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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays

Molecular initiating events (MIEs) are key events in adverse outcome pathways (AOPs) that link molecular chemistry to target biology. As they are based in chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modelling. In this work, we aim to link ligand chemical structure to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs, and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

Allen, Timothy H.↗
DOE OSTI2021

Effect of the Segmental Structure of Thermoplastic Polyurethane (Hardness) on the Interfacial Adhesion of Textile-Grade Carbon Fiber Composites

In polymer composites, the fiber–matrix interface is primarily influenced by the surface treatment of the fibers and polymer morphology. Previous studies have investigated the effect of surface treatment of carbon fiber on the mechanical properties of the resulting composites. However, very few studies have explored the chemical structural modification of polymer effect on the fiber–matrix adhesion. In this work, the interfaces of soft thermoplastic polyurethane (S-TPU) and hard-segmented TPU (H-TPU) were investigated through surface, thermal, and mechanical characterization. A textile-grade carbon fiber (TCF) with 1% concentration of epoxy sizing (an emerging material for nonaerospace applications with 450 K filament tows) was used as a reinforcement to investigate the structure–property relationship at the interface. Atomic force microscopy results showed 39% higher surface roughness for S-TPU than for H-TPU. X-ray photoelectron spectroscopy results revealed a 550% increase in C=O content, which can provide multiple hydrogen bonding networks in H-TPU, and these C=O bonds can produce a strong chemical bond at the fiber–matrix interface. Additionally, dynamic mechanical analysis and differential scanning calorimetry results confirmed the presence of hydrogen bonding that enhances the cross-linked density by 232% in H-TPU compared to S-TPU. The improved mechanical properties of H-TPU composites, such as flexural, impact and tensile by 30, 50, and 130% compared to S-TPU composites, prove that the crystallinity and hydrogen bonding significantly increase the load bearing capacity due to the strong interface. The mechanical properties of TCF–TPU composites validate the formation of chemical bonds through a nucleophilic addition reaction at the interface and improve the bond strength of the composites. Thus, tailoring the polyurethane structure broadens the performance of segmented TPU in conjunction with TCF reinforcement, which has implications for cost-effective, high-strength composites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗
DOE OSTI2020

Ambient Ion Trapping and Separations for High Throughput Structurally Selective Material Deposition

While ion mobility coupled to mass spectrometry (IMS/MS) has been increasingly adapted to biological analysis, the structural and molecular specificity offered by IMS/MS has untapped potential for creating novel materials (for energy, catalysis, biomolecular switches etc.) with rationally tailored properties by precisely controlling the chemical structure and composition of these materials. While electrochemical and catalytic properties of the bulk material are understood by mass-selective ion-soft-landing, different structures of one molecule can exhibit distinct properties from the bulk. Many biological phenomenon occur at interfaces and are highly structurally specific. IMS/MS offers means to separate molecules in a sample (which is first ionized and released to gas phase from the condensed phase) based on mass and structure. Species thus separated can potentially be deposited onto surfaces with molecular and structural specificity, to perform fundamental investigations for understanding critical interfacial phenomenon. Key performance metrics for IMS/MS analysis are resolution of separations and sensitivity. Recently developed Structures for Lossless Ion Manipulations (SLIM)4 enabled unprecedented resolution in IMS separations. In addition, high sensitivity was enabled in SLIM due to lossless confinement of ions using radio frequency fields. Thus, SLIM enabled extremely long path length separations (up to 0.1 km thus far) providing efficient separation of molecular ions never before achieved (e.g. isotopologues, D&L amino acids). While SLIM has unprecedented analytical separations power/utility, the use of SLIM platform for depositing mobility and mass selected molecular ions has two specific technological challenges: (a) Pulsed nature of IMS separations and their low duty cycle and (b) the need for a low vacuum of ~4 torr for lossless ion confinement. These limitations lead to long deposition times due to loss of ions generated from the sample. Enabling ion confinement at atmospheric pressure will be path-breaking contribution to the fundamental understanding of ion manipulations and separations and will also enable novel science currently precluded due to the inability of the present technologies to confine ions under ambient conditions. Traditionally used radio frequency fields are ineffective in confining ions at high pressures due to ion/neutral collisions. To address these, we propose to (a) develop methods for confining and manipulating ions for lossless IMS separations at atmospheric pressure enabling high sensitivity and throughput and (b) use the high specificity of IMS/MS and high throughput from ambient ion confinement to perform mobility and mass selective ion deposition at atmospheric pressure. Our goal is to enable the study of the fundamental properties of energy/catalysis materials and biomolecules. This project paves the way for high-throughput, mass and structure selective material deposition for highly specific surface creation/functionalization (discussed in Section 2 of the report). Further, the developments reported continue to push the boundaries of this process to ambient conditions (discussed in Section 3 of the report). We conclude with a list of specific intellectual property and publications which have been the outcome of this funded project.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Real-time Characterization of the Fine Structure and Dynamics of an Electrical Double Layer at Electrode-electrolyte Interfaces

The chemisorption of species from supporting electrolytes on electrode surfaces is ubiquitous in electrochemical systems and affects the dynamics and mechanism of various electrochemical reactions. The understanding of chemical structure and property of the resulting electrical double layer is vital but limited. In this work, we operando probed the electrochemical interface between a gold electrode surface and a common supporting electrolyte, phosphate buffer, using our newly developed in situ liquid secondary ion mass spectrometry. We surprisingly found that on the positively charged gold electrode surface sodium cations were anchored in the stern layer in a partially dehydrated form by formation of compact ion pairs with the accumulated phosphate anions. The resulting strong and dense adsorption phase was further revealed to retard the electro-oxidation reaction of ascorbate. This finding addressed one major gap in the fundamental science of electrode-electrolyte interfaces that where and how the cations reside in the double layer to impose effects on electrochemical reactions, providing insights into engineering of better electrode-electrolyte interfaces in electrochemical processes.

Zhang, Yanyan↗
DOE OSTI2022

Tailoring the Structure of Carbon Molecular Sieves Derived from an Aromatic Polyamide

Energy-efficient and selective separation technologies are required to reclaim aqueous waste streams for reuse. Carbon molecular sieves (CMS) are one material capable of separating organic solvent and gaseous mixtures due to the presence of permanent, rigid, and molecular sieving pores and could potentially be used in aqueous separations. Here, a novel CMS material is derived from an aromatic polyamide precursor with tailored fabrication techniques. Structural characteristics of this CMS have been probed with various analyses and are potentially ideal for selective separations as adsorbents. In this work, elemental analyses provide insight about the potential chemical structures of CMS materials that result from degradation mechanisms. Gravimetric sorption data is used to estimate sorption, diffusion, and permeability selectivity with water and N, N-dimethylformamide. While challenges to the development and implementation of such carbonaceous materials remain, highly selective materials are critical starting points for enabling ultra-challenging separation processes of the future.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exceptional Cycling Performance Enabled by Local Structural Rearrangements in Disordered Rocksalt Cathodes

The capacity of lithium transition-metal (TM) oxide cathodes is directly linked to the magnitude and accessibility of the redox reservoir associated with TM cations and/or oxygen anions, which traditionally decreases with cycling as a result of chemical, structural, or mechanical fatigue. Here, it is shown that a capacity increase over 125% can be achieved upon cycling of high-energy Mn- and F-rich cation-disordered rocksalt oxyfluoride cathodes. This study reveals that in Li 1.2 Mn 0.7 Nb 0.1 O 1.8 F 0.2 , repeated Li extraction/reinsertion utilizing Mn 3+ /Mn 4+ redox along with some degree of O-redox participation leads to local structural rearrangements and formation of domains with off-stoichiometry spinel-like features. The effective integration of these local “structure-domains” within the cubic disordered rocksalt framework promotes better Li diffusion and improves material utilization, consequently increased capacity upon cycling. Finally, this study provides important new insights into materials design strategies to further exploit the rich compositional and structural space of Mn chemistry for developing sustainable, high-energy cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2020

Structural characterization of sugarcane lignins extracted from different protic ionic liquid pretreatments

Previous studies based on protic ionic liquids have shown them to be effective for extracting lignins from biomass. Moreover, ILs synthesized with amine cations can promote the insertion of nitrogen into lignin, favoring its valorization for industrial applications. Here, lignins extracted from sugarcane bagasse by eight different PILs were analyzed. Several techniques of characterization were employed (2D HSQC NMR, 31P NMR, GPC, Py-GC/MS, TGA, FTIR, and elemental analysis), seeking a deeper understanding of lignin structure post-PILS treatment. In particular, lignin obtained with acetate and lactate anions with monoethanolamonium cations showed similar structural compositions. Lignins obtained from ethylenediamonium cation combined anions acetate and lactate exhibited relatively higher molecular weights compared to the other lignins studied. The elemental analysis of these lignins further revealed the presence of nitrogen in their structures, suggesting that amination occurred. In this work we used PILs not yet reported in the literature for this purpose, and consequently obtained lignins with specific structures. Overall, our results demonstrated that depending on the PILs used, lignins with distinct properties could be obtained which might be used for value-added applications. This work covered new advances in elucidation of lignin chemical structure and opened a new path for lignin valorization.

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

Random Polymerization Strategy Leads to a Family of Donor Polymers Enabling Well‐Controlled Morphology and Multiple Cases of High‐Performance Organic Solar Cells

Abstract Developing high‐performance donor polymers is important for nonfullerene organic solar cells (NF‐OSCs), as state‐of‐the‐art nonfullerene acceptors can only perform well if they are coupled with a matching donor with suitable energy levels. However, there are very limited choices of donor polymers for NF‐OSCs, and the most commonly used ones are polymers named PM6 and PM7, which suffer from several problems. First, the performance of these polymers (particularly PM7) relies on precise control of their molecular weights. Also, their optimal morphology is extremely sensitive to any structural modification. In this work, a family of donor polymers is developed based on a random polymerization strategy. These polymers can achieve well‐controlled morphology and high‐performance with a variety of chemical structures and molecular weights. The polymer donors are D–A1–D–A2‐type random copolymers in which the D and A1 units are monomers originating from PM6 or PM7, while the A2 unit comprises an electron‐deficient core flanked by two thiophene rings with branched alkyl chains. Consequently, multiple cases of highly efficient NF‐OSCs are achieved with efficiencies between 16.0% and 17.1%. As the electron‐deficient cores can be changed to many other structural units, the strategy can easily expand the choices of high‐performance donor polymers for NF‐OSCs.

Liang, Jiaen↗
DOE OSTI2024

Uncovering fast solid-acid proton conductors based on dynamics of polyanion groups and proton bonding strength

Achieving high proton conductivity in inorganic solids is key for advancing many electrochemical technologies, including low-energy nano-electronics and energy-efficient fuel cells and electrolyzers. A quantitative understanding of the physical traits of a material that regulate proton diffusion is necessary for accelerating the discovery of fast proton conductors. In this work, we have mapped the structural, chemical and dynamic properties of solid acids to the elementary steps of the Grotthuss mechanism of proton diffusion. Our approach combines ab initio molecular dynamics simulations, analysis of phonon spectra and atomic structure calculations. We have identified the donor–hydrogen bond lengths and the acidity of polyanion groups as key descriptors of local proton transfer and the vibrational frequencies of the cation framework as the key descriptor of lattice flexibility. The latter facilitates rotations of polyanion groups and long-range proton migration in solid acid proton conductors. The calculated lattice flexibility also correlates with the experimentally reported superprotonic transition temperatures. Using these descriptors, we have screened the Materials Project database and identified potential solid acid proton conductors with monovalent, divalent and trivalent cations, including Ag + , Sr 2+ , Ba 2+ and Er 3+ cations, which go beyond the traditionally considered monovalent alkali cations (Cs + , Rb + , K + , and NH 4 + ) in solid acids.

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

Chemical and Electronic Structure at the Interface between a Sputter-Deposited Zn(O,S) Buffer and a Cu(In,Ga)(S,Se) 2 Solar Cell Absorber

The chemical and electronic structure of the interface between a sputter-deposited Zn(O,S) buffer layer and an industrial Cu(In,Ga)(S,Se) 2 (CIGSSe) absorber for thin-film solar cells is investigated with X-ray and UV photoelectron spectroscopy, inverse photoemission spectroscopy, and X-ray emission spectroscopy. We find a CIGSSe absorber surface band gap of 1.61 (±0.14) eV, which is significantly increased as compared to the minimal value derived with bulk-sensitive methods (≈1.1 eV). We find no indication for diffusion of absorber elements into the buffer layer. Surface- and bulk-sensitive measurements of the buffer layer suggest the presence of S-Zn and S-O bonds in the Zn(O,S) layer. We find that the naturally existing downward band bending toward the CIGSSe absorber surface is increased by the formation of the interface, likely enhancing carrier separation under illumination. We also derive a flat conduction band alignment, in line with the reported high conversion efficiencies of corresponding large-area solar cells.

14 SOLAR ENERGY↗
DOE OSTI2020

Role of Alkali-Metal Cations in Electronic Structure and Halide Segregation of Hybrid Perovskites

The ability to control or prevent phase segregation in perovskites is crucial to realizing stable and tunable mixed-halide optoelectronic devices. Here, we systematically examine the impact of alkali-metal-cation (Cs + and K + ) concentration on the band structure, chemical composition, phase segregation, and polycrystalline microstructure on formamidinium-dominated mixed-halide mixed-cation perovskite films. It was found that the incorporation of Cs + and K + cations decreases the work function and the core levels of all components shift toward higher binding energy consistent with n-doping the perovskite film, which facilitates electron transfer to the electron transport layer TiO 2 . A concentration-dependent film structure was observed by X-ray photoemission spectroscopy and grazing incidence wide-angle X-ray scattering where the halides and cations are distributed evenly across perovskite films at low metallic cation concentration (5%). A high metal-cation ratio (20%) leads to halide segregation within the perovskite film and the surface becomes bromide-poor, whereas the bromide and metal cations diffuse more deeply within the film. These differences in electronic properties, element distribution, and film morphology were reflected in the device performance where the power conversion efficiency of low-metallic-cation concentration (5% of Cs + and K + ) perovskite solar cells is ≈5% higher than the high-concentration ones (20%). This study provides valuable chemical and physical insight into the underlying trade-offs in the careful tuning of electrical properties and film structure to optimize multication and mixed-halide hybrid perovskites.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structural elucidation of polydopamine facilitated by ionic liquid solvation

Minimal understanding of the formation mechanism and structure of polydopamine (pDA) and its natural analogue, eumelanin, impedes the practical application of these versatile polymers and limits our knowledge of the origin of melanoma. The lack of conclusive structural evidence stems from the insolubility of these materials, which has spawned significantly diverse suggestions of pDA's structure in the literature. We discovered that pDA is soluble in certain ionic liquids. Using these ionic liquids (ILs) as solvents, we present an experimental methodology to solvate pDA, enabling us to identify pDA's chemical structure. The resolved pDA structure consists of self-assembled supramolecular aggregates that contribute to the increasing complexity of the polymer. The underlying molecular energetics of pDA solvation and a macroscopic picture of the disruption of the aggregates using IL solvents have been investigated, along with studies of the aggregation mechanism in water.

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

Structural transition and chemical reactivity of atomic carbon chains

The sp-hybridized carbon chain (carbyne) is a representative 1D atomic material, whose bonding structure and chemical reactivity have remained a mystery for a century. Here, in this work, we report the unexpected alternating bond orders of 1.4 and 2.6 for the most stable carbon chain and the in situ diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) detection of the temperature-dependent reversible change of the bond order alternation. Moreover, we revealed its reactivities with O 2 , H 2 , and CO 2 at temperatures up to 600 °C and created an end-group-protection strategy to stabilize it. These observations open a new door to the chemistry of atomic materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Chemical Design of Organic Interface Modifiers for Highly Efficient and Stable Perovskite Solar Cells

Perovskite solar cells (PSCs) have demonstrated rapid progress in their power conversion efficiencies (PCEs) - from 3.8% in 2009 to 25.7% in 2022 - and they have received considerable attention as a promising future photovoltaic (PV) technology. However, the operational stability of PSCs is still inadequate to satisfy the standards for commercial applications. Interface engineering has become one of the most important strategies to push PSCs' efficiency and stability for practical use. Among the various interface engineering approaches, organic interface modifiers (OIMs) have been frequently used by the PSC field to address the issues limiting PSC stability at high efficiency levels. In this perspective, the chemical structures of state-of-the-art OIMs are discussed, and their characteristics are reviewed, as well as the impact on device performance associated with key device interfaces (e.g., metal oxide/perovskite and organic transport layer/perovskite interfaces) from a chemical and materials engineering point of view is discussed. Finally, design considerations and the authors' perspective are discussed, on the basis of representative literature examples, for building new, customized organic OIMs to further improve PSC efficiency and stability toward commercialization.

14 SOLAR ENERGY↗
DOE OSTI2021

ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning

Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks where each network casts one vote for the predicted molecule, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The ensemble model achieved an accuracy of 76% on hand-drawn hydrocarbons, increasing to 86% if the top 3 predictions were considered.

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

Polymer genome–based prediction of gas permeabilities in polymers

Abstract Predicting gas permeabilities of polymers a priori is a long-standing challenge within the membrane research community that has important applications for membrane process design and ultimately widespread adoption of membrane technology. From early attempts based on free volume and cohesive energy to more recent group contribution methods, the ability to predict membrane permeability has improved in terms of accuracy. However, these models usually stay “within the paper”, i.e. limited model details are provided to the wider community such that adoption of these predictive platforms is limited. In this work, we combined an advanced polymer chemical structure fingerprinting method with a large experimental database of gas permeabilities to provide unprecedented prediction precision over a large range of polymer classes. No prior knowledge of the polymer is needed for the prediction other than the repeating unit chemical formula. In addition, we have incorporated this model into the existing Polymer Genome project to make it open to the membrane research community.

Polymer Science↗