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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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Light-Induced Decoupling of Electronic and Magnetic Properties in Manganites

The strongly correlated material La 0.7 ⁢Sr 0.3 ⁢Mn⁢O 3 (LSMO) exhibits metal-to-insulator and magnetic transition near room temperature. Although the physical properties of LSMO can be manipulated by strain, chemical doping, temperature, or magnetic field, they often require large external stimuli. Further, to include additional flexibility and tunability, we developed a hybrid optoelectronic heterostructure that uses photocarrier injection from cadmium sulfide (Cd⁢ S) to an LSMO layer to change its electrical conductivity. LSMO exhibits no significant optical response; however, the Cd ⁢S/LSMO heterostructures show an enhanced conductivity, with a resistance drop of about 37%, at the transition temperature under light stimuli. This enhanced conductivity in response to light is comparable to the effect of a 9 T magnetic field in pure LSMO. Surprisingly, the optical and magnetic responses of Cd⁢ S/LSMO heterostructures are decoupled and exhibit different effects when both stimuli are applied. This unexpected behavior shows that heterostructuring strongly correlated oxides may require a new understanding of the coupling of physical properties across the transitions and provide the means to implement new functionalities.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Gluing. Part I. Integrals and symmetries

We review some aspects of the cutting and gluing law in local quantum field theory (QFT) and study it from a new point of view. In particular, we emphasize the description of gluing by a path integral over a space of polarized boundary conditions, which are given by leaves of some Lagrangian foliation in the phase space. We think of this path integral as a non-local (d – 1)-dimensional gluing theory associated to the parent local d-dimensional QFT. This is a novel point of view paving the way for applications of the standard QFT techniques (that do not rely on locality) to the gluing theory. We describe various properties of this procedure and spell out conditions under which symmetries of the parent theory lead to symmetries of the gluing theory. The purpose of this paper is to set up a playground for the companion paper where these techniques are applied to obtain new results in supersymmetric theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Do Deep Eutectic Solvents Behave Like Ionic Liquid Electrolytes? A Perspective from the Electrode-Electrolyte Interface

The differential capacitance as a function of potential and composition for choline chloride (ChCl) and ethylene glycol (EG) at 1:2,1:4, 1:6, and 1:20 molar ratios were studied using electrochemical impedance spectroscopy (EIS) on glassy carbon. In addition, ChCl:EG at the known ethaline DES composition (1:2) and 1:20 molar ratio were studied on Au and Pt electrodes, and compared to glyceline (ChCl:glycerol, 1:2). The capacitance-potential curves on glassy carbon were best interpreted by the modified Gouy-Chapman model. A dampened U-shape was observed similar to dilute electrolytes, however, the non-negligible ionic and H-bonding interactions in these electrolytes resulted in ambiguity about the point of zero charge where capacitance was weakly dependent on potential. Additionally, the differential capacitance was independent of composition on glassy carbon due to limited availability of mobile free ions and constraints by solvated ion sizes within the interface. Increases in capacitance are observed with Au as a result of desolvation and specific adsorption of Cl, and with Pt upon decreased Cl concentrations. These results indicate DESs and H-bonded electrolytes with high salt concentrations are distinct from ionic liquids and present no pronounced over screening or crowding with applied potential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. Here, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Complexity: from Turbulence to Accelerators to Poetry

Though it may seem intuitive at times, the term 'complexity' is multifaceted, often encompassing various interacting aspects and components that follow local rules. This interaction leads to non-linearity, randomness, collective dynamics, hierarchy, and emergence. The phenomenological complexity found in large accelerators has recently been addressed by the Snowmass’21 Future Colliders Implementation Task Force (ITF). In this presentation, we will provide a concise overview of the mathematical constructs employed to assess hierarchical complexity, summarize the key findings from the ITF analysis, and offer a few examples demonstrating how this approach can be applied to analyze certain literaryclassics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Superhydrophobic coating and process of making same

An improved superhydrophobic coating and a process of making it is provided herein. More particularly, a robust superhydrophobic coating is produced by using carbon dioxide to enhance the integration of a binder material into the superhydrophobic coating. The carbon dioxide may be used to infiltrate and fill the interstitial voids of a superhydrophobic material, such as diatomaceous earth. Consequently, occupying these voids in the superhydrophobic material effectively blocks other components (e.g., binders) from entering the voids. As a result, the coating formulations of the present invention are more robust and may strongly adhere to the substrates to which they are applied.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Impact of strong magnetization in cylindrical plasma implosions with applied B-field measured via x-ray emission spectroscopy

Magnetization is a key strategy for enhancing inertial fusion performance, though accurate characterization of magnetized dense plasmas is needed for a better comprehension of the underlying physics. Measured spectra from imploding Ar-doped D 2 -filled cylinders at the OMEGA laser show distinctive features with and without an imposed magnetic field. A multizone spectroscopic diagnosis leads to quantitative estimates of the plasma conditions, namely revealing a 50% core temperature rise at half mass density when a 30-T seed field is applied. Concurrently, experimental spectra align well with predictions from extended-magnetohydrodynamics simulations, providing strong evidence that the attained core conditions at peak compression are consistent with the impact of a 10-kT compressed field. These results pave the way for the validation of magnetized transport models in dense plasmas and for future magnetized laser implosion experiments at a larger scale. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

FAIR Framework for Physics-Inspired Artificial Intelligence in High Energy Physics (Final Report)

The FAIR4HEP project was a collaboration between Argonne National Laboratory, University of Illinois Urbana-Champaign, Massachusetts Institute of Technology, University of Minnesota, and University of California San Diego funded by the US Department of Energy, Office of Science, Office of Advanced Scientific Research (ASCR) from 2020 to 2024. The primary focus of the FAIR4HEP project was to advance our understanding of the relationship between data and artificial intelligence (AI) models by exploring relationships among them through the development of findable, accessible, interoperable, and reusable (FAIR) frameworks. Using high-energy physics (HEP) as the science driver, this project developed a FAIR framework to advance our understanding of AI, provide new insights to apply AI techniques, and provide an environment where novel approaches to AI can be explored. This final report summarizes the accomplishments of the University of Illinois group.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Workflow for evaluating enzyme immobilization and performance for continuous flow manufacturing

Enzymes have shown promise in various industries due to their functional specificity, catalytic efficiency, and environmental sustainability. These biological catalysts can be a pivotal component of manufacturing pipelines like continuous flow chemistry. For this, there exists a need to robustly immobilize enzymes on solid supports and assess the effects of the solid supports on catalytic performance and stability. Here, we use an industrially relevant model enzyme, C. ensiformis (Jack bean) urease, to demonstrate immobilization and assess performance in the context of continuous flow manufacturing. Various immobilization strategies were screened focusing on immobilization efficiency, protocol simplicity, and urease biocatalyst kinetics. Based on this, CDI-agarose and NHS-agarose resins were identified as the best-performing immobilization strategies for urease. CDI-agarose-urease and NHS-agarose-urease were then scaled up and applied to a large-scale continuous flow reactor to evaluate product yields, operational stability, and long-term stability. These experiments identified differences in stability and performance depending on the immobilization method tested. This highlights the importance of screening immobilization methods and subsequent enzyme performance for each candidate biocatalyst used in manufacturing to promote optimal performance and stability. As such, this work provides a framework for evaluating enzyme biocatalyst immobilization approaches to improve performance and enable transition into industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1950

Isentropic phase changes in dissociating gases and the method of sound dispersion for the investigation of homogeneous gas reactions with very high speed : Conclusion

The analytical results of Part I are also applied to sound dispersion by friction and heat conduction, An irreversible change of momentum, energy, and type of particle corresponding to friction, heat conduction, and diffusion effects can appear both in the direction of the sound field and traverse to it. Longitudinal damping, the coupling of longitudinal damping and that due to chemical and physical changes, and coupling of diffusion and compositional changes are treated for a plane sound wave of infinite extent. The same principles are also applied to sound effects in cylindrical tubes. The limitations of the method are discussed in some detail.

SOUND VELOCITY - GASES↗
DOE OSTI2020

Quantum algorithms for disordered physics

We show how a quantum computer may efficiently simulate a disordered Hamiltonian, by incorporating a pseudo-random number generator directly into the time evolution circuit. This technique is applied to quantum simulation of few-body disordered systems in the large volume limit; in particular, Anderson localization. The method requires a number of (error corrected) qubits proportional to the logarithm of the volume of the system, and each time evolution step requires a number of gates polylogarithmic in the volume. We simulate the method to observe the metal-insulator transition on a three-dimensional lattice. Additionally, we demonstrate the algorithm on a one-dimensional lattice, using physical quantum processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1995

Thermal conductivity of zirconia thermal barrier coatings

Thermal barrier coatings (TBC's) applied to the hot gas components of turbine engines lead to enhanced fuel efficiency and component reliability. Understanding the mechanisms which control the thermal transport behavior of the TBC's is of primary importance. Physical vapor description (PVD) and plasma spraying (PS) are the two most commonly used coating techniques. These techniques produce coatings with unique microstructures which control their performance and stability. The PS coatings were applied with either standard power or hollow sphere particles. The hollow sphere particles yielded a lower density and lower thermal conductivity coating. The thermal conductivity of both fully and partially stabilized zirconia, before and after thermal aging, will be compared. The thermal conductivity of the coatings permanently increase upon being exposed to high temperatures. These increases are attributed to microstructural changes within the coatings. Sintering of the as fabricated plasma sprayed lamellar structure is observed by scanning electron microscopy of coatings isothermally heat treated at temperatures greater than 1100 C. During this sintering process the planar porosity between lamella is converted to a series of small spherical pores. The change in pore morphology is the primary reason for the observed increase in thermal conductivity. This increase in thermal conductivity can be modeled using a relationship which depends on both the temperature and time of exposure. Although the PVD coatings are less susceptible to thermal aging effects, preliminary results suggest that they have a higher thermal conductivity than PS coatings, both before and after thermal aging. The increases in thermal conductivity due to thermal aging for partially stabilized plasma sprayed zirconia have been found to be less than for fully stabilized plasma sprayed zirconia coatings. The high temperature thermal diffusivity data indicates that if these coatings reach a temperature above 1100 C during operation, they will begin to lose their effectiveness as a thermal barrier.

Dinwiddie, R. B.↗
NASA NTRS1995

Thermal conductivity of zirconia thermal barrier coatings

Thermal barrier coatings (TBC's) applied to the hot gas components of turbine engines lead to enhanced fuel efficiency and component reliability. Understanding the mechanisms which control the thermal transport behavior of the TBC's is of primary importance. Physical vapor deposition (PVD) and plasma spraying (PS) are the two most commonly used coating techniques. These techniques produce coatings with unique microstructures which control their performance and stability. The PS coatings were applied with either standard powder or hollow sphere particles. The hollow sphere particles yielded a lower density and lower thermal conductivity coating. The thermal conductivity of both fully and partially stabilized zirconia, before and after thermal aging, will be compared. The thermal conductivity of the coatings permanently increases upon exposed to high temperatures. These increases are attributed to microstructural changes within the coatings. Sintering of the as-fabricated plasma sprayed lamellar structure is observed by scanning electron microscopy of coatings isothermally heat treated at temperatures greater than 1100 C. During this sintering process the planar porosity between lamella is converted to a series of small spherical pores. The change in pore morphology is the primary reason for the observed increase in thermal conductivity. This increase in thermal conductivity can be modeled using a relationship which depends on both the temperature and time of exposure. Although the PVD coatings are less susceptible to thermal aging effects, preliminary results suggest that they have a higher thermal conductivity than PS coatings, both before and after thermal aging. The increases in thermal conductivity due to thermal aging for partially stabilized plasma sprayed zirconia have been found to be less than for fully stabilized plasma sprayed zirconia coatings. The high temperature thermal diffusivity data indicate that if these coatings reach a temperature above 1100 C during operation, they will begin to lose their effectiveness as a thermal barrier.

Dinwiddie, R. B.↗
DOE OSTI2020

New method for fitting coefficients in standard model effective theory

We present an alternative method for carrying out a principal-component analysis of Wilson coefficients in standard model effective field theory (SMEFT). The method is based on singular-value decomposition (SVD). The SVD method provides information about the sensitivity of experimental observables to physics beyond the standard model that is not accessible in the Fisher-information method. In principle, the SVD method can also have computational advantages over diagonalization of the Fisher information matrix. We demonstrate the SVD method by applying it to the dimension-6 coefficients for the process of top-quark decay to a b quark and a W boson and use this example to illustrate some pitfalls in widely used fitting procedures. We also outline an iterative procedure for applying the SVD method to dimension-8 SMEFT coefficients.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Superhydrophobic Array Devices for the Enhanced Formation of 3D Cancer Models

During the metastatic cascade, cancer cells travel through the bloodstream as circulating tumor cells (CTCs) to a secondary site. Clustered CTCs have greater shear stress and treatment resistance, yet their biology remains poorly understood. We therefore engineered a tunable superhydrophobic array device (SHArD). The SHArD-C was applied to culture a clinically relevant model of CTC clusters. Using our device, we cultured a model of cancer cell aggregates of various sizes with immortalized cancer cell lines. These exhibited higher E-cadherin expression and are significantly more capable of surviving high fluid shear stress-related forces compared to single cells and model clusters grown using the control method, helping to explain why clustering may provide a metastatic advantage. Additionally, the SHArD-S, when compared with the AggreWell 800 method, provides a more consistent spheroid-forming device culturing reproducible sizes of spheroids for multiple cancer cell lines. Overall, we designed, fabricated, and validated an easily tunable engineered device which grows physiologically relevant three-dimensional (3D) cancer models containing tens to thousands of cells.

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

Structure–Capacitance Relationships of Graphene/Ionic Liquid Electrolyte Double Layers

The differential capacitance profile of electrochemical interfaces reflects the physical properties of the double layer. For carbon electrodes and ionic liquid based electrolytes, these capacitance profiles are not fully understood. In this work, we utilize constant voltage molecular dynamics simulations to compute differential capacitance profiles of ionic liquids [BMIm + ][BF 4 - ] and [BMIm + ][TFSI - ] mixed with acetonitrile and 1,2-dichloroethane, at model graphene electrodes. We find that both pure and 10% mole fraction ionic liquid electrolytes exhibit camel shaped capacitance profiles with two peaks on either side of a minimum centered at the potential of zero charge. This profile shape results from electric field induced rearrangement of ion structure within the inner layer closest to the electrode interface. At low potential, the ionic liquid inner layer is concentrated with non-polar tri-fluoromethyl and butyl functional groups of the anions and cations, corresponding to the minimum of the capacitance profiles. With increasing voltage, electrostatic interactions of polar/charged functional groups with the electrode surface compete with these non-polar interactions, leading to ion rearrangement that increases the inner layer charge density and results in higher capacitance. After the ion restructuring is complete, the response saturates and capacitance diminishes. The presence of organic solvent significantly changes the composition of the inner layer. For example, strong non-polar interactions between dichloroethane molecules and the graphene surface substantially block ion/electrode contact at moderate potentials. Overall, our simulations highlight the dynamic nature of the inner region of organic electrolyte double layers, and the sensitive dependence on electrolyte composition and applied voltage.

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

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

active distribution network↗
DOE OSTI2024

Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers

Glass transition temperature (T g ) is important for understanding the physical and mechanical properties of a polymer material because it relates to the thermal energy required to transition between a hard glassy state and a soft rubbery one. Over the years, various models have been developed for predicting this thermal property from molecular structure to aid in designing novel polymers in selected classes. This work builds on those efforts by utilizing both machine learning (ML) and quantum chemistry (QC) techniques to develop models that can predict T g values from the molecular structure under different data availability scenarios and for a wide variety of polymer types. For the ML model, a graph convolutional network (GCN) was used to map topological polymer features; this model was trained against a dataset of more than 7500 T g values and resulted in a root mean square error (RMSE) of 38.1 °C. The QC-based regression model was trained on 83 T g values and produced an RMSE of 34.5 °C. In conclusion, this work demonstrated that while both model techniques produce accurate predictions and are suitable for different data availability scenarios, the QC-based regression model offered a more interpretable model framework with significantly less training data.

36 MATERIALS SCIENCE↗

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