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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Revealing structure and shaping priorities in plant and fungal cell wall architecture via solid-state NMR

Plant and fungal cell walls are essential for growth, adaptation, and survival, with their intricate architectures dictating both resistance to stress and susceptibility to antifungal or biomass-degrading strategies. Understanding how these walls are built, remodeled, and function at the molecular level is therefore central to both clinical and biotechnological applications. Solid-state nuclear magnetic resonance (ssNMR) has emerged as a uniquely powerful tool for this purpose, as it reveals the structure, dynamics, and interactions of intact biopolymers without disrupting their native organization. Using this approach, recent studies have shown how structural polymorphism, polymer-polymer interactions, and species-specific remodeling govern mechanical integrity, drug resistance, and stress adaptation. Applications highlighted here include lignin-carbohydrate packing during plant stem maturation, fungal wall reorganization under treatment by wall-targeting antifungals such as echinocandin and nikkomycin, and the functional diversity of glucans, chitins, and mannans. Together, these insights uncover conserved principles of polymer assembly across kingdoms while informing new opportunities for antifungal development and biomass utilization. Ongoing advances in sensitivity and resolution are expected to broaden the reach of ssNMR and further accelerate its role in linking structural heterogeneity to biosynthetic complexity and biological function.

Antifungal↗
DOE OSTI2022

Stable CsPbBr 3 Nanoclusters Feature a Disk-like Shape and a Distorted Orthorhombic Structure

CsPbBr 3 nanoclusters have been synthesized by several groups and mostly employed as single-source precursors for the synthesis of anisotropic perovskite nanostructures or perovskite-based heterostructures. Yet, a detailed characterization of such clusters is still lacking due to their high instability. In this work, we were able to stabilize CsPbBr 3 nanoclusters by carefully selecting ad hoc ligands (benzoic acid together with oleylamine) to passivate their surface. The clusters have a narrow absorption peak at 400 nm, a band-edge emission peaked at 410 nm at room temperature, and their composition is identified as CsPbBr 2.3 . Synchrotron X-ray pair distribution function measurements indicate that the clusters exhibit a disk-like shape with a thickness smaller than 2 nm and a diameter of 13 nm, and their crystal structure is a highly distorted orthorhombic CsPbBr 3 . Based on small- and wide-angle X-ray scattering analyses, the clusters tend to form a two-dimensional (2D) hexagonal packing with a short-range order and a lamellar packing with a long-range order.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Synthesis, structural characterization, Hirshfeld surface analysis and spectroscopic studies of cadmium (II) chloride complex with 4-hydroxy-1-methylpiperidine

The chemical preparation, crystal structure, Hirshfeld surface analysis and spectroscopic characterization of the novel cadmium (II) 4-hydroxy-1-methylpiperidine complex, Cd{sub 4}Cl{sub 10}(C{sub 6}H{sub 14}NO){sub 2}·2H{sub 2}O, have been reported. The atomic arrangement can be described as built up by an anionic framework, formed by edge sharing CdCl{sub 6} and CdCl{sub 5}O octahedral linear chains spreading along the a-axis. These chains are interconnected by water molecules via O–H⋯Cl and O–H⋯O hydrogen bonds to form layers parallel to (011) plane. The organic cations are inserted between layers through C–H⋯Cl hydrogen bonds. Investigation of intermolecular interactions and crystal packing via Hirshfeld surface analysis reveals that the H{sub C}⋯Cl and H{sub C}⋯H{sub C} intermolecular interactions are the most abundant contacts of the organic cation in the crystal packing. The statistical analysis of crystal contacts reveals the driving forces in the packing formation. The {sup 13}C and {sup 15}N CP-MAS NMR spectra are in agreement with the X-ray structure. The vibrational absorption bands were identified by infrared spectroscopy. DFT calculations allowed the attribution of the NMR peaks and of the IR bands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Finding new perovskite halides via machine learning

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Decoding Liquid Crystal Oligomer Phase Transitions: Toward Molecularly Engineered Shape Changing Materials

This work details an integrated investigation of liquid crystal (LC) oligomers that combines experiments and molecular dynamics simulations to obtain a detailed understanding of the molecular structure of LC oligomers and the mechanism underlying their phase transition temperatures. We synthesized and characterized a series of LC oligomers prepared from different lengths of methylene spacers in the reactive LC monomers and n-alkylamine chain extenders via the aza -Michael addition reaction. In parallel, we performed isothermal–isobaric ( NPT ) ensemble coarse-grained molecular dynamics (CG-MD) simulation of analogue mesogens that are connected to flexible spacers and extenders at varying temperatures, spacer lengths, and extender lengths. This approach allowed the effect of length in the flexible spacer as well as in the chain extender on the nematic–isotropic transition temperature ( T ni ) to be determined. The results showed that increasing the length of the extender decreases T ni for LC oligomers and amplifies the decrease of T ni in LC oligomers when the spacer length is short. We infer that the combination of spacer and extender changes the shape anisotropy of LC oligomers, changing the packing behavior of constituent mesogens, thus affecting their ability to transition from the isotropic to the nematic phase. Here, the detailed molecular structure–property relationships formulated enable prescribing design rules for LC oligomers geared toward molecularly engineered shape changing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Disk Density Tuning of a Maximal Random Packing

We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Characterizing the Three-Dimensional Structure of Block Copolymers via Sequential Infiltration Synthesis and Scanning Transmission Electron Tomography

Understanding and controlling the three-dimensional structure of block copolymer (BCP) thin films is critical for utilizing these materials for sub-20 nm nanopatterning in semiconductor devices, as well as in membranes and solar cell applications. Combining an atomic layer deposition (ALD) based technique for enhancing the contrast of BCPs in transmission electron microscopy (TEM) together with scanning TEM (STEM) tomography reveals and characterizes the three-dimensional structures of poly(styrene-block-methyl methacrylate) (PS-b-PMMA) thin films with great clarity. Sequential infiltration synthesis (SIS), a block-selective technique for growing inorganic materials in BCPs films in ALD, and an emerging tool for enhancing the etch contrast of BCPs, was harnessed to significantly enhance the high-angle scattering from the polar domains of BCP films in the TEM. The power of combining SIS and STEM tomography for three dimensional (3D) characterization of BCPs films was demonstrated with the following cases: self-assembled cylindrical, lamellar, and spherical PS-PMMA thin films. In all cases, STEM tomography has revealed 3D structures that were hidden underneath the surface, including: 1) the 3D structure of defects in cylindrical and lamellar phases, 2) non-perpendicular 3D surface of grain boundaries in the cylindrical phase, and 3) the 3D arrangement of spheres in body centered cubic (BCC) and hexagonal closed pack (HCP) morphologies in the spherical phase. The 3D data of the spherical morphologies was compared to coarse-grained simulations and assisted in validating the simulations’ parameters. STEM tomography of SIS-treated BCP films enables the characterization of the exact structure used for pattern transfer, and can lead to better understating of the physics which is utilized in BCP lithography.

Block copolymers↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2019

Two-dimensional polythiophene homopolymer as promising hole transport material for high-performance perovskite solar cells

A polythiophene homopolymer with conjugated side chains (2D-PT) was applied as a hole transport material (HTM) of perovskite solar cells (PSCs). The all-conjugated two-dimensional structure effectively increases the ionization potential, realizing a high open-circuit voltage near 1 V for the 2D-PT-based PSC. Very interestingly, the two-dimensional wide-angle X-ray scattering measurements show that the 2D-PT molecules are self-assembled into ordered structure with a face-on dominant orientation and exhibit a hole mobility, which is ~1.6 times faster than that of poly(3-hexylthiophene) (P3HT). Consequently, the power conversion efficiency of PSC is noticeably increased by ~30% as 2D-PT replaces P3HT as HTM. Moreover, the densely packed 2D-PT slows down the moisture induced degradation of perovskite crystals, leading to a better environmental stability. As a result, this work demonstrates that two-dimensional non-donor-acceptor structural conjugated polymers can be utilized as promising HTM of PSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Asynchronous Photoexcited Electronic and Structural Relaxation in Lead-Free Perovskites

Vacancy-ordered lead free perovskites with more stable crystalline structure have been intensively explored as the alternatives for resolving the toxic and long-term stable issues of Lead halide perovskites (LHPs). The dispersive energy bands produced by the closely packed halide octahedral sublattice in these perovskites are meanwhile anticipated to facility the mobility of charge carriers. However, these perovskites suffer from unexpectedly poor charge carrier transport. To tackle this issue, we have employed the ultrafast, elemental-specific X-ray transient absorption (XTA) spectroscopy to directly probe the photoexcited electronic and structural dynamics of a prototypical vacancy-ordered lead-free perovskite (Cs 3 Bi 2 Br 9 ). We have discovered that the photogenerated holes quickly self-trapped at Br centers, simultaneously distorting the local lattice structure, likely forming small polarons in the configuration of V k center (Br 2 - dimer). More significantly, we have found a surprisingly long-lived, structural distorted state with a lifetime of ~ 59 μs, which is about three orders of magnitude slower than that of the charge carrier recombination. Such long-lived structural distortion may produce a transient “background” under continuous light illumination, influencing the charge carrier transport along the lattice framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

The 2.1 Å structure of protein F9 and its comparison to L1, two components of the conserved poxvirus entry-fusion complex

The poxvirus F9 protein is a component of the vaccinia virus entry fusion complex (EFC) which consists of 11 proteins. The EFC forms a unique apparatus among viral fusion proteins and complexes. We solved the atomic structure of the F9 ectodomain at 2.10Å. A structural comparison to the ectodomain of the EFC protein L1 indicated a similar fold and organization, in which a bundle of fve α-helices is packed against two pairs of β-strands. However, instead of the L1 myristoylation site and hydrophobic cavity, F9 possesses a protruding loop between α-helices α3 and α4 starting at Gly90. Gly90 is conserved in all poxviruses except Salmon gill poxvirus (SGPV) and Diachasmimorpha longicaudata entomopoxvirus. Phylogenetic sequence analysis of all Poxviridae F9 and L1 orthologs revealed the SGPV genome to contain the most distantly related F9 and L1 sequences compared to the vaccinia proteins studied here. The structural diferences between F9 and L1 suggest functional adaptations during evolution from a common precursor that underlie the present requirement for each protein.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structural Characterization of Disulfide-Linked p53-Derived Peptide Dimers

Disulfide bonds provide a convenient method for chemoselective alteration of peptide and protein structure and function. We previously reported that mild oxidation of a p53-derived bisthiol peptide (CTFANLWRLLAQNC) under dilute non-denaturing conditions led to unexpected disulfide-linked dimers as the exclusive product. The dimers were antiparallel, significantly α-helical, resistant to protease degradation, and easily reduced back to the original bisthiol peptide. Here, in this work, we examine the intrinsic factors influencing peptide dimerization using a combination of amino acid substitution, circular dichroism (CD) spectroscopy, and X-ray crystallography. CD analysis of peptide variants suggests critical roles for Leu6 and Leu10 in the formation of stable disulfide-linked dimers. The 1.0 Å resolution crystal structure of the peptide dimer supports these data, revealing a leucine-rich LxxLL dimer interface with canonical knobs-into-holes packing. Two levels of higher-order oligomerization are also observed in the crystal: an antiparallel “dimer of dimers” mediated by Phe3 and Trp7 residues in the asymmetric unit and a tetramer of dimers mediated by Trp7 and Leu10. In CD spectra of Trp-containing peptide variants, minima at 227 nm provide evidence for the dimer of dimers in dilute aqueous solution. Importantly, and in contrast to the original dimer model, the canonical leucine-rich core and robust dimerization of most peptide variants suggests a tunable molecular architecture to target various proteins and evaluate how folding and oligomerization impact various properties, such as cell permeability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Genetic control of morphological transitions in a coacervating protein template

Nature routinely exploits liquid–liquid phase separation (LLPS) of proteins to control the assembly and mineralization of hybrid materials. Here, we show that fusion of the Car9 silica-binding peptide to an elastin-like polypeptide (ELP) yields temperature- and sequence-programmable soft matter templates for the synthesis of silicified architectures ranging in size from nanometers to micrometers. Specifically, we demonstrate unprecedented control over the diameter of silica nanoparticles (SiNP) in the 30–60 nm range with 4 nm precision, show that a single arginine residue (R4) in the Car9 sequence underpins the transition from micelles to proteinosomes, and find that substitutions in other basic residues modulate electrostatic repulsion and solvation to enable access to kinetically trapped species. These structures, which include interconnected micelles, small (∼200 nm) and large (>5 µm) vesicles, are readily visualized by SEM imaging following silicification. Molecular dynamics (MD) simulations and AlphaFold predictions reveal that mutations in positively charged residues alter interfacial packing, hydration, and conformational freedom of the silica-binding segments. Overall, our results establish sequence and thermal energy as synergistic levers for morphological control across length scales using solid-binding ELPs and establish mineralization as a powerful tool to visualize the structure of dynamic soft matter assemblies.

hierarchy↗
NASA NTRS2017

Parametric Study of an Ablative TPS and Hot Structure Heatshield for a Mars Entry Capsule Vehicle

The National Aeronautics and Space Administration is planning to send humans to Mars. As part of the Evolvable Mars Campaign, different en- try vehicle configurations are being designed and considered for delivering larger payloads than have been previously sent to the surface of Mars. Mass and packing volume are driving factors in the vehicle design, and the thermal protection for planetary entry is an area in which advances in technology can offer potential mass and volume savings. The feasibility and potential benefits of a carbon-carbon hot structure concept for a Mars entry vehicle is explored in this paper. The windward heat shield of a capsule design is assessed for the hot structure concept as well as an ablative thermal protection system (TPS) attached to a honeycomb sandwich structure. Independent thermal and structural analyses are performed to determine the minimum mass design. The analyses are repeated for a range of design parameters, which include the trajectory, vehicle size, and payload. Polynomial response functions are created from the analysis results to study the capsule mass with respect to the design parameters. Results from the polynomial response functions created from the thermal and structural analyses indicate that the mass of the capsule was higher for the hot structure concept as compared to the ablative TPS for the parameter space considered in this study.

Langston, Sarah L.↗
DOE OSTI2017

Embarras de richesses – It is not good to be too anomalous: Accurate structure of selenourea, a chiral crystal of planar molecules

Selenourea, SeC(NH 2 ) 2 , recently found an application as a derivatization reagent providing a significant anomalous diffraction signal used for phasing macromolecular crystal structures. The crystal structure of selenourea itself was solved about 50 years ago, from data recorded on films and evaluated by eye and refined to R = 0.15 with errors of bond lengths and angles about 0.1 Å and 6°. In the current work this structure is re-evaluated on the basis of synchrotron data and refined to R1 = 0.021 with bond and angle errors about 0.007 Å and 0.5°. The nine planar molecules of selenourea pack either in the P3 1 or in the P3 2 unit cell. All unique molecules are connected by a complex network of Se•••H-N hydrogen bonds and Se•••Se contacts. The packing of selenourea molecules is highly pseudosymmetric, approximating either of the P3 1(2) 12, R3, and R32 space groups. Because the overwhelming majority of diffracted X-ray intensity originates form the anomalously scattering selenium atoms, the measurable anomalous Bijvoet differences are diminished and it was not possible to solve this crystal structure based on the anomalous signal alone.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Nanotubes, Plates, and Needles: Pathway-Dependent Self-Assembly of Computationally Designed Peptides

Computationally designed peptides form desired antiparallel, tetrameric coiled-coil bundles that hierarchically assemble into a variety of well-controlled nanostructures depending on aqueous solution conditions. The bundles selectively self-assemble into different structures: nanotubes, platelets, or needle-like structures at solution pH values of 4.5, 7, and 10, respectively. The self-assembly produces hollow tubes or elongated needle-like structures at pH conditions associated with charged bundles (pH 4.5 or 10); at neutral pH, near the pI of the bundle, a plate-like self-assembled structure forms. Transmission electron microscopy and small-angle X-ray scattering show the nanotubes to be uniform with a tube diameter of ~13 nm and lengths of up to several μm, yielding aspect ratios >1000. Combining the measured nanostructure geometry with the apparent charged states of the constituent amino acids, a tilted-bundle packing model is proposed for the formation of the homogeneous nanotubes. Here, this work demonstrates the successful use of assembly pathway control for the construction of nanostructures with diverse, well-structured morphologies associated with the folding and self-association of a single type of molecule.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A spall and diffraction study of nanosecond pressure release across the iron ε-α phase boundary

The extreme response of polycrystalline iron at high pressures and high strain rates is revealed by means of high-power laser pulses. The compression portion of the pulse coupled with x-ray diffraction identifies the expected body-centered cubic (α) to hexagonal close packed (ε) displacive transformation. Upon release, observation shows that the complete reverse transformation takes approximately 8 ns and that the structure returns to its initial microstructural configuration, in a reversible transformation path. This is in good agreement with molecular dynamics (MD) simulations which predict an inverse dependence between transformation time and strain rate. The grain size is reduced from μm to nm range during compression and begins increasing back to the original grain size on decompression. The kinetics of the transition is dictated by heterogenous nucleation as it follows the Johnson-Mehl-Avrami-Kolmogorov equation with the appropriate time exponent of ~1. This is confirmed by MD simulations which also identify profuse twinning and dislocation generation. Here, the tensile pulse generated upon reflection at the free surface is captured by time-resolved free surface velocity measurements from which a peak tensile stress of 7 GPa is obtained, in stark contrast with its quasi-static value of ~200 MPa. At these strain rates, the strength of grain interiors, which is determined by twinning and slip exceeds the strength of the boundaries, and failure initiates preferentially in the latter.

36 MATERIALS SCIENCE↗