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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2004

Development of DMBZ-15 High-Glass-Transition-Temperature Polyimides as PMR-15 Replacements Given R&D 100 Award

PMR-15, a high-temperature polyimide developed in the mid-1970s at the NASA Lewis Research Center,1 offers the combination of low cost, easy processing, and good high-temperature performance and stability. It has been recognized as the leading polymer matrix resin for carbon-fiber-reinforced composites used in aircraft engine components. The state-of-the-art PMR-15 polyimide composite has a glass-transition temperature (Tg) of 348 C (658 F). Since composite materials must be used at temperatures well below their glass-transition temperature, the long-term use temperatures of PMR-15 composites can be no higher than 288 C (550 F). In addition, PMR-15 is made from methylene dianiline (MDA), a known liver toxin. Concerns about the safety of workers exposed to MDA during the fabrication of PMR-15 components and about the environmental impact of PMR-15 waste disposal have led to the industry-wide implementation of special handling procedures to minimize the health risks associated with this material. These procedures have increased manufacturing and maintenance costs significantly and have limited the use of PMR-15 in commercial aircraft engine components.

Chuang, Kathy↗
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↗
NASA NTRS1977

Inverse isotope effect and the x dependence of the superconducting transition temperature in PdHx and PdDx

First-principles electron-band-structure calculations and measured phonon properties are employed in a calculation of the concentration dependence of the superconducting transition temperatures for PdHx and PdDx, including the inverse isotope effect. Excellent agreement with the experimental results is obtained. Our model also gives a qualitative understanding of the enhanced transition temperatures in the palladium-noble-metal hydrides.

Klein, B. M.↗
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.↗
NASA NTRS2002

Determination of Material Properties Near the Glass Transition Temperature for an Isogrid Boom

Experiments were performed and results obtained to determine the temperature dependence of the modulus of elasticity for a thermoplastic isogrid tube. The isogrid tube was subjected to axial tensile loads of 0-100 lbf and strain was measured at room and elevated temperatures of 100, 120, 140, 160, 180, 190, and 200 F. These were based on tube manufacturer specifying an incorrect glass transition temperature of 210 F. Two protocols were used. For the first protocol the tube was brought to temperature and a tensile test performed. The tube was allowed to cool between tests. For the second protocol the tube was ramped to the desired test temperature and held. A tensile test was performed and the tube temperature ramped to the next test temperature. The second protocol spanned the entire test range. The strain rate was constant at 0.008 in/min. Room temperature tests resulted in the determination of an average modulus of 2.34 x 106 Psi. The modulus decreased above 100 F. At 140 F the modulus had decreased by 7.26%. The two test protocols showed good agreement below 160 F. At this point the glass transition temperature had been exceeded. The two protocols were not repeated because the tube failed.

Blandino, Joseph R.↗
NASA NTRS1991

Microwave response of high transition temperature superconducting thin films

We have studied the microwave response of YBa2Cu3O(7 - delta), Bi-Sr-Ca-Cu-O, and Tl-Ba-Ca-Cu-O high transition temperature superconducting (HTS) thin films by performing power transmission measurements. These measurements were carried out in the temperature range of 300 K to 20 K and at frequencies within the range of 30 to 40 GHz. Through these measurements we have determined the magnetic penetration depth (lambda), the complex conductivity (sigma(sup *) = sigma(sub 1) - j sigma(sub 2)) and the surface resistance (R(sub s)). An estimate of the intrinsic penetration depth (lambda approx. 121 nm) for the YBa2Cu3O(7 - delta) HTS has been obtained from the film thickness dependence of lambda. This value compares favorably with the best values reported so far (approx. 140 nm) in single crystals and high quality c-axis oriented thin films. Furthermore, it was observed that our technique is sensitive to the intrinsic anisotropy of lambda in this superconductor. Values of lambda are also reported for Bi-based and Tl-based thin films. We observed that for the three types of superconductors, both sigma(sub 1) and sigma(sub 2) increased when cooling the films below their transition temperature. The measured R(sub s) are in good agreement with other R(sub S) values obtained using resonant activity techniques if we assume a quadratic frequency dependence. Our analysis shows that, of the three types of HTS films studied, the YBa2Cu3O(7 - delta) thin film, deposited by laser ablation and off-axis magnetron sputtering are the most promising for microwave applications.

Miranda, Felix Antonio↗
NASA NTRS1997

On-Orbit Measurement of the Superconductive Transition Temperatures of YBa2Cu3O(7-x) Thick Films

Thick film superconductors were integrated into hybrid circuits and tested in the Materials In Devices As Superconductors (MIDAS) spaceflight experiment which operated autonomously aboard the MIR space station for 90 days. MIDAS was designed to cool the circuits from 300 to 75K, maintain the temperature at 75K for 28 days, and warm the circuits back to 300K. This cycle was performed a total of three times, during which the superconductive transition temperature was measured during each cool-down and warm-up portion of the experiment. All of the thick films used in this experiment exhibited superconductive transition temperatures of approximately 87K, and no significant differences in the resistance versus temperature properties of the materials were observed among the data collected during pre-flight, flight, and post-flight operations.

Hooker, Matthew W.↗
NASA NTRSDate not supplied

A Machine Learning Approach to Predict Martensitic Transition Temperatures for Shape Memory Alloys

Shape memory alloys (SMAs) are a unique class of materials with several remarkable properties including shape recovery, superelasticity, etc. Especially important for many NASA applications is the ability to tune the martensitic phase transition temperature by varying the alloy composition. Nickel-titanium (NiTi) based alloys are the most widely studied of this class, with compositions involving ternary, quaternary, or higher additions being considered. Over the past several years, a significant database of SMA properties has been assembled by NASA researchers. Such a database is ideal for data science-based approaches including machine learning. We present results from a developed machine learning model capable of accurately predicting the transition temperature of SMAs across a wide range of compositions. Our model has the added benefit of interpretability and even provides confidence intervals for our predictions. This model will make rapid screening and design of new SMA materials possible. Predictions from the machine learning model can be validated by empirical and/or atomistic scale modeling.

Shreyas Honrao↗
DOE OSTI2020

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.

36 MATERIALS SCIENCE↗
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↗