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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

BACKFLIP: Identification of Materials and Changes Upon Aging of Emerging Fluoropolymer-Free and Industry-Benchmark PV Backsheets

Extending lifetimes of photovoltaic (PV) modules and decreasing manufacturing and decommissioning costs are motivating research of novel PV module backsheet materials. Backsheet integrity is critical to the safety and performance of a PV module, however, the polymeric materials are often vulnerable to environmental degradation. The long-term durability of emerging polyolefin (PO) materials is being examined to inform regarding the possible replacement of laminated backsheets containing polyethylene terephthalate (PET) cores and fluoropolymer protective inner/outer layers. This study compares traditional PET-based backsheets with novel, co-extruded, and recyclable PO-based backsheets after being subjected to hygrometric and UV photolytic accelerated weathering. A large set of characterization methods will compare physical, chemical, structural, mechanical, and insulating properties of these backsheets and the resulting mini-module (MiMo) PV performances. The study of chemical composition, glass- and phase-transition temperatures, degree of crystallinity, and crystalline structure are described here.

mini-module↗
DOE OSTI2022

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington’s Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80–83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70–72% in negative predictivity, and 78–86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Demonstration of an x-ray Raman spectroscopy setup to study warm dense carbon at the high energy density instrument of European XFEL

We present a proof-of-principle study demonstrating x-ray Raman Spectroscopy (XRS) from carbon samples at ambient conditions in conjunction with other common diagnostics to study warm dense matter, performed at the high energy density scientific instrument of the European x-ray Free Electron Laser (European XFEL). We obtain sufficient spectral resolution to identify the local structure and chemical bonding of diamond and graphite samples, using highly annealed pyrolytic graphite spectrometers. Due to the high crystal reflectivity and XFEL brightness, we obtain signal strengths that will enable accurate XRS measurements in upcoming pump–probe experiments with a high repetition-rate, where the samples will be pumped with high-power lasers. Molecular dynamics simulations based on density functional theory together with XRS simulations demonstrate the potential of this technique and show predictions for high-energy-density conditions. Our setup allows simultaneous implementation of several different diagnostic methods to reduce ambiguities in the analysis of the experimental results, which, for warm dense matter, often relies on simplifying model assumptions. The promising capabilities demonstrated here provide unprecedented insights into chemical and structural dynamics in warm dense matter states of light elements, including conditions similar to the interiors of planets, low-mass stars, and other celestial bodies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Shock‐tube spectroscopic water measurements and detailed kinetics modeling of 1‐pentene and 3‐methyl‐1‐butene

Abstract To understand the effects of the chemical structure of two C 5 alkene isomers on their combustion properties, and to highlight the major chemical reactions occurring during their high‐temperature oxidation, water time histories were measured behind reflected shock waves for the oxidation of 1‐pentene (C 5 H 10 ‐1) and 3‐methyl‐1‐butene (3M1B) in 99.5% Ar. The experiments were carried out at three different equivalence ratios ( φ = 0.5, 1.0, and 2.0) at pressures and temperatures ranging from 1.29 to 1.47 atm and 1 331 to 1 877 K, respectively. The H 2 O quantification extends the database for 1‐pentene and provides new insights for 3M1B. These unique results were used to validate and to develop a new detailed kinetics model. Numerical predictions are presented, and the new model was able to capture the results with suitable accuracy, with 3M1B being notably more reactive than C 5 H 10 ‐1. Sensitivity and rate‐of‐production analyses were performed to help explain the results. Under the present conditions, the reactivity is rapidly initiated by molecular dissociation of a fraction of the pentene isomers. The initiation phase then induces H‐atom abstraction by active radicals (H, OH, O, HO 2 , and CH 3 ) to first produce alkenyl C 5 H 9 radicals (or an alkyl radical and an alkenyl radical by breaking a C─C bond) and subsequent, smaller fragments. The difference in terms of reactivity between the isomers is essentially due to the fact that 3M1B has one particularly weak tertiary allylic C─H bond, which allows for fast H‐atom abstraction compared with 1‐pentene.

Grégoire, Claire M.↗
DOE OSTI2021

Contrasting thermally-induced structural and microstructural evolution of alumino-silicates with tubular and planar arrangements: Case study of halloysite and kaolinite

Materials with the similar chemical structures but different morphological organization exhibit considerable differences in thermally induced chemo-morphological evolution. With recent advancements in multi-scale X-ray scattering measurements, it is now possible to non-invasively probe the structural and microstructural evolution of such materials. In this study, we investigate the chemo-morphological evolution of halloysite and contrast the results with those for kaolinite. Halloysite and kaolinite have tubular and planar morphologies, respectively. Both materials are alumino-silicates comprising silica tetrahedra (T) and alumina octahedra (O) organized in a TO structure. Four distinct stages in the structural evolution were identified. Stages I, II, III, and IV correspond to temperature ranges of 25 to 125 °C, 125 to 400 °C, 400 to 625 °C, and 625 to 875 °C. Major structural changes correspond to the removal of interlayer/adsorbed water in stage I, the existence of the halloysite structure without interlayer water in stage II, dehydroxylation of halloysite in stage III, and the conversion of ordered halloysite to amorphous meta-halloysite in stage IV. Additionally, heating halloysite up to 875 °C resulted in slight widening of the nanotubes, as the average pore radius increased from 6.4 nm to 6.6 nm. Heating also resulted in an increase of wall thickness of the nanotubes from ~120 nm (25 °C) to 161 nm (875 °C). The increase in the halloysite nanotube diameter was attributed to the expansion of the structure and an increase in the surface roughness. The pore sizes in halloysite nanotubes were also confirmed using N 2 adsorption-desorption and nano-X-ray computed tomography (nano-XCT) measurements. The interlayer basal spacing in halloysite changed from 9.8 Å to 7.2 Å after the removal of interlayer water. At temperatures in the range of 625 to 875 °C, heating halloysite causes a small widening of nanotube pores and a minor increase in the surface area. In contrast, the interlayer spacing in kaolinite collapses on heating which reduces the nanoscale porosity. Furthermore, these studies demonstrate the differences in the chemo-morphological evolution of alumino-silicates with tubular and planar morphologies.

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

Synthesis and optical properties of phosphorus doped ZnO: X-ray absorption, X-ray emission, and X-ray excited optical luminescence studies

Abstract Over the past decades, zinc oxide semiconductor and its derivatives have been extensively developed because of its optoelectronic properties. Since ZnO is an intrinsic n-type semiconductor, how to synthesize high-quality p-type ZnO semiconductors and investigate their optoelectronic properties and local chemical structures are important and necessary. In this article, two studies of synthesizing both undoped and phosphorus-doped ZnO nanostructures by hydrothermal method and chemical vapor deposition (CVD) technique are presented. Scanning electron microscopy (SEM) and laboratory X-ray diffraction (XRD) are used to track the surface morphology and the crystalline structure of both undoped and phosphorus doped ZnO nanostructures, respectively. X-ray absorption near edge structures (XANES), X-ray emission spectroscopy (XES) and X-ray excited optical luminescence (XEOL) are also used to determine the local chemical information of both undoped and phosphorus-doped ZnO nano/microstructures such as local symmetry and optical properties. It is found that the phosphorus is successfully doped into the surface of ZnO and substituted the Zn. The phosphorus-doped ZnO products have better crystallinity and less oxygen vacancies on the surface. These two are correlated as one leads to the other. The XEOL results suggest that the average bandgap for undoped and phosphorus doped ZnO is the same, 3.24 eV. The second derivative of the absorption-emission spectra yields the fundamental bandgap for undoped ZnO and phosphorus-doped ZnO is 3.25 eV. The implication of this observation is discussed.

Chemistry↗
DOE OSTI2022

Architector

Architector is a 3D chemical structure generation software package designed to take minimal 2D information about ligands and metal centers and generates chemically sensible 3D conformers and stereochemistry of the organometallic compounds. Architector is capable of high-throughput in-silico construction of s-, p-, d-, and f-block organometallic complexes. Architector represents a transformative step towards cross-periodic table computational design of metal complex chemistry.

Yang, Ping↗
DOE OSTI2025

Random heteropolymers as enzyme mimics

Despite successes in replicating the primary–secondary–tertiary structure hierarchy of protein, it remains elusive to synthetically materialize protein functions that are deeply rooted in their chemical, structural and dynamic heterogeneities. We propose that for polymers with backbone chemistries different from that of proteins, programming spatial and temporal projections of sidechains at the segmental level can be effective in replicating protein behaviours; and leveraging the rotational freedom of polymer can mitigate deficiencies in monomeric sequence specificity and achieve behaviour uniformity at the ensemble level. Here, guided by the active site analysis of about 1,300 metalloproteins, we design random heteropolymers (RHPs) as enzyme mimics based on one-pot synthesis. We introduce key monomers as the equivalents of the functional residues of protein and statistically modulate the chemical characteristics of key monomer-containing segments, such as segmental hydrophobicity. The resultant RHPs form pseudo-active sites that provide key monomers with protein-like microenvironments, co-localize substrates with catalytic or cofactor-binding sidechains and catalyse reactions such as oxidation and cyclization of citronellal with isopulegol/menthoglycol selectivity. This RHP design led to enzyme-like materials that can retain catalytic activity under non-biological conditions, are compatible with scalable processing and have expanded substrate scope, including environmentally long-lasting antibiotic tetracycline.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Prediction of impact sensitivity, heat of formation and heat of explosion using atomic connectivity

In these proceedings we revisit a large collection of explosives and explosive descriptors with the goal of predicting impact sensitivity using only local atomic environments that can be deciphered from molecular SMILES strings as descriptors without utilizing empirically measured values or computationally expensive electronic structure calculations. From the original database of nearly 500 descriptors, removing empirically measured and electronic structure values decreased the number of descriptors to 135, which we reduced to 18 the most important descriptors using Random Forests. The condensed model predicted impact sensitivity with essentially the same accuracy as the existing, more complex model (R 2 = 0.788 and RSME = 0.312), while remaining applicable to all types of explosives (Peroxides, azides, C-Nitros, Nitroamines, Nitrate Esters, etc.). In addition to impact sensitivity, we proposed similar models to accurately predict values heat of formation (ΔH f ) and heat of explosion (Q), with R 2 = 0.966 and 0.916, respectively. In conclusion, the work in these proceedings allows for prediction of explosive performance and sensitivity with only chemical structure information and an estimate of density.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Optimized structure and electronic band gap of monolayer GeSe from quantum Monte Carlo methods

Here, we have used highly accurate quantum Monte Carlo methods to determine the chemical structure and electronic band gaps of monolayer GeSe. Two-dimensional (2D) monolayer GeSe has received a great deal of attention due to its unique thermoelectric, electronic, and optoelectronic properties with a wide range of potential applications. Density functional theory (DFT) methods have usually been applied to obtain optical and structural properties of bulk and 2D GeSe. For the monolayer, DFT typically yields a larger band-gap energy than for bulk GeSe but cannot conclusively determine if the monolayer has a direct or indirect gap. Moreover, the DFT-optimized lattice parameters and atomic coordinates for monolayer GeSe depend strongly on the choice of approximation for the exchange-correlation functional, which makes the ideal structure-and its electronic properties-unclear. In order to obtain accurate lattice parameters and atomic coordinates for the monolayer, we use a surrogate Hessian-based parallel line search within diffusion Monte Carlo to fully optimize the GeSe monolayer structure. The DMC-optimized structure is different from those obtained using DFT, as are calculated band gaps. The potential energy surface has a shallow minimum at the optimal structure. This, combined with the sensitivity of the electronic structure to strain, suggests that the optical properties of monolayer GeSe are highly tunable by strain.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modeling Interactions within and between Peptide Amphiphile Supramolecular Filaments

Many peptides are able to self-assemble into one-dimensional (1D) nanostructures, such as cylindrical fibers or ribbons of variable widths, but the relationship between the morphology of 1D objects and their molecular structure is not well understood. Here, we use coarse-grained molecular dynamics (CG-MD) simulations to study the nanostructures formed by self-assembly of different peptide amphiphiles (PAs). The results show that ribbons are hierarchical superstructures formed by laterally assembled cylindrical fibers. Simulations starting from bilayer structures demonstrate the formation of filaments, whereas other simulations starting from filaments indicate varying degrees of interaction among them depending on chemical structure. These interactions are verified by observations using atomic force microscopy of the various systems. The interfilament interactions are predicted to be strongest in supramolecular assemblies that display hydrophilic groups on their surfaces, while those with hydrophobic ones are predicted to interact more weakly as confirmed by viscosity measurements. The simulations also suggest that peptide amphiphiles with hydrophobic termini bend to reduce their interfacial energy with water, which may explain why these systems do not collapse into superstructures of bundled filaments. The simulations suggest that future experiments will need to address mechanistic questions about the self-assembly of these systems into hierarchical structures, namely, the preformation of interactive filaments vs equilibration of large assemblies into superstructures.

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

Adaptive language model training for molecular design

Abstract The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks.

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

Editorial: Lignocellulose valorization: Fractionation, conversion and applications

Lignocellulosic biomass is the most abundant form of renewable feedstock on the Earth. Chemicals, energies, and materials derived from lignocellulosic biomass are renewable and sustainable, and have the potential to replace fossil feedstocks. Biorefinery, a process to fractionate lignocellulose into the three major components, is considered the most pivotal step for bioenergy, biomaterials and biochemicals. However, due to the complex hierarchy and chemical structures, only limited lignocellulose is valorized into value-added products, and most of them are burnt or just discarded. Therefore, there is a need to investigate the heterogeneous structure of lignocellulose, improve its efficient fractionation into cellulose, hemicelluloses and lignin, and develop the technology that can convert the lignocellulose into high-value chemical products and high-performance functional materials in a sustainable and promising configuration. In short, the ultimate goal of biomass utilization is that the production of biofuels and biomaterials in industrial applications must be cost-, and performance-competitive with petroleum-derived equivalents.

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

Machine learning prediction on the fractional free volume of polymer membranes

Fractional free volume (FFV) characterizes the microstructural level features of polymers and affects their properties including thermal, mechanical, and separation performance. Experimental measurements and theoretical analyses have been used to quantify the FFV of polymers, but challenges remain because of their limitations. Experimental measurements are laborious and based on semi empirical equations, while Bondi’s group contribution theory involves ambiguities like the determination of van der Waals volume and the choice of factor values in the theoretical equation. To efficiently evaluate the FFV of polymers, this study utilizes high-throughput molecular dynamics (MD) simulations to build a large dataset regarding polymer’s FFV. Based on this large dataset, we further build machine learning (ML) models to establish the composition-structure relation. Inspired by group contribution theory which correlates polymer’s functional groups to FFV, our ML models correlate polymer’s substructures or physico-chemical indexes to FFV. Here, our study first benchmarks the MD simulation protocol to obtain reliable FFV of polymers and then carries out high-throughput MD simulations for more than 6,500 homopolymers and 1,400 polyamides. Such a large and diverse dataset makes the well-trained ML models more generalizable, compared with the group contribution theory. The efficiency of a feed forward neural network model is further demonstrated by applying it to a hypothetical polyimide dataset of more than 8 million chemical structures. The predicted FFVs of hypothetical polyimides are further validated by MD simulations. The obtained FFVs of the 8 million polymers, plus their previously reported gas separation performances, demonstrate the promising capability of ML virtual screening for the discovery of polymer membranes with exceptional permeability/selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Intense Reactivity in Sulfur–Hydrogen Mixtures at High Pressure under X-ray Irradiation

Superconductivity near room temperature in the sulfur–hydrogen system arises from a sequence of reactions at high pressures, with X-ray diffraction experiments playing a central role in understanding these chemical–structural transformations and the corresponding S:H stoichiometry. Here we document X-ray irradiation acting as both a probe and as a driver of chemical reaction in this dense hydride system. We observe a reaction between molecular hydrogen (H 2 ) and elemental sulfur (S 8 ) under high pressure, induced directly by X-ray illumination, at photon energies of 12 keV using a free electron laser. The rapid synthesis of hydrogen sulfide (H 2 S) at 0.3 GPa was confirmed by optical observations, spectroscopic measurements, and microstructural changes detected by X-ray diffraction. These results document X-ray induced chemical synthesis of superconductor-forming dense hydrides, revealing an alternative production strategy and confirming the disruptive nature of X-ray exposure in studies on high-pressure hydrogen chalcogenides, from water to high-temperature superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Recycling the Energy of Indoor Light: Highly Efficient Organic Photovoltaics via a Ternary Strategy

A ternary approach in organic photovoltaics (OPV) is simple and reliable to effectively tune the optical, electrical and morphological properties of the photoactive layer for high efficiency as well as long-term stability under 1 sun. However, there have been few papers reporting spectra and weak illumination compared to outdoor light. In this study, by using two compatible 16 structures, we demonstrate a simultaneous modulation of light absorption and molecular donor polymers (PM7 and PM7 D1) with slightly-different band gaps and similar chemical packing under ambient conditions, which resulted in efficient indoor OPVs exhibiting power conversion efficiency (PCE) over 20% under 1000 lux of warm white light-emitting diode (2900 K). From morphological analysis, we infer that PM7 serves to seed nucleation of PM7 D1 in the ternary blend, templating its crystallization and alignment along the PM7 backbone. Such templating effect leads to increased domain spacing and relative degree of crystallinity (rDoC) compared to those of each binary system. We further show that higher rDoC helps suppress both bimolecular and trap-assisted recombination of photogenerated charges in the ternary devices. As a result, the complementary absorption and synergistic molecular assembly of the two donor polymers enhance the short-circuit current density as to increase the average PCEs from 9.6 to 10.3% under 1 sun and from 18.7 to 20.0% under 1000 lux. We envision that our strategy of incorporating both a planar and a flexible donor polymer with similar chemical structures can be generally applicable to attain a high performance ternary OPV under both 1 sun and indoor light.

14 SOLAR ENERGY↗
DOE OSTI2021

Ironsand (Titanomagnetite-Titanohematite): Chemistry, Magnetic Properties and Direct Applications for Wireless Power Transfer

Ironsand is an abundant and inexpensive magnetic mineral resource. However, the magnetic properties of unprocessed ironsand are often inadequate for any practical applications. In this work, the applicability of ironsand for use as a component in a soft magnetic composite for large-scale inductive power transfer applications was investigated. After magnetic separation, the chemical, structural and magnetic properties of ironsand sourced from different locations were compared. Differences observed in the DC magnetic properties were consistent with changes in the chemical compositions obtained from X-ray Absorption Near-Edge Spectroscopy (XANES), which suggests varying the titanohematite to titanomagnetite content. Increased content in titanomagnetite and magnetic permeability correlated well with the total Fe content in the materials. The best-performing ironsand with the highest permeability and lowest core losses was used alongside Mn,Zn-Ferrite particles (ranging from ~100 μm to 2 mm) to fabricate toroid cores with varying magnetic material loading. It was shown that ironsand can be used to replace up to 15 wt.% of the magnetic materials with minimal impact on the composite magnetic performance, thus reducing the cost. Ironsand was also used as a supporting material in a single-rail wireless power transfer system, effectively increasing the power transfer, demonstrating potential applications to reduce flux leakage.

36 MATERIALS SCIENCE↗