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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
NASA NTRS1991

Dust and molecular properties of the low-opacity cloud Lynds 1563

Optical, molecular, and far-infrared data are analyzed for L1563, estimated peak Ab 2.5 mag. The cloud is detected by IRAS at 12, 25, 60, and 100 microns, and with CO, (C-13)O, and H2CO molecules. A column density comparison yields an estimate of the temperature of the classical dust grains of 15.6 + or - 1 K, while the color temperature derived from the ratio I(60)/I(100) is 26 K. Both dust and color temperatures decrease toward the cloud center.

Clark, Frank O.↗
NASA NTRS1997

Bromine Dioxide, OBrO: The Rotational Spectrum and Molecular Properties

The spectra are well descrie by Hamiltonian which included centrifugal distortion efects for fine and hyperfine terms. The molecular structure and the harmonic force field have been derived, and they as well as fine and hyperfine structure constants, are compared with data of related molecules and ESR data from OBrO isolated in cryogenic salt matrices.

Bromine Dioxide Rotational Spectrum↗
DOE OSTI2024

Relating Molecular Properties to the Persistence of Marine Dissolved Organic Matter with Liquid Chromatography–Ultrahigh-Resolution Mass Spectrometry

Marine dissolved organic matter (DOM) contains a complex mixture of small molecules that eludes rapid biological degradation. Spatial and temporal variations in the abundance of DOM reflect the existence of fractions that are removed from the ocean over different time scales, ranging from seconds to millennia. However, it remains unknown whether the intrinsic chemical properties of these organic components relate to their persistence. Here, we elucidate and compare the molecular compositions of distinct DOM fractions with different lability along a water column in the North Atlantic Gyre. Our analysis utilized ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry at 21 T coupled to liquid chromatography and a novel data pipeline developed in CoreMS that generates molecular formula assignments and metrics of isomeric complexity. Clustering analysis binned 14 857 distinct molecular components into groups that correspond to the depth distribution of semilabile, semirefractory, and refractory fractions of DOM. The more labile fractions were concentrated near the ocean surface and contained more aliphatic, hydrophobic, and reduced molecules than the refractory fraction, which occurred uniformly throughout the water column. These findings suggest that processes that selectively remove hydrophobic compounds, such as aggregation and particle sorption, contribute to variable removal rates of marine DOM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessing the effect of regularization on the molecular properties predicted by SCAN and self-interaction corrected SCAN meta-GGA

Recent regularization of the SCAN meta-GGA functional (rSCAN) has simplified the numerical complexities of the SCAN functional, alleviating SCAN's stringent demand on the numerical integration grids to some extent. The regularization of rSCAN, however, results in the breaking of some constraints such as the uniform electron gas limit, the slowly varying density limit, and coordinate scaling of the iso-orbital indicator. Here, we assess the effects of regularization on the electronic, structural, vibrational, and magnetic properties of molecules by comparing the SCAN and rSCAN predictions. The properties studied include atomic energies, atomization energies, ionization potentials, electron affinities, barrier heights, infrared intensities, dissociation and reaction energies, spin moments of molecular magnets, and isomer ordering of water clusters. Our results show that rSCAN requires less dense numerical grids and gives very similar results to those of SCAN for all properties examined with the exception of atomization energies, which are worsened in rSCAN. We also examine the performance of self-interaction-corrected (SIC) rSCAN with respect to SIC-SCAN using the Perdew–Zunger (PZ) SIC method. The PZSIC method uses orbital densities to compute one-electron self-interaction errors and places an even more stringent demand on numerical grids. Our results show that SIC-rSCAN gives marginally better performance than SIC-SCAN for almost all properties studied in this work with numerical grids that are on average half or less as dense as that needed for SIC-SCAN.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗
NASA NTRS1990

Studies of molecular properties of polymeric materials

Aerospace environment effects (high energy electrons, thermal cycling, atomic oxygen, and aircraft fluids) on polymeric and composite materials considered for structural use in spacecraft and advanced aircraft are examined. These materials include Mylar, Ultem, and Kapton. In addition to providing information on the behavior of the materials, attempts are made to relate the measurements to the molecular processes occurring in the material. A summary and overview of the technical aspects are given along with a list of the papers that resulted from the studies. The actual papers are included in the appendices and a glossary of technical terms and definitions is included in the front matter.

Harries, W. L.↗
DOE OSTI2026

YAP1 Dysfunction Promotes Molecular Properties Linked to Breast Cancer Susceptibility

YAP1 is a cotranscription factor that promotes malignant and stem cell properties in cancer. We previously found that YAP1 dysregulation is associated with aging in human mammary epithelia. With increased age, YAP1 expression changes in luminal epithelial cells, the prospective breast cancer cell of origin. Because age is a significant risk factor for breast cancer, we tested whether YAP1 dysregulation acted early in cancer progression by conferring cellular states associated with increased cancer susceptibility. In this study, we find that with increased age and genetic risk for developing cancer, human breast tissues showed significantly increased YAP1 expression, and cultured primary human mammary epithelial cells (HMEC) showed significantly increased expression of both YAP1 and its transcriptional targets. Increased YAP1 expression in cultured HMEC induced gene expression changes associated with increased cancer susceptibility, such as genes associated with stem cell states, increased telomerase activity, breast cancer progression, and increased age and genetic breast cancer risk. Furthermore, overexpression of YAP1 in post-stasis HMEC—finite lifespan cells that have bypassed a retinoblastoma-mediated senescence barrier—promoted properties related to increased growth potential. We found that YAP1 dysregulation in finite epithelial cells allows for access to gene programs and functions that are typically thought to be restricted to stem cells. We hypothesize that YAP1 acts early in breast cancer progression, long before the development of a tumor, to impose cancer-susceptible molecular states.

YAP1↗
DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

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

Molecular Properties of Thorium Hydrides: Electron Affinities and Thermochemistry

In this work, high-level electronic structure calculations of the ground and low-lying energy electronic states for ThH x and ThH x – for x = 2–5 are reported and compared to available anion photoelectron detachment experiments. The adiabatic electron affinities (EAs) are predicted to be 0.82, 0.88, 0.51, and 2.36 eV for x = 2 to 5, respectively, at the Feller– Peterson–Dixon (FPD) level. The vertical detachment energies (VDEs) are predicted to be 0.84, 0.88, 0.81, and 4.38 eV for x = 2–5, respectively. The corresponding experimental VDEs are 0.871 eV for x = 2, 0.88 eV for x = 3, and 4.09 eV for x = 5. As for ThH, there is a significant spin–orbit (SO) correction for the EA of ThH 2 , and this correction decreases substantially for x > 2. The observed ThH 2 – photoelectron spectrum has many transitions as predicted at the CASPT2-SO level. The FPD bond dissociation energies (BDEs) increase from 67 to 75 kcal/mol for x = 2 to x = 4 at the FPD level. The BDE for ThH 5 is much lower as it is a complex of H 2 with ThH 3 . The hydride affinities for x = 2 to 4 are all comparable and near 70 kcal/mol. A natural bond orbital analysis is consistent with a significant Th + –H– ionic contribution to the Th–H bonds. There is very little participation of the 5f orbitals in the bonding and the valence electrons on the Th are dominated by 7s and 6d for the neutrals and anions except for ThH 2 – where there is a significant contribution from the 7p.

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

Improved Light Extraction by Engineering Molecular Properties of Square Planar Phosphorescent Emissive Materials

The ultimate objective of this project was to develop a cost-effective technology which could significantly improve the light outcoupling efficiency up to 70% and higher and the key tasks were listed as follows: (i) improving light extraction efficiency through a better control of horizontally aligned emitting dipoles of square planar phosphorescent emitters; (ii) fabricating high-efficiency monochromic and amber OLED in the device settings; (iii) fabricating high-efficiency white OLED in the device settings. With both materials innovation and device structure optimization, we realized OLEDs with an EQE of 66% with a LT 95 lifetime of over 100 khrs at a brightness of 1,000 cd/m 2 . This proposed research is a high impact project which could potentially revolutionize OLED lighting technology and expedite the commercialization process of OLED technology for solid state lighting. This project outcomes contributed to meet the targeted performance of organic solid state lighting set in the DOE MYPP.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Constraints on the molecular properties of interstellar X-ogen derived from radio observations

Twenty-eight X-ogen emission sources at 89.189 GHz have been detected and observed. Twenty of these are new X-ogen sources which also contain other interstellar molecules. No infrared stars are found to be X-ogen sources. A new rest frequency (89,188.65 MHz) is derived for X-ogen. It is shown that X-ogen is not SiC, C2H, or any molecule with spin-doubling, hyperfine structure, or both.

Hollis, J. M.↗