molecular features and these properties.

09 BIOMASS FUELS↗

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington’s Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80–83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70–72% in negative predictivity, and 78–86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

59 BASIC BIOLOGICAL SCIENCES↗