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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Lessons from combined experimental and theoretical examination of the FTIR and 2D-IR spectroelectrochemistry of the amide I region of cytochrome c</em

Amide I difference spectroscopy is widely used to investigate protein function and structure changes. In this article, we show that the common approach of assigning features in amide I difference signals to distinct secondary structure elements in many cases may not be justified. Evidence comes from Fourier transform infrared (FTIR) and 2D-IR spectroelectrochemistry of the protein cytochrome c in the amide I range, in combination with computational spectroscopy based on molecular dynamics (MD) simulations. This combination reveals that each secondary structure unit, such as an alpha-helix or a beta-sheet, exhibits broad overlapping contributions, usually spanning a large part of the amide I region, which in the case of difference absorption experiments (such as in FTIR spectroelectrochemistry) may lead to intensity-compensating and even sign-changing contributions. We use cytochrome c as the test case, as this small electron-transferring redox-active protein contains different kinds of secondary structure units. Upon switching its redox-state, the protein exhibits a different charge distribution while largely retaining its structural scaffold. Our theoretical analysis suggests that the change in charge distribution contributes to the spectral changes and that structural changes are small. However, in order to confidently interpret FTIR amide I difference signals in cytochrome c and proteins in general, MD simulations in combination with additional experimental approaches such as isotope labeling, the insertion of infrared labels to selectively probe local structural elements will be required. In case these data are not available, a critical assessment of previous interpretations of protein amide I 1D- and 2D-IR difference spectroscopy data is warranted.

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

Calcium transport into the cells of the sea urchin larva in relation to spicule formation

We investigated the manner in which the sea urchin larva takes up calcium from its body cavity into the primary mesenchymal cells (PMCs) that are responsible for spicule formation. We used the membrane-impermeable fluorescent dye calcein and alexa-dextran, with or without a calcium channel inhibitor, and imaged the larvae in vivo with selective-plane illumination microscopy. Both fluorescent molecules are taken up from the body cavity into the PMCs and ectoderm cells, where the two labels are predominantly colocalized in particles, whereas the calcium-binding calcein label is mainly excluded from the endoderm and is concentrated in the spicules. The presence of vesicles and vacuoles inside the PMCs that have openings through the plasma membrane directly to the body cavity was documented using high-resolution cryo-focused ion beam-SEM serial imaging. Some of the vesicles and vacuoles are interconnected to form large networks. We suggest that these vacuolar networks are involved in direct sea water uptake. We conclude that the calcium pathway from the body cavity into cells involves nonspecific endocytosis of sea water with its calcium.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Cryogenic single-molecule fluorescence annotations for electron tomography reveal in situ organization of key proteins in Caulobacter

Superresolution fluorescence microscopy and cryogenic electron tomography (CET) are powerful imaging methods for exploring the subcellular organization of biomolecules. Superresolution fluorescence microscopy based on covalent labeling highlights specific proteins and has sufficient sensitivity to observe single fluorescent molecules, but the reconstructions lack detailed cellular context. CET has molecular-scale resolution but lacks specific and nonperturbative intracellular labeling techniques. Here, we describe an imaging scheme that correlates cryogenic single-molecule fluorescence localizations with CET reconstructions. Our approach achieves single-molecule localizations with an average lateral precision of 9 nm, and a relative registration error between the set of localizations and CET reconstruction of ∼30 nm. We illustrate the workflow by annotating the positions of three proteins in the bacterium Caulobacter crescentus : McpA, PopZ, and SpmX. McpA, which forms a part of the chemoreceptor array, acts as a validation structure by being visible under both imaging modalities. In contrast, PopZ and SpmX cannot be directly identified in CET. While not directly discernable, PopZ fills a region at the cell poles that is devoid of electron-dense ribosomes. We annotate the position of PopZ with single-molecule localizations and confirm its position within the ribosome excluded region. We further use the locations of PopZ to provide context for localizations of SpmX, a low-copy integral membrane protein sequestered by PopZ as part of a signaling pathway that leads to an asymmetric cell division. Our correlative approach reveals that SpmX localizes along one side of the cell pole and its extent closely matches that of the PopZ region.

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

Intracellular pathways for lignin catabolism in white-rot fungi

White-rot fungi, the most efficient organisms at breaking down lignin from wood in Nature, utilize lignin degradation products as a carbon source. 15 Lignin is a biopolymer found in plant cell walls that accounts for 30% of the organic carbon in the biosphere. White-rot fungi (WRF) are considered the most efficient organisms at degrading lignin in Nature. While lignin depolymerization by WRF has been exhaustively studied, the possibility that WRF are able to utilize lignin as a carbon source is still a matter of controversy. Here we employ 13C-labeling and systems biology approaches to demonstrate that two WRF, Trametes versicolor and Gelatoporia subvermispora, funnel lignin-derived aromatic compounds into central carbon metabolism via intracellular catabolic pathways. These results provide insights into global carbon cycling in 20 soil ecosystems, and furthermore establishes a foundation for employing WRF in simultaneous lignin depolymerization and bioconversion to bioproducts – a key step towards enabling a sustainable bioeconomy.

Del Cerro, Carlos↗
DOE OSTI2022

Single molecule tracking of bacterial cell surface cytochromes reveals dynamics that impact long-distance electron transport

Using a series of multiheme cytochromes, the metal-reducing bacterium Shewanella oneidensis MR-1 can perform extracellular electron transfer (EET) to respire redox-active surfaces, including minerals and electrodes outside the cell. While the role of multiheme cytochromes in transporting electrons across the cell wall is well established, these cytochromes were also recently found to facilitate long-distance (micrometer-scale) redox conduction along outer membranes and across multiple cells bridging electrodes. Recent studies proposed that long-distance conduction arises from the interplay of electron hopping and cytochrome diffusion, which allows collisions and electron exchange between cytochromes along membranes. However, the diffusive dynamics of the multiheme cytochromes have never been observed or quantified in vivo, making it difficult to assess their hypothesized contribution to the collision-exchange mechanism. Here, we use quantum dot labeling, total internal reflection fluorescence microscopy, and single-particle tracking to quantify the lateral diffusive dynamics of the outer membrane-associated decaheme cytochromes MtrC and OmcA, two key components of EET in S. oneidensis. We observe confined diffusion behavior for both quantum dot-labeled MtrC and OmcA along cell surfaces (diffusion coefficients D MtrC = 0.0192 ± 0.0018 µm 2 /s, D OmcA = 0.0125 ± 0.0024 µm 2 /s) and the membrane extensions thought to function as bacterial nanowires. We find that these dynamics can trace a path for electron transport via overlap of cytochrome trajectories, consistent with the long-distance conduction mechanism. The measured dynamics inform kinetic Monte Carlo simulations that combine direct electron hopping and redox molecule diffusion, revealing significant electron transport rates along cells and membrane nanowires.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Harnessing quantum light for microscopic biomechanical imaging of cells and tissues

The biomechanical properties of cells and tissues play an important role in our fundamental understanding of the structures and functions of biological systems at both the cellular and subcellular levels. Recently, Brillouin microscopy, which offers a label-free spectroscopic means of assessing viscoelastic properties in vivo, has emerged as a powerful way to interrogate those properties on a microscopic level in living tissues. However, susceptibility to photodamage and photobleaching, particularly when high-intensity laser beams are used to induce Brillouin scattering, poses a significant challenge. This article introduces a transformative approach designed to mitigate photodamage in biological and biomedical studies, enabling nondestructive, label-free assessments of mechanical properties in live biological samples. By leveraging quantum-light-enhanced stimulated Brillouin scattering (SBS) imaging contrast, the signal-to-noise ratio is significantly elevated, thereby increasing sample viability and extending interrogation times without compromising the integrity of living samples. The tangible impact of this methodology is evidenced by a notable three-fold increase in sample viability observed after subjecting the samples to three hours of continuous squeezed-light illumination, surpassing the traditional coherent light-based approaches. The quantum-enhanced SBS imaging holds promise across diverse fields, such as cancer biology and neuroscience where preserving sample vitality is of paramount significance. By mitigating concerns regarding photodamage and photobleaching associated with high-intensity lasers, this technological breakthrough expands our horizons for exploring the mechanical properties of live biological systems, paving the way for an era of research and clinical applications.

Li, Tian↗
DOE OSTI2025

Cartesian equivariant representations for learning and understanding molecular orbitals

Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree–Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network (CEONET), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine-learned potentials. We find that CEONET performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONET representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONET to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONET to infer multireference character via the orbital entropy paves the way toward the machine-learned selection of active spaces.

chemical reactions↗
DOE OSTI2016

Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry

Current proteomics approaches are comprised of both broad discovery measurements as well as more quantitative targeted measurements. These two different measurement types are used to initially identify potentially important proteins (e.g., candidate biomarkers) and then enable improved quantification for a limited number of selected proteins. However, both approaches suffer from limitations, particularly the lower sensitivity, accuracy, and quantitation precision for discovery approaches compared to targeted approaches, and the limited proteome coverage provided by targeted approaches. Herein, we describe a new proteomics approach that allows both discovery and targeted monitoring (DTM) in a single analysis using liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS). In DTM, heavy labeled peptides for target ions are spiked into tryptic digests and both the labeled and unlabeled peptides are broadly detected using LC-IMS-MS instrumentation, allowing the benefits of discovery and targeted approaches. To understand the possible improvement of the DTM approach, it was compared to LC-MS broad measurements using an accurate mass and time tag database and selected reaction monitoring (SRM) targeted measurements. The DTM results yielded greater peptide/protein coverage and a significant improvement in the detection of lower abundance species compared to LC-MS discovery measurements. DTM was also observed to have similar detection limits as SRM for the targeted measurements indicating its potential for combining the discovery and targeted approaches.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2023

Restoring permutational invariance in the Jordan–Wigner transformation

The Jordan–Wigner transformation is a powerful tool for converting systems of spins into systems of fermions, or vice versa. While this mapping is exact, the transformation itself depends on the labelling of the spins. One consequence of this dependence is that approximate solutions of a Jordan–Wigner-transformed Hamiltonian may depend on the (physically inconsequential) labelling of the spins. In this work, we turn to an extended Jordan–Wigner transformation which remedies this problem and which may also introduce some correlation atop the Hartree–Fock solution of a transformed spin Hamiltonian. We demonstrate that this extended Jordan–Wigner transformation can be thought of as arising from a unitary version of the Lie algebraic similarity transformation (LAST) theory. Here, we show how these ideas, particularly in combination with the standard (non-unitary) version of LAST, can provide a potentially powerful tool for the treatment of the XXZ and J 1 –J 2 Heisenberg Hamiltonians.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

EVAP: A two-photon imaging tool to study conformational changes in endogenous Kv2 channels in live tissues

A primary goal of molecular physiology is to understand how conformational changes of proteins affect the function of cells, tissues, and organisms. Here, we describe an imaging method for measuring the conformational changes of the voltage sensors of endogenous ion channel proteins within live tissue, without genetic modification. We synthesized GxTX-594, a variant of the peptidyl tarantula toxin guangxitoxin-1E, conjugated to a fluorophore optimal for two-photon excitation imaging through light-scattering tissue. We term this tool EVAP (Endogenous Voltage-sensor Activity Probe). GxTX-594 targets the voltage sensors of Kv2 proteins, which form potassium channels and plasma membrane–endoplasmic reticulum junctions. GxTX-594 dynamically labels Kv2 proteins on cell surfaces in response to voltage stimulation. To interpret dynamic changes in fluorescence intensity, we developed a statistical thermodynamic model that relates the conformational changes of Kv2 voltage sensors to degree of labeling. We used two-photon excitation imaging of rat brain slices to image Kv2 proteins in neurons. We found puncta of GxTX-594 on hippocampal CA1 neurons that responded to voltage stimulation and retain a voltage response roughly similar to heterologously expressed Kv2.1 protein. Our findings show that EVAP imaging methods enable the identification of conformational changes of endogenous Kv2 voltage sensors in tissue.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Using active learning to improve quasar identification for the DESI spectra processing pipeline

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, with the goal of improving classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approximately 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we achieve similar performance to the previously trained weights file in completeness and purity calculated on the validation dataset but do so with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

Machine learning↗
DOE OSTI2023

Deep learning symmetries and their Lie groups, algebras, and subalgebras from first principles

Abstract We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks to model the symmetry transformations and the corresponding generators. The constructed loss functions ensure that the applied transformations are symmetries and the corresponding set of generators forms a closed (sub)algebra. Our procedure is validated with several examples illustrating different types of conserved quantities preserved by symmetry. In the process of deriving the full set of symmetries, we analyze the complete subgroup structure of the rotation groups SO (2), SO (3), and SO (4), and of the Lorentz group S O ( 1 , 3 ) . Other examples include squeeze mapping, piecewise discontinuous labels, and SO (10), demonstrating that our method is completely general, with many possible applications in physics and data science. Our study also opens the door for using a machine learning approach in the mathematical study of Lie groups and their properties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks

Modern neural networks (NNs) often do not generalize well in the presence of a ‘covariate shift’; that is, in situations where the training and test data distributions differ, but the conditional distribution of classification labels given the data remains unchanged. In such cases, NN generalization can be reduced to a problem of learning more robust, domain-invariant features. Domain adaptation (DA) methods include a broad range of techniques aimed at achieving this; however, these methods have struggled with the need for extensive hyperparameter tuning, which then incurs significant computational costs. In this work, we introduce SInkhorn Dynamic Domain Adaptation (SIDDA), an out-of-the-box DA training algorithm built upon the Sinkhorn divergence, that can achieve effective domain alignment with minimal hyperparameter tuning and computational overhead. We demonstrate the efficacy of our method on multiple simulated and real datasets of varying complexity, including simple shapes, handwritten digits, real astronomical observations, and remote sensing data. These datasets exhibit covariate shifts due to noise, blurring, differences between telescopes, and variations in imaging wavelengths. SIDDA is compatible with a variety of NN architectures, and it works particularly well in improving classification accuracy and model calibration when paired with symmetry-aware equivariant NNs (ENNs). We find that SIDDA consistently enhances the generalization capabilities of NNs, achieving up to a ${\approx}40\%$ improvement in classification accuracy on unlabeled target data, while also providing a more modest performance gain of $\lesssim 1\%$ on labeled source data. We also study the efficacy of DA on ENNs with respect to the varying group orders of the dihedral group DN, and find that the model performance improves as the degree of equivariance increases. Finally, if SIDDA achieves proper domain alignment, it also enhances model calibration on both source and target data, with the most significant gains in the unlabeled target domain—achieving over an order of magnitude improvement in the expected calibration error and Brier score. SIDDA’s versatility across various NN models and datasets, combined with its automated approach to domain alignment, has the potential to significantly advance multi-dataset studies by enabling the development of highly generalizable models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Advancements in the application of NanoSIMS and Raman microspectroscopy to investigate the activity of microbial cells in soils

The combined approach of incubating environmental samples with stable isotope-labeled substrates followed by single-cell analyses through high-resolution secondary ion mass spectrometry (NanoSIMS) or Raman microspectroscopy provides insights into the in situ function of microorganisms. This approach has found limited application in soils presumably due to the dispersal of microbial cells in a large background of particles. We developed a pipeline for the efficient preparation of cell extracts from soils for subsequent single-cell methods by combining cell detachment with separation of cells and soil particles followed by cell concentration. The procedure was evaluated by examining its influence on cell recoveries and microbial community composition across two soils. This approach generated a cell fraction with considerably reduced soil particle load and of sufficient small size to allow single-cell analysis by NanoSIMS, as shown when detecting active N2-fixing and cellulose-responsive microorganisms via 15 N2 and 13 C-UL-cellulose incubations, respectively. The same procedure was also applicable for Raman microspectroscopic analyses of soil microorganisms, assessed via microcosm incubations with a 13 C-labeled carbon source and deuterium oxide (D2O, a general activity marker). Lastly, the described sample preparation procedure enables single-cell analysis of soil microorganisms using NanoSIMS and Raman microspectroscopy, but should also facilitate single-cell sorting and sequencing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Primary and heterotrophic productivity relate to multikingdom diversity in a hypersaline mat

Benthic microbial ecosystems are widespread yet knowledge gaps still remain on the relationships between the diversity of species across kingdoms and productivity. Here, we ask two fundamental questions: (i) How does species diversity relate to the rates of primary and heterotrophic productivity? (ii) How do diel variations in light-energy inputs influence productivity and microbiome diversity? To answer these questions, microbial mats from a magnesium sulfate hypersaline lake were used to establish microcosms. Both the number and relatedness between bacterial and eukaryotic taxa in the microbiome were assayed via amplicon-based sequencing of 16S and 18S rRNA genes over two diel cycles. These results correlated with biomass productivity obtained from substrate-specific 13 C stable isotope tracers that enabled comparisons between primary and heterotrophic productivity. Both bacterial and eukaryotic species richness and evenness were related only to the rates of 13 C-labeled glucose and acetate biomass incorporation. Interestingly, measures of these heterotrophic relationships changed from positive and negative correlations depending on carbon derived from glucose or acetate, respectively. The bacterial and eukaryotic diversity of this ecosystem is also controlled, in part, from energy constraints imposed by changing irradiance over a diel cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Increasing aggregate size reduces single-cell organic carbon incorporation by hydrogel-embedded wetland microbes

Abstract Microbial degradation of organic carbon in sediments is impacted by the availability of oxygen and substrates for growth. To better understand how particle size and redox zonation impact microbial organic carbon incorporation, techniques that maintain spatial information are necessary to quantify elemental cycling at the microscale. In this study, we produced hydrogel microspheres of various diameters (100, 250, and 500 μm) and inoculated them with an aerobic heterotrophic bacterium isolated from a freshwater wetland (Flavobacterium sp.), and in a second experiment with a microbial community from an urban lacustrine wetland. The hydrogel-embedded microbial populations were incubated with 13C-labeled substrates to quantify organic carbon incorporation into biomass via nanoSIMS. Additionally, luminescent nanosensors enabled spatially explicit measurements of oxygen concentrations inside the microspheres. The experimental data were then incorporated into a reactive-transport model to project long-term steady-state conditions. Smaller (100 μm) particles exhibited the highest microbial cell-specific growth per volume, but also showed higher absolute activity near the surface compared to the larger particles (250 and 500 μm). The experimental results and computational models demonstrate that organic carbon availability was not high enough to allow steep oxygen gradients and as a result, all particle sizes remained well-oxygenated. Our study provides a foundational framework for future studies investigating spatially dependent microbial activity in aggregates using isotopically labeled substrates to quantify growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structural and Morphological Characteristics of Rare Earth Element-based MAX Phase and MXene

There has been a tremendous effort for the synthesis of crystalline MAX phases—a family of transition metal carbides and nitrides with a chemical formula of Mn+1AXn, where M is an early transition metal, A belongs to groups XIII – XVI in the periodic table, X is either C or N, and n can be in between 1 and 3. Furthermore, these materials have shown thermal and electrical conductance and have low density and high stiffness which open broad aspects of practical applications. Recently double transition metal MAX phases (M’ 2/3 M’’ 1/3 ) 2 AX - out-of-plane ordered, labeled as o-MAX and in-plane ordered, labeled as i-MAX, were reported. However, there are few reports on the synthesis and characterization of i-MAX and the corresponding MXenes. In this work, we synthesized Molybdenum-based rare earth containing i-MAX phase using arc melt technique. We then removed “A” element of i-MAX phase by acid treatment and sonication which resulted in two-dimensional (2D) transition metal carbides called MXenes. These MXenes offer high conductivity, hydrophilicity, magnetism, good dispersion ability in solvents, mechanical stability, and structural diversity with at least 100 stoichiometric MXene compositions and are recognized as multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗