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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Crystal structures of tricalcium citrates

The crystal structures of calcium citrate hexahydrate, calcium citrate tetrahydrate, and anhydrous calcium citrate have been solved using laboratory and synchrotron X-ray powder diffraction data, and optimized using density functional techniques. Both the hexahydrate and tetrahydrate structures are characterized by layers of edge-sharing Ca coordination polyhedra, including triply chelated Ca. An additional isolated Ca is coordinated by water molecules, and two uncoordinated water molecules occur in the hexahydrate structure. The previously reported polymorph of the tetrahydrate contains the same layers, but only two H 2 O coordinated to the isolated Ca and two uncoordinated water molecules. Anhydrous calcium citrate has a three-dimensional network structure of Ca coordination polyhedra. The new polymorph of calcium citrate tetrahydrate is the major crystalline phase in several commercial calcium supplements.

Kaduk, James A.↗
DOE OSTI2025

From Structure to Function: Zn/Mn-Modified Maghemite as an Advanced Nanoplatform for Magnetic Hyperthermia and Radionuclide Therapy

The development of nanoplatforms capable of efficient heat generation and stable radionuclide delivery is essential for effective bimodal cancer therapy. Here, in this study, binary (Fe–M) and ternary (Fe–M–M′) metal oxide nanoparticles were synthesized via a polyol method optimized to produce flower-like γ-Fe 2 O 3 (maghemite) structures, with M and M′ representing Zn and/or Mn. Comprehensive structural and magnetic characterization was conducted to explain the relationship between composition, defect structure, and hyperthermic performance. The analyses revealed that cation substitution induced an Fe-site vacancy, primarily at octahedral positions, leading to local structural distortions, as confirmed by powder X-ray diffraction and pair distribution function analysis. The optimized composition, with Zn/Mn/Fe = 0.040:0.182:1, exhibited the highest concentration of vacancies and structural disorder. These vacancies altered the bonding environment, enhancing magnetic interactions at tetrahedral sites while weakening those at the octahedral positions. The resulting multicore nanoflowers (20–63 nm; core size 13–18 nm) displayed strong heating performance, with intrinsic loss power ranging from 0.34 to 5.77 nHm 2 kg –1 . The optimized sample achieved a temperature increase of 30 °C within 2 min and a specific absorption rate of 369 W g –1 . This composition was further coated with citrate (CA) and successfully radiolabeled with 177 Lu, achieving a radiolabeling yield of 92.7% and excellent stability, thus forming a robust nanoplatform for combined magnetic hyperthermia and radionuclide therapy. Biological evaluation of the optimized S5 composition revealed selective cytotoxicity toward HeLa and LS174 cells, while toxicity was significantly lower to A549, A375, and normal MRC-5 cells. Citrate coating of S5 nanoparticles (S5@CA) drastically reduced their cytotoxicity across all tested cell lines (IC 50 > 200 μg mL –1 ), confirming their enhanced biocompatibility for therapeutic applications. In HeLa cells subjected to magnetic hyperthermia, the viability decreased to approximately 84% after 30 min and 61% after 60 min of treatment, demonstrating the sustained hyperthermic effect at a controlled working temperature of 48 °C. These results underscore the effectiveness of cation substitution and vacancy engineering in tailoring the functional properties of maghemite-based nanomaterials for advanced multimodal cancer therapies.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Topology optimization beyond linear elasticity

Complex, non-linear path-dependent and transient response of engineering structures can today be numerically modeled with good accuracy. For linear systems, this modeling has successfully been combined with computational design optimization for many years. The combination of non-linear modeling and numerical design optimization is far less established. In the present paper we discuss some recent steps taken to close the gap between the use of linear and non-linear modeling with numerical optimization. Examples with nonlinear elasticity, finite strain plasticity and dynamic modeling combined with topology optimization are discussed.

42 ENGINEERING↗
DOE OSTI2021

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k -space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

Tsymbalov, Evgenii↗
DOE OSTI2017

Development of reactive force fields using ab initio molecular dynamics simulation minimally biased to experimental data

Incorporation of quantum mechanical electronic structure data is necessary to properly capture the physics of many chemical processes. Proton hopping in water, which involves rearrangement of chemical and hydrogen bonds, is one such example of an inherently quantum mechanical process. Standard ab initio molecular dynamics (AIMD) methods, however, do not yet accurately predict the structure of water and are therefore less than optimal for developing force fields. We have instead utilized here a recently developed method which minimally biases AIMD simulations to match limited experimental data to develop novel multiscale reactive molecular dynamics (MS-RMD) force fields by using relative entropy minimization. In this paper, we present two new MS-RMD models using such a parameterization: one which employs water with harmonic internal vibrations and another which uses anharmonic water. We show that the newly developed MS-RMD models very closely reproduce the solvation structure of the hydrated excess proton in the target AIMD data. We also find that the use of anharmonic water increases proton hopping, thereby increasing the proton diffusion constant.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Global seismic attenuation imaging using full-waveform inversion: a comparative assessment of different choices of misfit functionals

We present the results of synthetic tests that aim at evaluating the relative performance of three different definitions of misfit functionals in the context of 3-D imaging of shear wave attenuation in the earth’s upper mantle at the global scale, using long-period full-waveform data. The synthetic tests are conducted with simple hypothetical upper-mantle models that contain Q μ anomalies centred at different depths and locations, with or without additional seismic velocity anomalies. To build synthetic waveform data sets, we performed simulations of 50 events in the hypothetical (target) models, using the spectral element method, filtered in the period range 60–400 s. The selected events are chosen among 273 events used in the development of radially anisotropic model SEMUCB-WM1 and recorded at 495 stations worldwide. The synthetic Z-component waveforms correspond to paths and time intervals (fundamental mode and overtone Rayleigh waves) that exist in the real waveform data set. The inversions for shear attenuation structure are carried out using a Gauss–Newton optimization scheme in which the gradient and Hessian are computed using normal mode perturbation theory. The three different misfit functionals considered are based on time domain waveform (WF) and waveform envelope (E-WF) differences, as well as spectral amplitude ratios (SA), between observed and predicted waveforms. We evaluate the performance of the three misfit functional definitions in the presence of seismic noise and unresolved S-wave velocity heterogeneity and discuss the relative importance of physical dispersion effects due to 3-D Q μ structure. We observed that the performance of WF is poorer than the other two misfit functionals in recovering attenuation structure, unless anelastic dispersion effects are taken into account in the calculation of partial derivatives. WF also turns out to be more sensitive to seismic noise than E-WF and SA. Overall, SA performs best for attenuation imaging. Our tests show that it is important to account for 3-D elastic effects (focusing) before inverting for Q μ . Additionally, we show that including high signal-to-noise ratio overtone wave packets is necessary to resolve Q μ structure at depths greater than 250 km.

58 GEOSCIENCES↗
DOE OSTI2016

Clinically Applicable Monte Carlo–based Biological Dose Optimization for the Treatment of Head and Neck Cancers With Spot-Scanning Proton Therapy

Purpose: Our aim is to demonstrate the feasibility of fast Monte Carlo (MC)–based inverse biological planning for the treatment of head and neck tumors in spot-scanning proton therapy. Methods and Materials: Recently, a fast and accurate graphics processor unit (GPU)–based MC simulation of proton transport was developed and used as the dose-calculation engine in a GPU-accelerated intensity modulated proton therapy (IMPT) optimizer. Besides dose, the MC can simultaneously score the dose-averaged linear energy transfer (LET{sub d}), which makes biological dose (BD) optimization possible. To convert from LET{sub d} to BD, a simple linear relation was assumed. By use of this novel optimizer, inverse biological planning was applied to 4 patients, including 2 small and 1 large thyroid tumor targets, as well as 1 glioma case. To create these plans, constraints were placed to maintain the physical dose (PD) within 1.25 times the prescription while maximizing target BD. For comparison, conventional intensity modulated radiation therapy (IMRT) and IMPT plans were also created using Eclipse (Varian Medical Systems) in each case. The same critical-structure PD constraints were used for the IMRT, IMPT, and biologically optimized plans. The BD distributions for the IMPT plans were obtained through MC recalculations. Results: Compared with standard IMPT, the biologically optimal plans for patients with small tumor targets displayed a BD escalation that was around twice the PD increase. Dose sparing to critical structures was improved compared with both IMRT and IMPT. No significant BD increase could be achieved for the large thyroid tumor case and when the presence of critical structures mitigated the contribution of additional fields. The calculation of the biologically optimized plans can be completed in a clinically viable time (<30 minutes) on a small 24-GPU system. Conclusions: By exploiting GPU acceleration, MC-based, biologically optimized plans were created for small–tumor target patients. This optimizer will be used in an upcoming feasibility trial on LET{sub d} painting for radioresistant tumors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Lifelike metallic structures using origami and compliant mechanisms

In nature, materials and designs are smart i.e., they achieve the desired shape or size to optimize different processes without needing external devices. However, in the design of mechanical metallic structures, gaining such behavior without using special materials or external sources is still a visionary thought. Here the concept of lifelike metallic structures is introduced. Further, the vision is to produce designs where the metallic walls can behave like human muscles. To create such structures, origami-inspired designs in combination with compliant mechanisms are proposed. For fabricating such designs with desired mechanical properties, it is also proposed to leverage additive manufacturing techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Distributed Rules-Based Deconfliction of ADMS Applications: Part 1: Requirements & Decomposition

This work introduces a rules-based deconfliction methodology for resolving conflicting device control commands issued by advanced power applications considering a range of technical, economic, environmental, and social objectives. The methodology is designed to serve as one of multiple alternative implementations (along with application cooperation and global optimization) for the numerical component of the Deconfliction Pipeline. Development of the methodology is divided into two parts. This first document introduces the requirements, context, and methods for decomposing the deconfliction problem using the Laminar Coordination Framework and Variable Grid Structures. The deconfliction problem is decomposed into a distributed optimization problem based on the concept of quasi-static grid segments, which form independent distributed areas for control and coordination. Selection of the optimal number of decompositions of the deconfliction problem should be made based on a tradeoff analysis between computational speed and global optimality. This second document will define an initial set of technical, economic, and environmental criteria, as well as thirty specific qualitative rules that are used as part of the deconfliction methodology to eliminate non-viable setpoint alternatives. The deconfliction optimization problem is converted into a ranking of individual discrete setpoints, which are scored by the extent to which they satisfy specific decision criteria. The ranking is determined through the concepts of deconfliction exclusivity, priority, and preference. Several multi-criteria decision-making frameworks are examined with the simple multi-attribute rating technique exploiting ranks (SMARTER) recommended as a simple implementation alternative that aligns with the steps of the rules-based deconfliction methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Engineering Atomic-to-Nano Scale Structural Homogeneity towards High Corrosion Resistance of Amorphous Magnesium-Based Alloys

Magnesium-based amorphous alloys have aroused broad interest in being applied in marine use due to their merits of lightweight and high strength. Yet, the poor corrosion resistance to chloride-containing seawater has hindered their practical applications. Herein, we propose a new strategy to improve the chloride corrosion resistance of amorphous Mg 65 Cu 15 Ag 10 Gd 10 alloys by engineering atomic-to-nano scale structural homogeneity, which is implemented by heating the material to the critical temperature of the liquid–liquid transition. By using various electrochemical, microscopic, and spectroscopic characterization methods, we reveal that the liquid–liquid transition can rearrange the local structural units in the amorphous structure, slightly decreasing the alloy structure’s homogeneity, accelerate the formation of protective passivation film, and, therefore, increase the corrosion resistance. Our study has demonstrated the strong coupling between an amorphous structure and corrosion behavior, which is available for optimizing corrosion-resistant alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Explicit solvent machine-learned coarse-grained model of sodium polystyrene sulfonate to capture polymer structure and dynamics

In this article, strongly charged polyelectrolytes (PEs) demonstrate complex solution behavior as a function of chain length, concentrations, and ionic strength. The viscosity behavior is important to understand and is a core quantity for many applications, but aspects remain a challenge. Molecular dynamics simulations using implicit solvent coarse-grained (CG) models successfully reproduce structure, but are often inappropriate for calculating viscosities. To address the need for CG models which reproduce viscoelastic properties of one of the most studied PEs, sodium polystyrene sulfonate (NaPSS), we report our recent efforts in using Bayesian optimization to develop CG models of NaPSS which capture both polymer structure and dynamics in aqueous solutions with explicit solvent. We demonstrate that our explicit solvent CG NaPSS model with the ML-BOP water model [Chan et al. Nat Commun 10, 379 (2019)] quantitatively reproduces NaPSS chain statistics and solution structure. The new explicit solvent CG model is benchmarked against diffusivities from atomistic simulations and experimental specific viscosities for short chains. We also show that our Bayesian-optimized CG model is transferable to larger chain lengths across a range of concentrations. Overall, this work provides a machine-learned model to probe the structural, dynamic, and rheological properties of polyelectrolytes such as NaPSS and aids in the design of novel, strongly charged polymers with tunable structural and viscoelastic properties

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Nanoscale structural correlations in a model cuprate superconductor

Understanding the extent and role of inhomogeneity is a pivotal challenge in the physics of cuprate superconductors. While it is known that structural and electronic inhomogeneity is prevalent in the cuprates, it has proven difficult to disentangle compound-specific features from universally relevant effects. Here, in this study, we combine advanced neutron and x-ray diffuse scattering with numerical modeling to obtain insight into bulk structural correlations in HgBa 2 ⁢ CuO 4+δ . This cuprate exhibits a high optimal transition temperature of nearly 100 K, pristine charge-transport behavior, and a simple average crystal structure without long-range structural instabilities, and is therefore uniquely suited for investigations of intrinsic inhomogeneity. We uncover diffuse reciprocal-space patterns that correspond to prominent nanoscale correlations of atomic displacements perpendicular to the CuO 2 planes. The real-space nature of the correlations is revealed through three-dimensional pair distribution function analysis and complementary numerical refinement. We find that relative displacements of ionic and CuO 2 layers play a crucial role, and that the structural inhomogeneity is not directly caused by the presence of conventional point defects. The observed correlations are therefore intrinsic to HgBa 2 ⁢ CuO 4+δ , and thus likely important for the physics of cuprates more broadly. It is possible that the structural correlations are closely related to the unusual superconducting fluctuations and Mott-localization in these complex oxides. As advances in scattering techniques yield increasingly comprehensive data, the experimental and analysis tools developed here for large volumes of diffuse scattering data can be expected to aid future investigations of a wide range of materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Combined shape and topology optimization for minimization of maximal von Mises stress

Here, this work shows that a combined shape and topology optimization method can produce optimal 2D designs with minimal stress subject to a volume constraint. The method represents the surface explicitly and discretizes the domain into a simplicial complex which adapts both structural shape and topology. By performing repeated topology and shape optimizations and adaptive mesh updates, we can minimize the maximum von Mises stress using the p-norm stress measure with p-values as high as 30, provided that the stress is calculated with sufficient accuracy.

42 ENGINEERING↗
DOE OSTI2017

Structural Evolution of Sub-10 nm Octahedral Platinum$-$Nickel Bimetallic Nanocrystals

Octahedral Pt alloy nanocrystals (NCs) have shown excellent activities as electrocatalysts toward oxygen reduction reaction (ORR). As the activity and stability of NCs are highly dependent on their structure and the elemental distribution, it is of great importance to understand the formation mechanism of octahedral NCs and to rationally synthesize shape-controlled alloy catalysts with optimized ORR activity and stability. However, the factors controlling the structural and compositional evolution during the synthesis have not been well understood yet. Here in this paper, we systematically investigated the structure and composition evolution pathways of Pt–Ni octahedra synthesized with the assistance of W(CO) 6 and revealed a unique core–shell structure consisting of a Pt core and a Pt–Ni alloy shell. Below 140 °C, sphere-like pure Pt NCs with the diameter of 3–4 nm first nucleated, followed by the isotropic growth of Pt–Ni alloy on the seeds at temperatures between 170 and 230 °C forming Pt@Pt–Ni core–shell octahedra with {111} facets. Owing to its unique structure, the Pt@Pt–Ni octahedra show an unparalleled stability during potential cycling, that is, no activity drop after 10 000 cycles between 0.6 and 1.0 V. This work proposes the Pt@Pt–Ni octahedra as a high profile electrocatalyst for ORR and reveals the structural and composition evolution pathways of Pt-based bimetallic NCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Dataset For: A Guide to Residential Energy Storage and Rooftop Solar: State Net Metering Policies and Utility Rate Tariff Structures

Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies facing the challenge of above-average electric utility rates. This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.

14 SOLAR ENERGY↗
DOE OSTI2023

Polythiophene-based terpolymers with modulated aggregation behaviors for high-performance organic solar cells with 16.6% efficiency

Polythiophenes (PTs) are an attractive class of polymer donors (PDs) for organic solar cells (OSCs) owing to their relatively simple structures and scalable synthesis. Herein, a series of chlorinated thiazole-incorporated PT terpolymers are designed and high-performance OSCs with a power conversion efficiency (PCE) of 16.6% are demonstrated. By incorporating two different units, 3,3'-difluoro-2,2'-bithiophene (T2F2) and thieno[3,2-b]thiophene (TT), the aggregation properties of the terpolymers (PTz-FX; X = 0, 30, 50, 70, and 100, where X represents the mole percentage of T2F2 to total T2F2 +TT) are modulated. Among the PTz-FX series, PTz-F70 is found to be the optimal P D because its suitably tuned aggregation property leads to an optimized blend morphology with well-developed crystalline structures and donor–acceptor intermixed domains. The balanced morphology not only promotes charge generation/transport but also suppresses charge recombination in OSC devices. Thus, the PTz-F70-based OSCs achieve the highest PCE (16.6%), outperforming the OSCs based on PTz-FX with extremely strong (PTz-F100, PCE = 14.7%) or weak (PTz-F0, PCE = 12.0%) aggregation properties. The PCE of the PTz-F70-based OSCs is one of the highest performances among PT-based binary OSCs. Finally, this study highlights the importance of controlling the aggregation property of PTs for achieving high-performance PT-based OSCs.

14 SOLAR ENERGY↗
DOE OSTI2023

Distributed Rules-Based Deconfliction of ADMS Applications - Part 2: Conceptual Implementation

This work introduces a rules-based deconfliction methodology for resolving conflicting device control commands issued by advanced power applications considering a range of technical, economic, environmental, and social objectives. The methodology is designed to serve as one of multiple alternative implementations (along with application cooperation and global optimization) for the numerical component of the Deconfliction Pipeline. Development of the methodology is divided into two parts. The first document previously defined the requirements, context, and methods for decomposing the deconfliction problem using the Laminar Coordination Framework and Variable Grid Structures. The deconfliction problem was decomposed into a distributed optimization problem based on the concept of quasi-static grid segments, which form independent distributed areas for control and coordination. It was recommended that selection of the optimal number of decompositions of the deconfliction problem be made based on a tradeoff analysis between computational speed and global optimality. This second document defines an initial set of technical, economic, and environmental criteria, as well as thirty specific qualitative rules that are used as part of the deconfliction methodology to eliminate non-viable setpoint alternatives. The deconfliction optimization problem is converted into a ranking of individual discrete setpoints, which are scored by the extent to which they satisfy specific decision criteria. The ranking is determined through the concepts of deconfliction exclusivity, priority, and preference. Several multi-criteria decision-making frameworks are examined with the simple multi-attribute rating technique exploiting ranks (SMARTER) recommended as a simple implementation alternative that aligns with the steps of the rules-based deconfliction methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Liquid Crystal Orientation and Shape Optimization for the Active Response of Liquid Crystal Elastomers

Liquid crystal elastomers (LCEs) are responsive materials that can undergo large reversible deformations upon exposure to external stimuli, such as electrical and thermal fields. Controlling the alignment of their liquid crystals mesogens to achieve desired shape changes unlocks a new design paradigm that is unavailable when using traditional materials. While experimental measurements can provide valuable insights into their behavior, computational analysis is essential to exploit their full potential. Accurate simulation is not, however, the end goal; rather, it is the means to achieve their optimal design. Such design optimization problems are best solved with algorithms that require gradients, i.e., sensitivities, of the cost and constraint functions with respect to the design parameters, to efficiently traverse the design space. In this work, a nonlinear LCE model and adjoint sensitivity analysis are implemented in a scalable and flexible finite element-based open source framework and integrated into a gradient-based design optimization tool. To display the versatility of the computational framework, LCE design problems that optimize both the material, i.e., liquid crystal orientation, and structural shape to reach a target actuated shapes or maximize energy absorption are solved. Multiple parameterizations, customized to address fabrication limitations, are investigated in both 2D and 3D. The case studies are followed by a discussion on the simulation and design optimization hurdles, as well as potential avenues for improving the robustness of similar computational frameworks for applications of interest.

42 ENGINEERING↗