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

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

In Situ Detection of Amino Acids from Bacterial Biofilms and Plant Root Exudates by Liquid Microjunction Surface-Sampling Probe Mass Spectrometry

The plant rhizosphere is a complex and dynamic chemical environment where the exchange of molecular signals between plants, microbes, and fungi drives the development of the entire biological system. Exogenous compounds in the rhizosphere are known to affect plant-microbe organization, interactions between organisms, and ultimately, growth and survivability. The function of exogenous compounds in the rhizosphere is still under much investigation, specifically with respect to their roles in plant growth and development, the assembly of the associated microbial community, and the spatiotemporal distribution of molecular components. A major challenge for spatiotemporal measurements is developing a nondisruptive and nondestructive technique capable of analyzing the exogenous compounds contained within the environment. A methodology using liquid microjunction-surface sampling probe-mass spectrometry (LMJ-SSP-MS) and microfluidic devices with attached microporous membranes was developed for in situ, spatiotemporal measurement of amino acids (AAs) from bacterial biofilms and plant roots. Exuded arginine was measured from a living Pantoea YR 343 biofilm, which resulted in a chemical image indicative of biofilm growth within the device. Spot sampling along the roots of Populus trichocarpa with the LMJ-SSP-MS resulted in the detection of 15 AAs. Of note, variation in AA concentrations across the root system was observed, indicating that exudation is not homogeneous and may be linked to local rhizosphere architecture and different biological processes along the root.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Assessment of ESM Readiness Level for Exascale HPC

Advancement of Earth System Models (ESMs) is becoming increasingly challenging due to a confluence of factors including increasing model complexity – to more fully represent the earth system, increasing spatial resolution - to achieve higher accuracy by resolving fine-scale dynamical to physical, biological, and chemical processes and their interaction, increasing ensemble size - to more accurately represent predictive uncertainty, and increased computing requirements – to enable more accurate and timely weather predictions and climate projections for societal benefit. The belief by many that computing will take care of itself is no longer valid given the disruptive changes in HPC that are driving up the cost of computing, increasing the difficulty of using emerging HPC effectively, and exposing limits in parallelism, portability and scalability of the ESM applications themselves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Extracellular vesicles in β cell biology: Role of lipids in vesicle biogenesis, cargo, and intercellular signaling

Background: Type 1 diabetes (T1D) is a complex autoimmune disorder whose pathogenesis involves an intricate interplay between ß cells of the pancreatic islet, other islet cells, and cells of the immune system. Direct intercellular communication within the islet occurs via cell surface proteins and indirect intercellular communication has traditionally been as occurring via secreted proteins (e.g., endocrine hormones and cytokines). However, recent literature suggests that extracellular vesicles (EVs) secreted by ß cells constitute an additional and biologically important mechanism for transmitting signals to adjacent ß cells or immune cells. Scope of Review: This review summarizes the general mechanisms of EV formation, with a particular focus on how lipids and lipid signaling pathways influence their formation and cargo. We review the implications of EV release from ß cells for T1D pathogenesis, how EVs and their cargo might be leveraged as biomarkers of the early events leading to T1D, and how EVs might be engineered as a therapeutic candidate to counter T1D outcomes. Major Conclusions: Islet ß cells have been viewed as initiators and propagators of the cellular circuit giving rise to autoimmunity in T1D. In this context, emerging literature suggests that EVs may represent a conduit for communication that holds more comprehensive messaging about the ß cells from which they arise. As the field of EV biology advances, it opens the possibility that intervening with EV formation and cargo loading could be a novel disease-modifying approach in T1D.

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

The engineered single guide RNA structure as a biomarker for gene-editing reagent exposure

Abstract CRISPR arrays and CRISPR-associated (Cas) proteins comprise a prevalent adaptive immune system in bacteria and archaea. These systems defend against exogenous parasitic mobile genetic elements. The adaption of single effector CRISPR-Cas systems has massively facilitated gene-editing due to the reprogrammable guide RNA. The guide RNA affords little priming space for conventional PCR-based nucleic acid tests without foreknowledge of the spacer sequence. Further impeding detection of gene-editor exposure, these systems are derived from human microflora and pathogens ( Staphylococcus pyogenes , Streptococcus aureus , etc.) that contaminate human patient samples. The single guide RNA—formed from the CRISPR RNA (crRNA) and transactivating RNA (tracrRNA)—harbors a variable tetraloop sequence between the two RNA segments, complicating PCR assays. Identical single effector Cas proteins are used for gene-editing and naturally by bacteria. Antibodies raised against these Cas proteins are unable to distinguish CRISPR-Cas gene-editors from bacterial contaminant. To overcome the high potential for false positives, we have developed a DNA displacement assay to specifically detect gene-editors. We leveraged the single guide RNA structure as an engineered moiety for gene-editor exposure that does not cross-react with bacterial CRISPRs. Our assay has been validated for five common CRISPR systems and functions in complex sample matrices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Unnatural biosynthesis by an engineered microorganism with heterologously expressed natural enzymes and an artificial metalloenzyme

Synthetic biology enables microbial hosts to produce complex molecules from organisms that are rare or difficult to cultivate, but the structures of these molecules are limited to those formed by reactions of natural enzymes. The integration of artificial metalloenzymes (ArMs) that catalyse unnatural reactions into metabolic networks could broaden the cache of molecules produced biosynthetically. Here, in this work, we report an engineered microbial cell expressing a heterologous biosynthetic pathway, containing both natural enzymes and ArMs, that produces an unnatural product with high diastereoselectivity. We engineered Escherichia coli with a heterologous terpene biosynthetic pathway and an ArM containing an iridium–porphyrin complex that was transported into the cell with a heterologous transport system. We improved the diastereoselectivity and product titre of the unnatural product by evolving the ArM and selecting the appropriate gene induction and cultivation conditions. This work shows that synthetic biology and synthetic chemistry can produce, by combining natural and artificial enzymes in whole cells, molecules that were previously inaccessible to nature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Ion-selective conformational stabilization of a disordered repeats-in-toxin protein domain

Ion-binding intrinsically disordered proteins (IDPs) recruit and bind to specific metal ions to perform critical biological functions. In proteins where ion binding and structural transitions are coupled, interactions with off-target toxic metals can dramatically disrupt protein structure and function, exemplified by lead and mercury poisoning. Understanding the complex mechanisms underlying how IDPs exclude or allow binding to different ionic species is crucial for addressing the origins of metal toxicity in biological systems. Here, we elucidate mechanisms of ion selectivity in an IDP that adopts a structure upon Ca 2+ binding. We probed ion-induced conformational changes of a repeats-in-toxin (RTX) protein domain in the presence of different ion ligands—Mg 2+ , Ca 2+ , Sr 2+ , and Ba 2+ —with chemical similarities but drastically different ionic radii. RTX adopts ion-selective conformations measured by x-ray crystallography, small-angle x-ray scattering, and circular dichroism. High-resolution x-ray structures reveal that Sr 2+ induces a nearly identical RTX structure as natively binding Ca 2+ , enabled by the intrinsic flexibility and disorder of the protein. Small-angle x-ray scattering and circular dichroism indicate that smaller Mg 2+ does not induce a significant conformational change in RTX, whereas larger Ba 2+ induces a partially folded structure. These results highlight the importance of geometric constraints imposed by protein structure in determining metal ion selectivity, yielding insights into how off-target ion binding may result in protein misfolding and malfunction.

Gudinas, Alana P. [Stanford Univ., CA (United Stat↗
DOE OSTI2020

Enzymatic degradation of liquid droplets of DNA is modulated near the phase boundary

Significance Biomolecules, such as proteins or nucleic acids, can condense into liquid droplets whose biological or technological function depends on interactions with other solutes. We explore such interactions using a system of self-assembled DNA particles that forms liquid droplets which are degraded by an active solute, a restriction enzyme. We find that enzymes are able to penetrate the droplets when the liquid is poised near the phase boundary, enhancing the degradation rate and, in certain cases, causing internal bubbling. These results point to a complex interplay of phase stability and solute transport in liquid–liquid phase-separating macromolecular systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

AI/ML↗
DOE OSTI2025

A Roadmap for the Future of Systems Biology in Cancer Research

Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field’s promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, in this study, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field.

Modeling & Simulation↗
DOE OSTI2026

Brochure for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research

In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Utilizing plant synthetic biology to accelerate plant-microbe interactions research

Plant-microbe interactions are critical to ecosystem resilience and substantially influence crop production. From the perspective of plant science, two important focus areas concerning plant-microbe interactions include: 1) understanding plant molecular mechanisms involved in plant-microbe interfaces and 2) engineering plants for increasing plant disease resistance or enhancing beneficial interactions with microbes to increase their resilience to biotic and abiotic stress conditions. Molecular biology and genetics approaches have been used to investigate the molecular mechanisms underlying plant responses to various beneficial and pathogenic microbes. While these approaches are valuable for elucidating the functions of individual genes and pathways, they fall short of unraveling the complex cross-talk across pathways or systems that plants employ to respond and adapt to environmental stresses. Also, genetic engineering of plants to increase disease resistance or enhance symbiosis with microbes has mainly been attempted or conducted through targeted manipulation of single genes/pathways of plants. Recent advancements in synthetic biology tool development are paving the way for multi-gene characterization and engineering in plants in relation to plant-microbe interactions. Here, we briefly summarize the current understanding of plant molecular pathways involved in plant interactions with beneficial and pathogenic microorganisms. Then, we highlight the progress in applying plant synthetic biology to elucidate the molecular basis of plant responses to microbes, enhance plant disease resistance, engineer synthetic symbiosis, and conduct in situ microbiome engineering. Lastly, we discuss the challenges, opportunities, and future directions for advancing plant-microbe interactions research using the capabilities of plant synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hydrophobe Containing Polypeptoids Complex with Lipids and Induce Fusogenesis of Lipid Vesicles

The hydrophobic effect of alkyl group insertion into phospholipid bilayers is exploited in modifying and modulating vesicle structure. We show that amphiphilic polypeptoids (peptide mimics) with n-decyl side chains, which we term as hydrophobe-containing polypeptoids (HCPs), can insert the alkyl hydrophobes into the membrane bilayer of phospholipid-based vesicles. Such insertion leads to disruption of the liposomes and the formation of HCP–lipid complexes that are colloidally stable in aqueous solution. Interestingly, when these complexes are added to fresh liposomes, remnant uncomplexed hydrophobes (the n-decyl groups) bridge liposomes and fuse them. The fusion leads to the engulfing of liposomes and the formation of multilayered vesicles. The morphology of the liposome system can be changed from stopping fusion and forming clustered vesicles to the continued formation of multilayered liposomes simply by controlling the amount of the HCP–lipid complex added. The entire procedure occurs in aqueous systems without the addition of any other solvents. There are several implications to these observations including the biological relevance of mimicking fusogenic proteins such as the SNARE proteins and the development of new drug delivery technologies to impact delivery to cell organelles.

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

Motility of Synthetic Cells from Engineered Lipids

Synthetic cells are artificial systems that resemble natural cells. Significant efforts have been made over the years to construct synthetic protocells that can mimic biological mechanisms and perform various complex processes. These include compartmentalization, metabolism, energy supply, communication, and gene reproduction. Cell motility is also of great importance, as nature uses elegant mechanisms for intracellular trafficking, immune response, and embryogenesis. In this review, we discuss the motility of synthetic cells made from lipid vesicles and relevant molecular mechanisms. Synthetic cell motion may be classified into surface-based or solution-based depending on whether it involves interactions with surfaces or movement in fluids. Collective migration behaviors have also been demonstrated. The swarm motion requires additional mechanisms for intercellular signaling and directional motility that enable communication and coordination among the synthetic vesicles. In addition, intracellular trafficking for molecular transport has been reconstituted in minimal cells with the help of DNA nanotechnology. These efforts demonstrate synthetic cells that can move, detect, respond, and interact. Finally, we envision that new developments in protocell motility will enhance our understanding of biological processes and be instrumental in bioengineering and therapeutic applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Signal Differentiation of Moving Magnetic Nanoparticles for Enhanced Biodetection and Diagnostics

Magnetic nanoparticles are extensively utilized as markers/signal labelling in various biomedical applications. Detecting and distinguishing magnetic signals from similarly sized moving magnetic nanoparticles in microfluidic systems is crucial yet challenging for biosensing. In this study, we have developed an original method to detect and differentiate magnetic signals from moving superparamagnetic (SPM) and ferrimagnetic (FM) nanoparticles of comparable sizes. Our approach utilizes a highly sensitive magnetic-coil-based sensor that harnesses the combined effects of giant magnetoimpedance (GMI) and an LC-resonance circuit, offering performance superior to that of conventional GMI sensors. Iron oxide nanoparticles, which have similar particle sizes but differing coercivities (zero for SPM and non-zero for FM) or similar zero coercivities but differing particle sizes, flow through the magnetic coil at controlled velocities. Their distinct effects are analyzed through changes in the complex impedance of the sensing system. Our findings provide a unique pathway for utilizing SPM and FM nanoparticles as innovative magnetic markers to identify specific biological entities, thereby expanding their potential applications.

Hwang, Kee Young↗
DOE OSTI2020

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. 1. Theory, Algorithm, and Performance

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal density functional theory (DFT) and thereby furnish a more accurate and reliable description of the underlying electronic structure in systems throughout biology, chemistry, physics, and materials science. However, the high computational cost associated with the evaluation of all required EXX quantities has limited the applicability of hybrid DFT in the treatment of large molecules and complex condensed-phase materials. To overcome this limitation, we describe a linear-scaling approach that utilizes a local representation of the occupied orbitals (e.g., maximally localized Wannier functions (MLWFs)) to exploit the sparsity in the real-space evaluation of the quantum mechanical exchange interaction in finite-gap systems. In this work, we present a detailed description of the theoretical and algorithmic advances required to perform MLWF-based ab initio molecular dynamics (AIMD) simulations of large-scale condensed-phase systems of interest at the hybrid DFT level. We focus our theoretical discussion on the integration of this approach into the framework of Car–Parrinello AIMD, and highlight the central role played by the MLWF-product potential (i.e., the solution of Poisson’s equation for each corresponding MLWF-product density) in the evaluation of the EXX energy and wave function forces. We then provide a comprehensive description of the exx algorithm implemented in the open-source Quantum ESPRESSO program, which employs a hybrid MPI/OpenMP parallelization scheme to efficiently utilize the high-performance computing (HPC) resources available on current- and next-generation supercomputer architectures. Furthermore, this is followed by a critical assessment of the accuracy and parallel performance (e.g., strong and weak scaling) of this approach when AIMD simulations of liquid water are performed in the canonical (NVT) ensemble. With access to HPC resources, we demonstrate that exx enables hybrid DFT-based AIMD simulations of condensed-phase systems containing 500–1000 atoms (e.g., (H₂O)₂₅₆) with a wall time cost that is comparable to that of semilocal DFT. In doing so, exx takes us one step closer to routinely performing AIMD simulations of complex and large-scale condensed-phase systems for sufficiently long time scales at the hybrid DFT level of theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Capturing Ring Opening in Photoexcited Enolic Acetylacetone upon Hydrogen Bond Dissociation by Ultrafast Electron Diffraction

Photoinduced biological and chemical reactions are often based on key structural transformations of a molecule driven across multiple electronic states. Acetylacetone (AcAc) is a prototypical system for complex chemical pathways involving several conical intersections (CI) and singlet–triplet intersystem crossings (ISC) characterized by distinct geometries. In the gas phase, AcAc is predominantly in a planar ring-like enolic form stabilized by a strong intramolecular O–H···O hydrogen bond. Following excitation into the S 2 (ππ*) state at 266 nm, acetylacetone undergoes rapid internal conversion followed by intersystem crossing. Such relaxation pathways are associated with structural changes including ring opening, deplanarization, and bond elongation. In this work, ultrafast electron diffraction (UED) at the SLAC MeV-UED setup is employed as a direct structural probe with a time resolution of 160 fs. Together with trajectory surface hopping simulations, analysis of the UED data provides a new perspective on the early time nuclear dynamics in acetylacetone. Specifically, AcAc is observed to undergo ring opening, deplanarization, and bond elongation all within the first 700 fs after photoexcitation. The monitored dynamics is associated mainly with the nuclear motion on the S 1 potential energy surface, formed after very rapid transfer from S 2 to S 1 , allowing AcAc to reach the conical intersection to intersystem crossing. Such time scales of nuclear motion are contrasted with the time scales of electronic transitions in AcAc that were previously characterized with spectroscopic methods, specifically internal conversion (<100 fs) and intersystem crossing (∼1.5 ps).

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

Non-equilibrium signal integration in hydrogels

Abstract Materials that perform complex chemical signal processing are ubiquitous in living systems. Their synthetic analogs would transform developments in biomedicine, catalysis, and many other areas. By drawing inspiration from biological signaling dynamics, we show how simple hydrogels have a previously untapped capacity for non-equilibrium chemical signal processing and integration. Using a common polyacrylic acid hydrogel, with divalent cations and acid as representative stimuli, we demonstrate the emergence of non-monotonic osmosis-driven spikes and waves of expansion/contraction, as well as traveling color waves. These distinct responses emerge from different combinations of rates and sequences of arriving stimuli. A non-equilibrium continuum theory we developed quantitatively captures the non-monotonic osmosis-driven deformation waves and determines the onset of their emergence in terms of the input parameters. These results suggest that simple hydrogels, already built into numerous systems, have a much larger sensing space than currently employed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Motility of acoustically powered micro-swimmers in a liquid crystalline environment

Suspensions of microswimmers in liquid crystals demonstrate remarkably complex dynamics and serve as a model system for studying active nematics. So far, experimental realization of microswimmers suspended in liquid crystalline media has relied on biological microorganisms that impose strict limitations on the compatible media and makes it difficult to regulate activity. Here, we demonstrate that acoustically powered bubble microswimmers can efficiently self-propel in a lyotropic liquid crystal. The velocity of the swimmers is controlled by the amplitude of the acoustic field. Histograms of swimming directions with respect to the local nematic field reveal a bimodal distribution: the swimmers tend to either fully align with or swim perpendicular to the director field of the liquid crystal, occasionally switching between these two states. The bubble-induced streaming from a swimmer locally melts the liquid crystal and produces topological defects at the tail of the swimmer. Further, we show that the defect proliferation rate increases with the angle between the swimmer's velocity and the local orientation of the director field.

36 MATERIALS SCIENCE↗