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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Genetic identification of 91 poplar cultivars based on SSR markers

Accurate genetic identification and relationship analysis of poplar cultivars is necessary to establish commercial poplar plantations and select suitable breeding strategies. In this study, 91 poplar cultivars belonging to four sections (Aigeiros, Tacamahaca, Populus and Turanga) and inter/intra-sectional hybrids were genotyped using 18 polymorphic simple sequence repeat (SSR) markers. In total, 222 alleles were amplified with an average of 12.3 alleles per marker. The mean polymorphic information content and power of discrimination were 0.706 and 0.813, respectively. Five SSR markers (ORPM_103, ORPM_247, GCPM_1048, GCPM_1255 and LG_X_19) constituted a core fingerprint and were sufficient to identify all the tested cultivars. With some notable exceptions, cultivars of the same species generally clustered together in cluster (UPGMA) and ordination (PCO) analyses. Flow cytometry indicated that 11 poplar cultivars were triploid. Among these, seven had three alleles at some loci, suggesting that SSR markers could indicate the ploidy level to some extent. This study provides useful genetic information for the identification and protection of poplar cultivars in China and offers a guideline for the selection of poplar crossing parents based on ploidy level and genetic relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Understanding Recent Global Hydroclimate Change Using Multivariate Detection and Attribution Techniques and GCM Experiments

The observed slowdown in the rate of global average surface temperature (T) warming during the post-1998 decade (referred to at the time as the “global warming hiatus”) posed a challenge for climate researchers because it was not captured in global climate models projection. Observational and modeling evidence hinted that that leveling of the T trend was linked with the simultaneous appearance of cold surface temperature in the eastern equatorial Pacific that formed a protracted La Niña-like state, a phenomenon known as the Inter-decadal Pacific Oscillation (IPO). It was however not clear whether the concurrent equatorial Pacific cooling was a regional response to the rise in atmospheric greenhouse gas levels or anthropogenic aerosols that is not captured by the climate models or whether it was an unrelated, serendipitous manifestation of a natural phenomenon, internal to the climate system. To resolve this issue, we proposed in this project to develop and apply fingerprinting methods to differentiate between the observed climate response to various external forcing agents and the manifestations of internal climate variability. The project also sought to find out if climate models are able to reproduce the change signals detected in key observational phenomena and if they can be used to investigate the underlying processes. As such we examined changes in global hydroclimate variability and in an outstanding example of regional hydroclimate change, namely the multi-decadal 20 th century change in Sahel rainfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm -1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

quantitative structure–property relationship (QSPR↗
DOE OSTI2024

Unsupervised physics-informed disentanglement of multimodal data

Here, we introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal datasets. Individual modalities are embedded into a shared latent space and fused through a product-of-experts formulation, enabling a Gaussian mixture prior to identify shared features. Sampling from clusters allows cross-modal generative modeling, with a mixture-of-experts decoder that imposes inductive biases from prior scientific knowledge and thereby imparts structured disentanglement of the latent space. This approach enables cross-modal inference and the discovery of features in high-dimensional heterogeneous datasets. Consequently, this approach provides a means to discover fingerprints in multimodal scientific datasets and to avoid traditional bottlenecks related to high-fidelity measurement and characterization of scientific datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Probing Defects and Spin‐Phonon Coupling in CrSBr via Resonant Raman Scattering

Abstract Understanding the stability limitations and defect formation mechanisms in 2D magnets is essential for their utilization in spintronic and memory technologies. Here, defects in mono‐ to multilayer CrSBr are correlated with structural, vibrational, and magnetic properties. Resonant Raman scattering is used to reveal distinct vibrational defect signatures. In pristine CrSBr, it is shown that bromine atoms mediate vibrational interlayer coupling, allowing for distinguishing between surface and bulk defect modes. Environmental exposure is shown to cause drastic degradation in monolayers, with the formation of intralayer defects. This is in contrast to multilayers that predominantly show bromine surface defects. Through deliberate ion irradiation, the formation of defect modes is tuned: these are strongly polarized and resonantly enhanced, reflecting the quasi‐‐1D electronic character of CrSBr. Strikingly, pronounced signatures of spin‐phonon coupling of the intrinsic phonon modes and the ion beam‐induced defect modes are observed throughout the magnetic transition temperature. Overall, defect engineering of magnetic properties is possible, with resonant Raman spectroscopy serving as a direct fingerprint of magnetic phases and defects in CrSBr.

2D magnetic semiconductors↗
DOE OSTI2024

Cationic and Anionic Redox of Battery Cathodes Investigated by Advanced Synchrotron‐Based Mapping of Resonant Inelastic X‐ray Scattering

Abstract Redox reaction builds the foundation stone for the energy density of rechargeable battery cathodes. Probing and understanding the redox reaction behavior is crucial, but also extremely formidable, which requires individual and reliable detection of cationic and anionic redox states. Fortunately, the recently developed ultra‐high‐efficiency mapping of resonant inelastic X‐ray scattering (mRIXS) has emerged as a powerful tool to probe the battery chemistry states. Here, the latest advances of employing advanced mRIXS is summarized to investigate the cationic and anionic redox mechanism of battery cathodes during electrochemical operation. Owing to the new dimension of information along the emission energy and high sensitivity to valence 3d states, 3d transition‐metal L ‐edge (TM‐ L ) mRIXS can eliminate the lineshape distortion in conventional 3d TM‐ L fluorescence X‐ray absorption spectra and investigate the cationic redox quantitatively. Moreover, O‐ K mRIXS could fingerprint the intrinsic oxidized lattice oxygen states and quantify the oxygen redox (OR) reversibility, thus demystifying the controversy in traditional wisdom. In addition, different modification strategies coupled with underlying mechanisms for regulating the activity and reversibility of OR utilizing mRIXS are also summarized. This review provides valuable guidance for further exploration of underlying reaction mechanisms of battery cathodes by mRIXS, along with both technological and scientific improvements.

Cheng, Chen↗
DOE OSTI2022

Machine Learning for Optical Scanning Probe Nanoscopy

Abstract The ability to perform nanometer‐scale optical imaging and spectroscopy is key to deciphering the low‐energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering‐type scanning near‐field optical microscopy (s‐SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s‐SNOM, together with scanning probe research in general, can benefit in many ways from artificial‐intelligence (AI) and machine‐learning (ML) algorithms. Augmented with AI‐ and ML‐enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.

Chen, Xinzhong↗
DOE OSTI2022

Combining Experimental and Theoretical Techniques to Gain an Atomic Level Understanding of the Defect Binding Mechanism in Hard Carbon Anodes for Sodium Ion Batteries

Sodium ion batteries (NIBs) are an attractive alternative to lithium–ion batteries in applications that require large–scale energy storage due to sodium's high natural abundance and low cost. Hard carbon (HC) is the most promising anode material for NIBs; however, there is a knowledge gap in the understanding of the sodium binding mechanism that prevents a rational design of HC. This study tunes sucrose–derived HC via synthesis temperature then evaluates the structural, physical, and electrochemical properties. Neutron total scattering is used to generate structural models by fitting pair distribution functions (PDF) with a combination of molecular dynamics and reverse Monte Carlo methods. From this model, the number and type of structural features are identified, quantified, and correlated to the galvanostatic charge/discharge. A method of PDF “fingerprinting” binding sites using Na probe atoms is developed and analyzing these PDFs reveals an atomistic view of ion binding sites responsible for “defect” storage mechanisms. Combining these techniques results in an atomic–level study that provides a big picture of the Na–binding mechanism in NIBs, which allows for more precise tuning of the structure–property relationships in the future. Finally, the methodologies developed will also enable new strategies for the analysis of amorphous functional materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Quantifying Twist Angles in Cuprate Heterostructures with Anisotropic Raman Signatures

Artificially engineered twisted van der Waals (vdW) heterostructures have unlocked new pathways for exploring emergent quantum phenomena and strongly correlated electronic states. Many of these phenomena are highly sensitive to the twist angle, which can be deliberately tuned to tailor the interlayer interactions. This makes the twist angle a critical tunable parameter, emphasizing the need for precise control and accurate characterization during device fabrication. In particular, twisted cuprate heterostructures based on Bi 2 Sr 2 CaCu 2 O 8 + x (BSCCO) have demonstrated angle-dependent superconducting properties, positioning the twist angle as a key tunable parameter. However, the twisted interface is highly unstable under ambient conditions and vulnerable to damage from conventional characterization tools such as electron microscopy or scanning probe techniques. In this work, a fully non-invasive, polarization-resolved Raman spectroscopy approach is introduced for determining twist angles in artificially stacked BSCCO heterostructures. By analyzing twist-dependent anisotropic vibrational Raman modes, particularly utilizing the out-of-plane A 1g vibrational mode of Bi/Sr at ≈116 cm −1 , clear optical fingerprints of the rotational misalignment between cuprate layers are identified. The high-resolution confocal Raman setup, equipped with polarization control and RayShield filtering down to 10 cm −1 , allows for reliable and reproducible measurements without compromising the material's structural integrity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Rapid Biocatalytic Synthesis of Aromatic Acid CoA Thioesters by Using Microbial Aromatic Acid CoA Ligases

Abstract Chemically labile ester linkages can be introduced into lignin by incorporation of monolignol conjugates, which are synthesized in planta by acyltransferases that use a coenzyme A (CoA) thioester donor and a nucleophilic monolignol alcohol acceptor. The presence of these esters facilitates processing and aids in the valorization of renewable biomass feedstocks. However, the effectiveness of this strategy is potentially limited by the low steady‐state levels of aromatic acid thioester donors in plants. As part of an effort to overcome this, aromatic acid CoA ligases involved in microbial aromatic degradation were identified and screened against a broad panel of substituted cinnamic and benzoic acids involved in plant lignification. Functional fingerprinting of this ligase library identified four robust, highly active enzymes capable of facile, rapid, and high‐yield synthesis of aromatic acid CoA thioesters under mild aqueous reaction conditions mimicking in planta activity.

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

Indirectly Detected DNP-Enhanced 17 O NMR Spectroscopy: Observation of Non-Protonated Near-Surface Oxygen at Naturally Abundant Silica and Silica-Alumina

Recent studies have shown that dynamic nuclear polarization (DNP) can be used to detect 17 O solid-state NMR spectra of naturally abundant samples within a reasonable experimental time. Observations using indirect DNP, which relies on 1 H mediation in transferring electron hyperpolarization to 17 O, are currently limited mostly to hydroxyls. Direct DNP schemes can hyperpolarize non-protonated oxygen near the radicals; however, they generally offer much lower signal enhancements. In this study, we demonstrate the detection of signals from non-protonated 17 O in materials containing silicon. The sensitivity boost that made the experiment possible originates from three sources: indirect DNP excitation of 29 Si via protons, indirect detection of 17 O through 29 Si nuclei using two-dimensional 29 Si{ 17 O} D-HMQC, and Carr-Purcell-Meiboom-Gill refocusing of 29 Si magnetization during acquisition. This 29 Si-detected scheme enabled, for the first time, 2D 17 O– 29 Si heteronuclear correlation spectroscopy in mesoporous silica and silica-alumina surfaces at natural abundance. In contrast to the silanols showing motion-averaged 17 O signals, the framework oxygens exhibit unperturbed powder patterns as unambiguous fingerprints of surface sites. Furthermore, along with hydroxyl oxygens, detection of these moieties will help in gaining more atomistic-scale insights into surface chemistry.

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

An international laboratory comparison of dissolved organic matter composition by high resolution mass spectrometry: Are we getting the same answer?

Since the first detailed compositional analysis of dissolved organic matter (DOM) in 1995, high resolution mass spectrometry (HRMS) has become a vital tool for DOM characterization. While the upward trend in HRMS for molecular level analysis of DOM continues, so do the challenges of data comparison and interpretation between laboratories operating instruments of differing performance and user operating conditions. It is therefore essential that the community establishes whether data and trends can be compared robustly between research groups. To this end, 4 identically prepared DOM samples were each studied by 16 laboratories, representing 17 commercially-purchased instruments located across 8 countries, using positive- and negative-mode electrospray ionization (ESI) HRMS analysis. Despite the widely reported sensitivity of ESI experiments to sample matrix, preparation, ion source operation and instrument tuning parameters, the instruments used in this study showed relatively low variability between results for the same samples, particularly H/C and AI metrics. The variation in O/C and m/z metrics was relatively higher, although z-score graphs and %BCD demonstrated that the relative difference in general chemical composition between samples was the same for each instrument included in the study. As such, these metrics can be used for fingerprinting of DOM samples and as a benchmark for quality control in participating laboratories, with future strides in environmental science building upon these efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Investigating the impacts of solid phase extraction on dissolved organic matter optical signatures and the pairing with high‐resolution mass spectrometry data across a freshwater stream network

Abstract Advancing our understanding of dissolved organic matter (DOM) chemistry in aquatic systems necessitates the integration of data streams from multiple analytical platforms. Some measurements require pretreatment with solid phase extraction (SPE), while others are performed directly on whole water samples. Evidence has suggested that SPE will be biased against select DOM fractions, leading to concerns over the ability to establish data linkages across platforms with variable needs for SPE pretreatment, such as those from optical measurements and those that provide high‐resolution molecular information. Here, we directly addressed this concern by assessing the impact of SPE on DOM optical properties through excitation–emission matrices with parallel factor analysis (PARAFAC) for 47 samples across a stream network within a single watershed reflective of variable DOM sources. PARAFAC data was further paired with molecular information obtained by Fourier transform ion cyclotron resonance mass spectrometry (FTICR‐MS). A comparison of PARAFAC models first revealed no systematic qualitative differences in major components between whole water DOM and DOM isolated by SPE (SPE‐DOM); however, quantitative biases against select components were observed. Further linkages with FTICR‐MS data revealed that the molecular fingerprint associated with each PARAFAC component was consistent between the whole water DOM and SPE‐DOM. Our results suggest that bulk scale linkages across these analytical platforms could be inferred irrespective of the observed quantitative biases resulting from SPE for samples within this example watershed. This work represents a key step toward the systematic evaluation of linkages between optical and high‐resolution mass spectrometry datasets in freshwater lotic environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Graph Identification of Proteins in Tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins

Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying proteins in cryo-ET data we pursued a Graph Network and topologically invariant approach. Here, we report on a fast algorithm that classifies particles by searching for nuances of evolutionarily conversed motifs and the geometrical characteristics of protein structure. GRIP-Tomo 2.0 is a machine-learning pipeline that extracts interpretable topological features of protein structures within noisy experimental backgrounds. Compared to version 1.0, the new pipeline includes three upgrades that significantly improve performance including synthetic tomogram generation simulating realistic noise, graph-based persistent feature extraction as protein fingerprints, and high-performance computing acceleration. GRIP-Tomo 2.0 achieves over 90% accuracy in classifying between proteins and noise using both real and synthetic datasets which represents a foundational step toward advancing cryo-ET workflows and empowering automated visual proteomics.

Li, Chengxuan↗
DOE OSTI2023

Formation of Amorphous Carbon Multi‐Walled Nanotubes from Random Initial Configurations

Amorphous carbon nanotubes (a‐CNT) with up to four walls and sizes ranging from 200 to 3200 atoms have been simulated, starting from initial random configurations and using the Gaussian Approximation Potential. The important variables (like density, height, and diameter) required to successfully simulate a‐CNTs were predicted with the machine learning random forest technique. The width of the a‐CNT models ranged between 0.55–2 nm with an average inter‐wall spacing of 0.31 nm. The topological defects in a‐CNTs were analyzed and new defect configurations were observed. The electronic density of states and localization in these phases were discussed and delocalized electrons in the π subspace were identified as an important factor for inter‐layer cohesion. Spatial projection of the electronic conductivity favors axial transport along connecting hexagons, while non‐hexagonal parts of the network either hinder or bifurcate the electronic transport. A vibrational density of states was calculated and is potentially an experimentally comparable fingerprint of the material. The appearance of a low‐frequency radial breathing mode was discussed and the thermal conductivity at 300 K was estimated using the Green‐Kubo formula.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Multimodal, microspectroscopic speciation of legacy phosphorus in two US mid-Atlantic agricultural soils

To understand phosphorus (P) mobility in agricultural soils and its potential environmental risk, it is essential to directly measure solid phase P speciation. Often, bulk P K-edge X-ray absorption near edge structure (XANES) spectroscopy followed by linear combination fitting (LCF) is utilized to determine the solid P phases in soil. However, this method may limit results to only a few major phases. Additionally, XANES spectra for different P species may have very similar features, leading to an over- or underestimate of their contribution to LCF. Here, an improved P speciation by pairing multimodal microbeam-X-ray fluorescence (µ-XRF) mapping coupled with µ-XANES (microbeam-X-ray absorption near edge structure) analysis to directly speciate major and minor P phases on the micron scale is provided. We combined maps of both tender (P, sulfur, aluminum, and silicon) and hard energy (calcium, iron [Fe], and manganese) elements to evaluate the elemental co-locations with P. To better account for uncertainty assigning XANES peaks to individual compounds, a more quantitative fingerprinting by “spectral feature analysis” was completed. With this analysis, an R-factor is reported for the fit. These results were compared to traditional LCF. Pre-edge fitting results revealed the presence of a two-component pre-edge feature for phosphate adsorbed to ferrihydrite. Additionally, phytate co-precipitated with ferrihydrite (Phytate-Fe-Cop) had a pre-edge feature, indicating direct association with Fe. Lastly, a unique P species associated with manganese oxide was identified in the soil via multimodal mapping and µ-XANES. These results allow for better prediction of P dissolution and mobility.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Revealing Nanoscale Chemical Heterogeneities in Polycrystalline Mo-BiVO 4 Thin Films

The activity of polycrystalline thin film photoelectrodes is impacted by local variations of the material properties due to the exposure of different crystal facets and the presence of grain/domain boundaries. Here a multi-modal approach is applied to correlate nanoscale heterogeneities in chemical composition and electronic structure with nanoscale morphology in polycrystalline Mo-BiVO 4 . By using scanning transmission X-ray microscopy, the characteristic structure of polycrystalline film is used to disentangle the different X-ray absorption spectra corresponding to grain centers and grain boundaries. Comparing both spectra reveals phase segregation of V 2 O 5 at grain boundaries of Mo-BiVO 4 thin films, which is further supported by X-ray photoelectron spectroscopy and many-body density functional theory calculations. Theoretical calculations also enable to predict the X-ray absorption spectral fingerprint of polarons in Mo-BiVO 4 . After photo-electrochemical operation, the degraded Mo-BiVO 4 films show similar grain center and grain boundary spectra indicating V 2 O 5 dissolution in the course of the reaction. Overall, these findings provide valuable insights into the degradation mechanism and the impact of material heterogeneities on the material performance and stability of polycrystalline photoelectrodes.

36 MATERIALS SCIENCE↗