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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Real-time bioelectronic sensing of environmental contaminants

Real-time chemical sensing is crucial for applications in environmental and health monitoring. Biosensors can detect a variety of molecules through genetic circuits that use these chemicals to trigger the synthesis of a coloured protein, thereby producing an optical signal. However, the process of protein expression limits the speed of this sensing to approximately half an hour, and optical signals are often difficult to detect in situ. Here we combine synthetic biology and materials engineering to develop biosensors that produce electrical readouts and have detection times of minutes. Here we programmed Escherichia coli to produce an electrical current in response to specific chemicals using a modular, eight-component, synthetic electron transport chain. As designed, this strain produced current following exposure to thiosulfate, an anion that causes microbial blooms, within 2 min. This amperometric sensor was then modified to detect an endocrine disruptor. The incorporation of a protein switch into the synthetic pathway and encapsulation of the bacteria with conductive nanomaterials enabled the detection of the endocrine disruptor in urban waterway samples within 3 min. Our results provide design rules to sense various chemicals with mass-transport-limited detection times and a new platform for miniature, low-power bioelectronic sensors that safeguard ecological and human health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Recent advances in nano-scale spatial resolution x-ray microscopy instrumentation at NSLS-II

X-ray microscopy is an invaluable and powerful characterization tool applied in many scientific fields, such as materials science, biology, environmental science, and energy research. In recent years it has been driven by rapid developments of novel technologies and systems resulting in imaging experiments elucidating structural inhomogeneities and chemical reactions at the nanometer scale. To obtain high spatial resolution comprehensive chemical and structural information, an X-ray microscope must be equipped with adequate capabilities and allow for simultaneous acquisition of multiple datasets. In recent years, a number of X-ray microscopes have been designed, constructed, and commissioned at NSLS-II. Here we provide an overview of the microscopy instrumentation development program at NSLS-II and specifically focus on the multilayer Laue lens-based hard X-ray nanoprobe optimized for ~10 nm spatial resolution imaging, its current status, and future upgrades along with recently constructed Kirkpatrick-Baez based scanning microscope designed for ~100 nm spatial resolution experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Less Is More: Oligomer Extraction and Hydrothermal Annealing Increase PDMS Adhesion Forces for Materials Studies and for Biology-Focused Microfluidic Applications

Cues in the micro-environment are key determinants in the emergence of complex cellular morphologies and functions. Primary among these is the presence of neighboring cells that form networks. For high-resolution analysis, it is crucial to develop micro-environments that permit exquisite control of network formation. This is especially true in cell science, tissue engineering, and clinical biology. We introduce a new approach for assembling polydimethylsiloxane (PDMS)-based microfluidic environments that enhances cell network formation and analyses. We report that the combined processes of PDMS solvent-extraction and hydrothermal annealing create unique conditions that produce high-strength bonds between solvent-extracted PDMS (E-PDMS) and glass—properties not associated with conventional PDMS. Extraction followed by hydrothermal annealing removes unbound oligomers, promotes polymer cross-linking, facilitates covalent bond formation with glass, and retains the highest biocompatibility. Herein, our extraction protocol accelerates oligomer removal from 5 to 2 days. Resulting microfluidic platforms are uniquely suited for cell-network studies owing to high adhesion forces, effectively corralling cellular extensions and eliminating harmful oligomers. We demonstrate the simple, simultaneous actuation of multiple microfluidic domains for invoking ATP- and glutamate-induced Ca 2+ signaling in glial-cell networks. These E-PDMS modifications and flow manipulations further enable microfluidic technologies for cell-signaling and network studies as well as novel applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sampling Rare Events in Aqueous Systems Using Molecular Simulations

Birth of a new distinct phase is a phenomenon encountered in a myriad of processes, and has wide ranging consequences in material processing, biological self-assembly, separations and several other processes. Several phase transitions are nucleation driven. The nucleation events occur over nanosecond timescales and involve hundreds to thousands of molecules. These length and timescales are difficult to access in experiments, thereby making experimental studies of nucleation challenging. On the other hand, molecular simulations sample the nanosecond and nanometer scales making them ideal to study nucleation. However, nucleation is a rare event, meaning that the waiting time to observe one nucleation event is significant. This makes simulation studies of rare events challenging. The project focused on a multi-pronged approach to address such challenges to develop the next generation rare event sampling methods for molecular simulations. The key outcomes of our work include developing more effective methods for sampling rare events, utilizing machine learning to better elucidate nucleation mechanisms, development of software for easy implementation of the methodologies, and applications of the methods to realistic systems to push the method applicability beyond model systems. Overall, this work has enabled pushing the frontiers of molecular simulations to study rare events with a focus on nucleation in aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physical Models from Physical Templates Using Biocompatible Liquid Crystal Elastomers as Morphologically Programmable Inks For 3D Printing

Advanced manufacturing has received considerable attention as a tool for the fabrication of cell scaffolds however, finding ideal biocompatible and biodegradable materials that fit the correct parameters for 3D printing and guide cells to align remain a challenge. Herein, a photocrosslinkable smectic-A (Sm-A) liquid crystal elastomer (LCE) designed for 3D printing is presented, that promotes cell proliferation but most importantly induces cell anisotropy. The LCE-based bio-ink allows the 3D duplication of a highly complex brain structure generated from an animal model. Vascular tissue models are generated from fluorescently stained mouse tissue spatially imaged using confocal microscopy and subsequently processed to create a digital 3D model suitable for printing. The 3D structure is reproduced using a Digital Light Processing (DLP) stereolithography (SLA) desktop 3D printer. Synchrotron Small-Angle X-ray Diffraction (SAXD) data reveal a strong alignment of the LCE layering within the struts of the printed 3D scaffold. The resultant anisotropy of the LCE struts is then shown to direct cell growth. This study offers a simple approach to produce model tissues built within hours that promote cellular alignment.

3D cell culture↗
DOE OSTI2026

Learning neural representations for X-ray ptychography reconstruction with unknown probes

X-ray ptychography provides exceptional nanoscale resolution and is widely applied in materials science, biology, and nanotechnology. However, its full potential is constrained by the critical challenge of accurately reconstructing images when the illuminating probe is unknown. Conventional iterative methods and deep learning approaches are often suboptimal, particularly under the low-signal conditions inherent to low-dose and high-speed experiments. These limitations compromise reconstruction fidelity and restrict the broader adoption of the technique. In this work, we introduce the Ptychographic Implicit Neural Representation (PtyINR), a self-supervised framework that simultaneously addresses the object- and probe-recovery problem. By parameterizing both as continuous neural representations, PtyINR performs end-to-end reconstruction directly from raw diffraction patterns without requiring any pre-characterization of the probe. Extensive evaluations demonstrate that PtyINR achieves superior reconstruction quality on both simulated and experimental data, with remarkable robustness under challenging low-signal conditions. Furthermore, PtyINR offers a generalizable, physics-informed framework for addressing probe-dependent inverse problems, making it applicable to a wide range of computational microscopy problems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

2022 AI Testbed Expeditions Report

By exploiting the coherent properties of a light source, coherent diffraction imaging (CDI) is able to obtain the sample image at a nanoscale resolution using the measured diffraction pattern. Bragg Coherent Diffraction Imaging (BCDI) has become valuable for recovering the displacement and strain field of crystals, providing a valuable tool in material science and solid-state physics. X-ray ptychography is another emerging CDI technique that can produce a high-resolution image of the extended sample and has become popular in many research areas (e.g., materials science, biology, electronics, and optics characterization). CDI including BCDI and ptychography has become an established technique in Synchrotron Facilities including the Advanced Photon Source (APS) and will greatly benefit from the 100x coherent flux increase of the upcoming APS Upgrade (APSU). The current image formation process in CDI employs iterative phase retrieval algorithms, which is a time-consuming and computationally expensive process. Especially after APSU, the traditional iterative methods will not be able to match the experimental data acquisition speed. We employ deep learning (DL) approach to replace the iterative approaches, therefore allowing hundreds of times faster recovery of the object. We developed AutoPhaseNN, a DL-based approach which learns to solve the inverse problem without labeled data. Taking 3D BCDI as a representative technique, AutoPhaseNN has been demonstrated to be one hundred times faster than traditional iterative phase retrieval methods while providing comparable image quality. The current network is trained with 64 x 64 x 64 data size, to achieve higher resolution imaging, we will need to scale the network to input and train/infer 3D arrays of size 256 x 256 x 256 (today) and of size 2560x2560x2560 (APSU). However, the scalability of the network is restricted due to the memory-intensive training process. To perform the training for a 256 x 256 x 256 data size, the required memory exceeds the capacity of the current machine. In this project, we explore using Sambanova system to train the network for the direct data inversion for CDI.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Probing Noncovalent Interaction Strengths of Host-Guest Complexes Using Negative Ion Photoelectron Spectroscopy

Noncovalent interactions (NCIs) are crucial for the formation and stability of host-guest complexes, which have wide-ranging implications across various fields, including biology, chemistry, materials science, pharmaceuticals, and environmental science. However, since NCIs are relatively weak and sensitive to bulk perturbation, direct and accurate measurement of their absolute strength has always been a significant challenge. This concept article aims to demonstrate the gas-phase electrospray ionization (ESI)-negative ion photoelectron spectroscopy (NIPES) as a direct and precise technique to measure the absolute interaction strength, probe nature of NCIs, and reveal the electronic structural information for host-guest complexes. Here, our recent studies in investigating various host-guest complexes that involve various types of NCIs such as anion–π, (di)hydrogen bonding, charge-separated ionic interactions, are overviewed. Finally, a summary and outlook are provided for this field.

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

Chain trajectories, domain shapes, and terminal boundaries in block copolymers

The packing geometry of macromolecules in complex mesophases is of key importance to self-organization in synthetic and biological soft materials. While approximate or heuristic models rely on often-untested assumptions about how flexible molecules “fit in” to distinct locations of complex assemblies, physical assemblies derive from ensembles of fluctuating conformations, obscuring the connection between mesophase geometry and the underlying arrangements. Here, we present an approach to extract and analyze features of molecular packing in diblock block copolymer (BCP) melts, a prototypical soft matter system, based on the statistical description of chain conformations in self-consistent field (SCF) theory. We show how average BCP chain trajectories in ordered morphologies can be analyzed from the SCF-derived orientational order parameter of chain segments. We use these extracted trajectories to analyze the features of local packing geometry, including chain bending and tilt, as well as the terminal boundaries that delineate distinct domains in ordered BCP morphologies. In conclusion, we illustrate this analysis by focusing on measurable features of packing frustration in 2D (columnar) and 3D (spherical and bicontinuous) morphologies, notably establishing an explicit link between chain conformations in complex morphologies and their medial geometry.

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

Data-scarce surrogate modeling of shock-induced pore collapse process

Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore collapse processes can be costly. To this end, a strong need exists to develop surrogate models for generating economic predictions of pore collapse processes. Here, in this work, we study the use of a data-driven reduced-order model, namely dynamic mode decomposition, and a deep generative model, namely conditional generative adversarial networks, to resemble the numerical simulations of the pore collapse process at representative training shock pressures. Since the simulations are expensive, the training data are scarce, which makes training an accurate surrogate model challenging. To overcome the difficulties posed by the complex physics phenomena, we make several crucial treatments to the plain original form of the methods to increase the capability of approximating and predicting the dynamics. In particular, physics information is used as indicators or conditional inputs to guide the prediction. In realizing these methods, the training of each dynamic mode composition model takes only around 30 s on CPU. In contrast, training a generative adversarial network model takes 8 h on GPU. Moreover, using dynamic mode decomposition, the final-time relative error is around 0.3% in the reproductive cases. We also demonstrate the predictive power of the methods at unseen testing shock pressures, where the error ranges from 1.3 to 5% in the interpolatory cases and 8 to 9% in extrapolatory cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Coupling of nitric acid digestion and anion-exchange resin separation for the determination of methylmercury isotopic composition within organisms

Isotope ratios of methylmercury (MeHg) within organisms can be used to identify sources of MeHg that have accumulated in food webs, but these isotopic compositions are masked in organisms at lower trophic levels by the presence of inorganic mercury (iHg). To facilitate measurement of MeHg isotope ratios in organisms, we developed a method of extracting and isolating MeHg from fsh and aquatic invertebrates for compound-specifc isotopic analysis involving nitric acid digestion, batch anion-exchange resin separation, and pre-concentration by purge and trap. Recovery of MeHg was quantifed after each step in the procedure, and the average cumulative recovery of MeHg was 93.4±2.9% (1 SD, n=28) for biological reference materials and natural biota samples and 96.9±1.8% (1 SD, n=5) for aqueous MeHgCl standards. The amount of iHg impurities was also quantifed after each step, and the average MeHg purity was 97.8±4.3% (1 SD, n=28) across all reference materials and natural biota samples after the fnal separation step. Measured MeHg isotopic compositions of reference materials agreed with literature values obtained using other MeHg separation techniques, and MeHg isotope ratios of aqueous standards, reference materials, and natural biota samples were reproducible. On average, the reproducibility associated with reference material process replicates (2 SD) was 0.10‰ for δ 202 MeHg and 0.04‰ for Δ 199 MeHg. This new method provides a streamlined, reliable technique that utilizes a single sample aliquot for MeHg concentration and isotopic analysis. Furthermore, this promotes a tight coupling between MeHg concentration, %MeHg, and Hg isotopic composition, which may be especially benefcial for studying complex food webs with multiple isotopically distinct sources of iHg and/or MeHg.

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

Dirichlet absorbing boundary conditions for classical and peridynamic diffusion-type models

Diffusion-type problems in (nearly) unbounded domains play important roles in various fields of fluid dynamics, biology, and materials science. The aim of this paper is to construct accurate absorbing boundary conditions (ABCs) suitable for classical (local) as well as nonlocal peridynamic (PD) diffusion models. The main focus of the present study is on the PD diffusion formulation. The majority of the PD diffusion models proposed so far are applied to bounded domains only. In this study, we propose an effective way to handle unbounded domains both with PD and classical diffusion models. For the former, we employ a meshfree discretization, whereas for the latter the finite element method (FEM) is employed. The proposed ABCs are time-dependent and Dirichlet-type, making the approach easy to implement in the available models. The performance of the approach, in terms of accuracy and stability, is illustrated by numerical examples in 1D, 2D, and 3D.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Terahertz EPR spectroscopy using a 36-tesla high-homogeneity series-connected hybrid magnet

Electron Paramagnetic Resonance (EPR) is a powerful technique to study materials and biological samples on an atomic scale. High-field EPR in particular enables extracting very small g-anisotropies in organic radicals and half-filled 3d and 4f metal ions such as Mn II (3d 5 ) or Gd III (4f 7 ), and resolving EPR signals from unpaired spins with very close g-values, both of which provide high-resolution details of the local atomic environment. Before the recent commissioning of the high-homogeneity Series Connected Hybrid magnet (SCH, superconducting + resistive) at the National High Magnetic Field Laboratory (NHMFL), the highest-field, high-resolution EPR spectrometer available was limited to 25 T using a purely resistive “Keck” magnet at the NHMFL. Herein, we report the first EPR experiments performed using the SCH magnet capable of reaching the field of 36 T, corresponding to an EPR frequency of 1 THz for g = 2. The magnet’s intrinsic homogeneity (25 ppm, that is 0.9 mT at 36 T over 1 cm diameter, 1 cm length cylinder) was previously established by NMR. We characterized the magnet’s temporal stability (5 ppm, which is 0.2 mT at 36 T over one-minute, the typical acquisition time) using 2,2-diphenyl-1-picrylhydrazyl (DPPH). This high resolution enables resolving the weak g-anisotropy of 1,3-bis(diphenylene)-2-phenylallyl (BDPA), Δg = 2.5 × 10 –4 obtained from measurements at 932 GHz and 33 T. Subsequently, we recorded EPR spectra at multiple frequencies for two Gd III complexes with potential applications as spin labels. In conclusion, we demonstrated a significant reduction in line broadening in Gd[DTPA], attributed to second order zero field splitting, and a resolution enhancement of g-tensor anisotropy for Gd[sTPATCN]-SL.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Engineering the atomic structure of sequence-defined peptoid polymers and their assemblies

Peptoids are a family of sequence-defined, non-natural biomimetic polymers which show excellent properties including good chemical and enzymatic stability and high structural tunability. The solid-phase submonomer synthesis method allows precise control over the identity and sequence of chemically diverse side chains, enabling the atomic engineering of their chemical structures for a variety of applications. This unprecedented level of structural control enables access to atomically defined three-dimensional chain conformations and assemblies, facilitating the design and optimization of a variety of nanoscale architectures that can function in biology and materials science. In order to approach the rational design of peptoid materials in a more predictive and precise manner, it is crucial to fully understand how chemical information, in the form of the monomer sequence, encodes their folding and assembly into structurally defined, functional 3D shapes. Furthermore, this perspective focuses on recent studies into the atomic engineering of peptoid nanostructures by examining the impact of sequence variations on their secondary and three-dimensional structures, as well as their functional properties.

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

Determination of the mutual orientation between proton CSA tensors mediated through band-selective 1 H– 1 H recoupling under fast MAS

We report the mutual orientation of nuclear spin interaction tensors provides critical information on the conformation and arrangement of molecules in chemicals, materials, and biological systems at an atomic level. Proton is a ubiquitous and important element in a variety of substances, and its NMR is highly sensitive due to their virtually 100% natural abundance and large gyromagnetic ratio. Nevertheless, the measurement of mutual orientation between the 1 H CSA tensors has remained largely untouched in the past due to strong 1 H– 1 H homonuclear interactions in a dense network of protons. In this study, we have developed a proton-detected 3D 1 H CSA/ 1 H CSA/ 1 H CS correlation method that utilizes three techniques to manage homonuclear interactions, namely fast magic-angle spinning, windowless C-symmetry-based CSA recoupling (windowless-ROCSA), and a band-selective 1 H– 1 H polarization transfer. The asymmetric 1 H CSA/ 1 H CSA correlated powder patterns produced by the C-symmetry-based methods are highly sensitive to the sign and asymmetry parameter of the 1 H CSA, and the Euler angle β as compared to the symmetric pattern obtained by the existing γ-encoded R-symmetry-based CSA/CSA correlation methods and allows a larger spectral area for data fitting. These features are beneficial for determining the mutual orientation between the nuclear spin interaction tensors with improved accuracy.

1H CSA↗
DOE OSTI2024

Development of a liquid-helium free cryogenic sample holder with mK temperature control for autonomous electron microscopy

The automated and autonomous cryogenic transmission electron microscopy (Cryo-EM) demands a sample holder capable of maintaining temperatures below 10 K with precise control, long holding times, and minimal helium use. Rising to this challenge, we initiated an ambitious project to develop a novel closed-cycle cryocooler-based cryogenic sample holder that operates without the use of liquid helium and the consumption of gaseous helium. Here, this article presents the design, construction, and experimental testing of the initial prototype, which achieves an ultimate temperature of 5.6 K with exceptional stability close to 1mK, while providing a wide temperature control range from 295 K to 5.6 K, marking a clear advancement in cryo-EM holder development. While the prototype was not designed for atomic resolution imaging and thus lacks a sturdy support system to mitigate mechanical vibrations from the cryocooler's pulsed tube, this innovative approach successfully demonstrates proof of concept. It offers unprecedented capabilities for state-of-the-art cryogenic microscopy and microanalysis in materials and biological sciences.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Effects of laminin-111 peptide coatings on rat neural stem/progenitor cell culture

Neurons require adhesive scaffolds for their growth and differentiation. Laminins are a major cell adhesive component of basement membranes and have various biological activities in the peripheral and central nervous systems. Here, we evaluated the biological activities of 5 peptides derived from laminin-111 as a scaffold for mouse neuroblastoma Neuro2a cells and rat neural stem/progenitor cells (NPCs). The 5 peptides showed Neuro2a cell attachment activity similar to that of poly-d-lysine. However, when NPCs were cultured on the peptides, 2 syndecan-binding peptides, AG73 (RKRLQVQLSIRT, mouse laminin α1 chain 2719-2730) and C16 (KAFDITYVRLKF, laminin γ1 chain 139–150), demonstrated significantly higher cell attachment and neurite extension activities than other peptides including integrin-binding ones. Long-term cell culture experiments showed that both AG73 and C16 supported the growth of neurons and astrocytes that had differentiated from NPCs. Furthermore, C16 markedly promoted the expression of neuronal markers such as synaptosomal-associated protein-25 and syntaxin 1A. These results indicate that AG73 and C16 are useful for NPC cultures and that C16 can be applied to specialized research on synapses in differentiated neurons. These peptides have the potential for use as valuable biomaterials for NPC research.

60 APPLIED LIFE SCIENCES↗