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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine learning the relationship between Debye temperature and superconducting transition temperature

Recently a relationship between the Debye temperature $Θ_D$ and the superconducting transition temperature $T_c$ of conventional superconductors has been proposed [Esterlis et al., npj Quantum Mater. 3, 59 (2018)]. The relationship indicates that $T_c$ ≤ $AΘ_D$ for phonon-mediated BCS superconductors, with $A$ being a prefactor of order ~ $0.1$. In order to verify this bound, we train machine learning (ML) models with 10 330 samples in the Materials Project database to predict $Θ_D$. Here, by applying our ML models to 9860 known superconductors in the NIMS SuperCon database, we find that the conventional superconductors in the database indeed follow the proposed bound. We also perform first-principles phonon calculations for $\mathrm{H_3S}$ and $\mathrm{LaH_{10}}$ at 200 GPa. The calculation results indicate that these high-pressure hydrides essentially saturate the bound of $T_c$ versus $Θ_D$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

High temperature transition aluminas in gamma-Al2O3: Review

High temperature treated transition aluminas display adsorptive and catalytic properties that are in many ways comparable to those of their low temperature counterpart gamma-Al2O3. While being important industrial catalysts as well as catalytic supports, their very basic crystallographic and structural characteristics remain poorly understood and thus actively studied. In this review, we critically examine the crystallography and structural complexity of these materials. Specifically, we address the crystallography of delta- and theta-Al2O3 polymorphs and show how structural intergrowth and disorder are accommodated in these phases. The structural complexity at the scale of overall microstructure is also examined, and the challenges and recent progress in quantification of the structure at ensemble level are discussed. Most pertinently to catalysis, we review the surfaces properties of high temperature treated Al2O3 and discuss the implications for understanding attributes relevant to heterogeneous catalysis.

Kovarik, Libor↗
DOE OSTI2021

Dynamic tunability of phase-change material transition temperatures using ions for thermal energy storage

Thermal energy storage (TES) based on phase-change materials (PCMs) has many current and potential applications, such as climate control in buildings, thermal management for batteries and electronics, thermal textiles, and transportation of pharmaceuticals. Despite its promise, the adoption of TES has been limited, in part due to limited tunability of the transition temperature, which hinders TES performance for varying use temperatures. Transition temperature tuning of a material using an external stimulus, such as pressure or an electric field, typically requires very large stimuli. To circumvent this problem, here, we report on the dynamic transition temperature tunability of a PCM using ions. We achieve a transition temperature tunability up to 6°C in polyethylene glycol (PEG) by using the salt lithium oxalatodifluoroborate at a low voltage of 2.5 V, which may enable simpler and safer devices/system designs. We also explain the thermal properties of the salt/PCM solution using the Flory-Huggins theory.

25 ENERGY STORAGE↗
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↗
DOE OSTI2020

Quantification of High-Temperature Transition Al2O3 and Their Phase Transformations

High temperature exposure of ?-Al2O3 can lead to a series of polymorphic transformations, including the formation of ?-Al2O3 and ?-Al2O3. Quantification of the microstructure in the ?/?-Al2O3 formation range represents a formidable challenge as both phases accommodate a high degree of structural disorder. In this work, we explore the use of XRD recursive stacking formalism for quantification of high temperature transition aluminas. We formulate the recursive stacking methodology for modelling of disorder in ?-Al2O3 and twinning in ?-Al2O3 and show that explicitly accounting for the disorder is necessary to reliably model the XRD patterns of high temperature transition alumina. In the second part, we use the recursive stacking approach to study phase transformation during high temperature (1050 ºC) treatment. We show that the two different intergrowth modes of ?-Al2O3 have different transformation characteristics, and that a significant portion of ?-Al2O3 is stabilized with ?-Al2O3 even after prolonged high-temperature exposures. In discussions, we outline the limitation of the current XRD approach and discuss a possible multimodal XRD and NMR approach which can improve analysis of complex transition aluminas. This work was performed in the Wiley Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by DOEs Office of Biological and Environmental Research and located at PNNL. The work was supported by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences.

Kovarik, Libor↗
DOE OSTI2023

Thermochromic Halide Perovskite Windows with Ideal Transition Temperatures

Abstract Urban centers across the globe are responsible for a significant fraction of energy consumption and CO 2 emission. As urban centers continue to grow, the popularity of glass as cladding material in urban buildings is an alarming trend. Dynamic windows reduce heating and cooling loads in buildings by passive heating in cold seasons and mitigating solar heat gain in hot seasons. Here, reduced energy consumption in highly glazed buildings in a mesoscopic building energy model is demonstrated when thermochromic windows are employed. Savings are realized across eight disparate climate zones of the United States. The model is used to determine ideal critical transition temperatures of 20–27.5 °C for thermochromic windows based on metal halide perovskite materials. Ideal transition temperatures are realized experimentally in composite metal halide perovskite films composed of perovskite crystals and an adjacent reservoir phase. The transition temperature is controlled by cointercalating methanol, instead of water, with methylammonium iodide and tailoring the hydrogen‐bonding chemistry of the reservoir phase. Thermochromic windows based on metal halide perovskites represent a clear opportunity to mitigate the effects of energy‐hungry buildings.

14 SOLAR ENERGY↗
DOE OSTI2022

Machine learning prediction of glass transition temperature of conjugated polymers from chemical structure

Predicting the glass transition temperature (T g ) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict T g of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 T g data collected, an ML model is developed by taking simplified “geometry” of six chemical building blocks as molecular features, where side-chain fraction, isolated rings, fused rings, and bridged rings features are identified as the dominant ones for T g . MD simulations further unravel the fundamental roles of those chemical building blocks in dynamical heterogeneity and local mobility of CPs at a molecular level. The developed ML model is demonstrated for its capability of predicting T g of several new high-performance solar cell materials to a good approximation. The established predictive framework facilitates the design and prediction of T g of complex CPs, paving the way for addressing device stability issues that have hampered the field from developing stable organic electronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes

Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. Here, we leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T 1/2 ) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T 1/2 values. We then compare ML T 1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T 1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T 1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T 1/2 values with a Pearson’s r of 0.82. In contrast, DFA-predicted T 1/2 values have large errors and show no correlation to estimated T 1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

quantitative structure–property relationship (QSPR↗
DOE OSTI2021

Digital Tuning of the Transition Temperature of Epitaxial VO 2 Thin Films on MgF 2 Substrates by Strain Engineering

Abstract Straining the vanadium dimers along the rutile c ‐axis can be used to tune the metal‐to‐insulator transition (MIT) of VO 2 but has thus far been limited to TiO 2 substrates. In this work VO 2 /MgF 2 epitaxial films are grown via molecular beam epitaxy (MBE) to strain engineer the transition temperature ( T MIT ). First, growth parameters are optimized by varying the synthesis temperature of the MgF 2 (001) substrate ( T S ) using a combination of X‐ray diffraction techniques, temperature dependent transport, and soft X‐ray photoelectron spectroscopy. It is determined that T S values greater than 350 °C induce Mg and F interdiffusion and ultimately the relaxation of the VO 2 layer. Using the optimized growth temperature, VO 2 /MgF 2 (101) and (110) films are then synthesized. The three film orientations display MITs with transition temperatures in the range of 15–60 °C through precise strain engineering.

Howard, Sebastian A.↗
DOE OSTI2021

Solvent depression of transition temperature to selectively stimulate actuation of shape memory polymer foams

An embodiment of the invention is a shape memory polymer (SMP) foam designed to recover its original shape through exposure to a solvent. Thermo-responsive SMPs are polymers designed to maintain a programmed secondary shape until heated above their transition temperature, upon which the polymer recovers its original, or primary, shape. The thermo-responsive SMP foam is programmed to its secondary shape prior to use, typically compression of the foam to a small volume, and remains in this programmed shape until exposed to a selected solvent such as dimethyl sulfoxide or ethyl alcohol. Upon exposure to the solvent, the transition temperature of the SMP foam decreases below the temperature of the environment and the SMP foam actuates to its primary shape. The SMP foam is tailored to actuate upon exposure to specific solvents while minimizing or preventing actuation when exposed to water or other solvents. This selective solvent actuation can be used to increase working time of a SMP foam device, that is, the time allowed for use of a device without undesired actuation, while maintaining functional SMP actuation. Solvent actuated SMP foams can be used in various applications including, but not limited to, treatment of aneurysms and arterio-venous malformations, tissue engineering, and wound healing.

Boyle, Anthony↗
DOE OSTI2024

Investigation of experimental signatures of spin glass transition temperature

We present a series of temperature and field-dependent magnetization studies of large single-crystal spin glass samples, focusing on both field-cooled (FC) and zero-field-cooled (ZFC) magnetization studies, as well as ac susceptibility measurements. Using the above experimental techniques we aim to understand the nature of spin glass transition in presence of a field, a key factor in understanding the properties of these systems. Building on previous studies that have explored magnetic signatures indicative of spin glass transitions, our research employs a systematic approach to refine the identification of this transition temperature. Through static and dynamic measurements, we aim to shed light on the open issues regarding the key markers of spin glass transitions, enhancing our understanding of these complex systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Artificial High-Transition Temperature Superconductors

In this work, we have used the well-understood quantum Hall (QH) stripes in high quality two-dimensional electron gases to mimic charge stripes in high transition temperature (Tc) superconductors. The science question we want to address is “Can QH stripes mimic high Tc superconductor stripes and provide a controlled experimental setup to pin-down the role of stripes in high Tc superconductivity?”. We have observed anomalous superconducting transition like behavior in GaAs double quantum well systems (DQWs) when each quantum well (QW) is tuned to the charge stripe states but with different Landau level fillings. Furthermore, we have shown that the transition like behavior is sharper in the DQWs when the two QWs are more strongly coupled. Our results suggest, for the first time, experimental evidence of the paired charge stripes model, which might lead to room-temperature superconductors that have enormously wide applications in computing, energy, and transportation industries. Advancing the science of high transition temperature superconductivity will have a profound impact in advancing energy technologies, ranging from the next generation microchips, new energy transfer grid to public transportation, and thus is important to nation’s energy security and relevant across the landscape of many mission spaces. Sandia has been a leader in materials science research and development. The proposed research takes advantage of Sandia’s state-ofthe-art MBE facilities at the Center for Integrated Nanotechnologies (CINT) and utilizes Sandia’s extensive advanced materials characterization resources. We envision a significant impact on the nation’s energy research and security challenges by investing in this research.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tuning the Superconducting Transition Temperature of Co-sputtered Iridium and Platinum Films

A superconducting film with a tunable low transition temperature (Tc) is required in high-resolution Transition-Edge Sensor (TES) detectors, which have applications including dark matter detection, low threshold coherent elastic neutrino nucleus scattering measurement, and X-ray spectroscopy. We have been investigating a new approach to tune the Tc of superconducting thin films fabricated by co-sputtering Iridium and Platinum. The effects of Pt concentration and deposition parameters on the films' structural, electrical, and superconducting properties have been studied. AFM and XRD techniques and low temperature resistance measurements have been utilized for film characterization. By varying the Pt concentration and deposition parameters when co-sputtering, we have successfully achieved controllable tuning of Tc in the range of 30-200 mK. The experimental results demonstrate co-sputtering as a viable method for controlling the Tc of Ir-based thin films that can be applied to fabricating high-resolution TESs.

Yefremenko, V. G.↗
DOE OSTI2026

A simple Rice-Ashby ductile–brittle transition temperature (DBTT) model based on dislocation mobility for body-centered cubic complex concentrated alloys

A simple Rice-Ashby type model for ductile–brittle transition temperature (DBTT) of body-centered cubic (bcc) complex concentrated alloys (structures) is presented. The effect of accumulation of dislocation density on DBTT is also analyzed. The model results are compared with experimental yield stress vs. temperature data for four complex concentrated alloys: Nb 45 Ta 25 Ti 15 Hf 15 (NTTH), MoNbTaW, HfNbTaTiZr, NbTiZr and two pure bcc metals, Fe and W. It is shown that the DBTT behavior of these alloys and pure metals are in agreement with the simple ductility model presented in this manuscript. The DBTT model presented in this manuscript along with yield strength models for bcc complex concentrated alloys described in the literature should serve as a useful guide for designing such alloys with good high temperature strength and significant room temperature ductility.

Crack tip processes↗
DOE OSTI2025

Translational diffusion in supercooled water at and near the glass transition temperature—136 K

The properties of amorphous solid water at and near the calorimetric glass transition temperature, T g , of 136 K have been debated for years. One hypothesis is that water turns into a “true” liquid at T g (i.e., it becomes ergodic) and exhibits all the characteristics of an ergodic liquid, including translational diffusion. A competing hypothesis is that only rotational motion becomes active at T g , while the “real” glass transition in water is at a considerably higher temperature. To address this dispute, we have investigated the diffusive mixing in nanoscale water films, with thicknesses up to ∼100 nm, using infrared (IR) spectroscopy. The experiments used films that were composed of at least 90% H 2 O with D 2 O making up the balance and were conducted under conditions where H/D exchange was essentially eliminated. Because the IR spectra of multilayer D 2 O films (e.g., thicknesses of ∼3–6 nm) embedded within thick H 2 O films are distinct from the spectrum of isolated D 2 O molecules within H 2 O, the diffusive mixing of (initially) isotopically layered water films could be followed as a function of annealing time and temperature. The results show that water films with total thicknesses ranging from ∼20 to 100 nm diffusively mixed prior to crystallization for temperatures between 120 and 144 K. The translational diffusion had an Arrhenius temperature dependence with an activation energy of 40.8 ± 3.5 kJ/mol, which indicates that water at and near T g is a strong liquid. The measured diffusion coefficient at 136 K is 6.25 ± 1.4 × 10 −21 m 2 /s.

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

Effect of Microstructure on Chain Flexibility and Glass Transition Temperature of Polybenzofulvene

Polybenzofulvene (PBF) is a polydiene with a very high glass transition temperature (T g ), which makes it a potential candidate for use as the hard block for high-temperature thermoplastic elastomer applications. The T g s of polydienes are known to be related to chain flexibility. However, no studies have been reported that correlate the chain flexibility to the microstructure of PBF. Herein, we present a study of solution properties of linear PBFs with narrow molecular weight dispersity and having 1,2-addition ranging from 23% to 99%. The materials were prepared by living anionic polymerization under different conditions. Specifically, the chain flexibility as defined by the Flory's characteristic ratio, , and dependence of chain flexibility on microstructure by combined measurements of intrinsic viscosity and molecular weight using triple-detector size exclusion chromatography (SEC) is studied. The persistence lengths (l p ) and chain diameters (d B ) were also estimated using the touched-bead wormlike chain model. To the best of our knowledge, the characteristic ratio of PBF is found to be the highest of all polydienes that have been reported so far.

36 MATERIALS SCIENCE↗