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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Pressure-induced structural phase transitions in CrSBr

There is growing interest in combining chemical complexity with external stimuli like pressure, field, and light for property control in van der Waals solids. This is because extreme conditions trigger the development of new states of matter and functionality. In this work, we bring together synchrotron-based infrared absorption, Raman scattering, and diamond anvil cell techniques with first-principles calculations of the lattice dynamics and energy landscape to reveal the series of structural phase transitions in CrSBr. By tracking how the phonons change under pressure, we uncover a remarkable chain of complex symmetry modifications, interlayer interactions, and chemical reactions. A group-subgroup analysis suggests that CrSBr undergoes an orthorhombic Pmmn → monoclinic P2/m transition at 7.6 GPa, and based upon a comparison with model oxychlorides like FeOCl and CrOCl, we propose that changes in the pendant halide groups drive the system to a P2 1 /m-like space group above 15.3 GPa. Compression above 20.2 GPa is irreversible, resulting in the formation of an entirely new compound that is metastable for months. This work opens the door to the use of pressure and possibly strain to control the properties of CrSBr.

Microscopy↗
DOE OSTI2022

Non-volatile electric-field control of inversion symmetry

Competition between ground states at phase boundaries can lead to significant changes in properties under stimuli, particularly when these ground states have different crystal symmetries. A key challenge is to stabilize and control the coexistence of symmetry-distinct phases. Using BiFeO 3 layers confined between layers of dielectric TbScO 3 as a model system, we stabilize the mixed-phase coexistence of centrosymmetric and non-centrosymmetric BiFeO 3 phases at room temperature with antipolar, insulating and polar semiconducting behaviour, respectively. Application of orthogonal in-plane electric (polar) fields results in reversible non-volatile interconversion between the two phases, hence removing and introducing centrosymmetry. Counterintuitively, we find that an electric field 'erases' polarization, resulting from the anisotropy in octahedral tilts introduced by the interweaving TbScO 3 layers. Consequently, this interconversion between centrosymmetric and non-centrosymmetric phases generates changes in the non-linear optical response of over three orders of magnitude, resistivity of over five orders of magnitude and control of microscopic polar order. Here our work establishes a platform for cross-functional devices that take advantage of changes in optical, electrical and ferroic responses, and demonstrates octahedral tilts as an important order parameter in materials interface design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Topological steering of light by nematic vortices and analogy to cosmic strings

Liquid crystals are widely known for their technological uses in displays, electro-optics, photonics and nonlinear optics, but these applications typically rely on defining and switching non-topological spatial patterns of the optical axis. Here, we demonstrate how a liquid crystal’s optical axis patterns with singular vortex lines can robustly steer beams of light. External stimuli, including an electric field and light itself, allow us to reconfigure these unusual light–matter interactions. Here, periodic arrays of vortices obtained by photo-patterning enable the vortex-mediated fission of optical solitons, yielding their lightning-like propagation patterns. Predesigned patterns and spatial trajectories of vortex lines in high-birefringence liquid crystals can steer light into closed loops or even knots. Our vortex lattices might find technological uses in beam steering, telecommunications, virtual reality implementations and anticounterfeiting, as well as possibly offering a model system for probing the interaction of light with defects, including the theoretically predicted, imagination-capturing light-steering action of cosmic strings, elusive defects in cosmology.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Non-equilibrium pathways to emergent polar supertextures

Ultrafast stimuli can stabilize metastable states of matter inaccessible by equilibrium means. Establishing the spatiotemporal link between ultrafast excitation and metastability is crucial to understand these phenomena. Here, in this work, we utilize single-shot optical pump–X-ray probe measurements to capture snapshots of the emergence of a persistent polar vortex supercrystal in a heterostructure that hosts a fine balance between built-in electrostatic and elastic frustrations by design. By perturbing this balance with photoinduced charges, an initially heterogeneous mixture of polar phase disorders within a few picoseconds, leading to a state composed of disordered ferroelectric and suppressed vortex orders. On the picosecond–nanosecond timescales, transient labyrinthine fluctuations develop, accompanied by the recovery of the vortex order. On longer timescales, these fluctuations are progressively quenched by dynamical strain modulations, which drive the collective emergence of a single vortex supercrystal phase. Our results, corroborated by dynamical phase-field modelling, reveal non-equilibrium pathways following the ultrafast excitation of designer systems to persistent metastability.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mechanical Regulation of Photosynthesis in Cyanobacteria

Photosynthetic organisms regulate their responses to many diverse stimuli in an effort to balance light harvesting with utilizable light energy for carbon fixation and growth (source–sink regulation). This balance is critical to prevent the formation of reactive oxygen species that can lead to cell death. However, investigating the molecular mechanisms that underlie the regulation of photosynthesis in cyanobacteria using ensemble-based measurements remains a challenge due to population heterogeneity. Here, to address this problem, we used long-term quantitative time-lapse fluorescence microscopy, transmission electron microscopy, mathematical modelling and genetic manipulation to visualize and analyse the growth and subcellular dynamics of individual wild-type and mutant cyanobacterial cells over multiple generations. We reveal that mechanical confinement of actively growing Synechococcus sp. PCC 7002 cells leads to the physical disassociation of phycobilisomes and energetic decoupling from the photosynthetic reaction centres. We suggest that the mechanical regulation of photosynthesis is a critical failsafe that prevents cell expansion when light and nutrients are plentiful, but when space is limiting. These results imply that cyanobacteria must convert a fraction of the available light energy into mechanical energy to overcome frictional forces in the environment, providing insight into the regulation of photosynthesis and how microorganisms navigate their physical environment.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2020

Berry curvature memory through electrically driven stacking transitions

In two-dimensional layered quantum materials, the interlayer stacking order determines both crystalline symmetry and quantum electronic properties such as Berry curvature, topology and electron correlation. Electrical stimuli can strongly influence quasi-particle interactions and the free energy landscape, thus making it possible to access hidden stacking orders with novel quantum properties and enabling dynamic engineering of these attributes. In this paper, we demonstrate electrically driven stacking transitions and a new type of nonvolatile memory based on Berry curvature in few-layer WTe 2 . The interplay of out-of-plane electric fields and electrostatic doping controls in-plane interlayer sliding and creates multiple polar and centrosymmetric stacking orders. In-situ nonlinear Hall transport reveals such stacking rearrangements result in a layer-parity-selective Berry curvature memory in momentum space, where the sign reversal of the Berry curvature only occurs in odd layer crystals. Our findings open an avenue towards exploring coupling between topology, electron correlations, and ferroelectricity in hidden stacking orders and demonstrate a new low energy cost, electrically-controlled topological memory in the atomically thin limit.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Constructing protein polyhedra via orthogonal chemical interactions

Many proteins exist naturally as symmetrical homooligomers or homopolymers. The emergent structural and functional properties of such protein assemblies have inspired extensive efforts in biomolecular design2–5. As synthesized by ribosomes, proteins are inherently asymmetric. Therefore, they must acquire multiple surface patches that selectively associate to generate the different symmetry elements needed to form higher-order architectures—a daunting task for protein design. Here we address this problem using an inorganic chemical approach, whereby multiple modes of protein–protein interactions and symmetry are simultaneously achieved by selective, ‘one-pot’ coordination of soft and hard metal ions. We show that a monomeric protein (protomer) appropriately modified with biologically inspired hydroxamate groups and zinc-binding motifs assembles through concurrent Fe 3+ and Zn 2+ coordination into discrete dodecameric and hexameric cages. Our cages closely resemble natural polyhedral protein architectures and are, to our knowledge, unique among designed systems in that they possess tightly packed shells devoid of large apertures. At the same time, they can assemble and disassemble in response to diverse stimuli, owing to their heterobimetallic construction on minimal interprotein-bonding footprints. With stoichiometries ranging from [2 Fe:9 Zn:6 protomers] to [8 Fe:21 Zn:12 protomers], these protein cages represent some of the compositionally most complex protein assemblies—or inorganic coordination complexes—obtained by design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Shape memory in self-adapting colloidal crystals

We report reconfigurable, mechanically responsive crystalline materials are central components in many sensing, soft robotic, and energy conversion and storage devices1,2,3,4. Crystalline materials can readily deform under various stimuli and the extent of recoverable deformation is highly dependent upon bond type1,2,5,6,7,8,9,10. Indeed, for structures held together via simple electrostatic interactions, minimal deformations are tolerated. By contrast, structures held together by molecular bonds can, in principle, sustain much larger deformations and more easily recover their original configurations. In this report we study the deformation properties of well-faceted colloidal crystals engineered with DNA. These crystals are large in size (greater than 100 µm) and have a body-centred cubic (bcc) structure with a high viscoelastic volume fraction (of more than 97%). Therefore, they can be compressed into irregular shapes with wrinkles and creases, and, notably, these deformed crystals, upon rehydration, assume their initial well-formed crystalline morphology and internal nanoscale order within seconds. For most crystals, such compression and deformation would lead to permanent, irreversible damage. The substantial structural changes to the colloidal crystals are accompanied by notable and reversible optical property changes. For example, whereas the original and structurally recovered crystals exhibit near-perfect (over 98%) broadband absorption in the ultraviolet–visible region, the deformed crystals exhibit significantly increased reflection (up to 50% of incident light at certain wavelengths), mainly because of increases in their refractive index and inhomogeneity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electric field stimulates production of highly conductive microbial OmcZ nanowires

Multifunctional living materials are attractive due to their powerful ability to self-repair and replicate. However, most natural materials lack electronic functionality. Here we show that an electric field, applied to electricity-producing Geobacter sulfurreducens biofilms, stimulates production of cytochrome OmcZ nanowires with 1,000-fold higher conductivity (30 S cm -1 ) and threefold higher stiffness (1.5 GPa) than the cytochrome OmcS nanowires that are important in natural environments. Using chemical imaging-based multimodal nanospectroscopy, we correlate protein structure with function and observe pH-induced conformational switching to β-sheets in individual nanowires, which increases their stiffness and conductivity by 100-fold due to enhanced π-stacking of heme groups; this was further confirmed by computational modeling and bulk spectroscopic studies. These nanowires can transduce mechanical and chemical stimuli into electrical signals to perform sensing, synthesis and energy production. Overall, these findings of biologically produced, highly conductive protein nanowires may help to guide the development of seamless, bidirectional interfaces between biological and electronic systems.

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

Model metamers reveal divergent invariances between biological and artificial neural networks

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated ‘model metamers’, stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from late model stages, suggesting differences between model and human invariances. Targeted model changes improved human recognizability of model metamers but did not eliminate the overall human–model discrepancy. The human recognizability of a model’s metamers was well predicted by their recognizability by other models, suggesting that models contain idiosyncratic invariances in addition to those required by the task. Metamer recognizability dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task

A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.

Science & Technology - Other Topics↗
DOE OSTI2020

Spatially resolved fibre cavity ring down spectroscopy

Abstract This paper presents a fibre cavity ring down spectroscopy probed by Rayleigh scattering optical frequency domain reflectometry (OFDR), which provides spatial location of stimuli and improved signal to noise ratio for distributed sensing measurements. A section of optical fibre was integrated into an active fibre ring cavity with optical gain and interrogated by the OFDR system for 11 cycles with a single laser scan. Through the cavity ring down configuration, root-mean-squared (RMS) noise of distributed temperature and strain measurements was reduced to 6.9 mK and less than 0.1 με, respectively for 1-cm spatially resolved measurements. Our work shows that the active fibre cavity configuration can be combined with distributed fibre sensing schemes to achieve both high spatial resolution and high sensitivity measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy

Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM’s high affinity with various analytical options into account. As is well known, STEM’s image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM’s current temporal resolution is far below the conventional TEM’s. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Epidermal growth factor receptor cascade prioritizes the maximization of signal transduction

Many studies have been performed to quantify cell signaling. Cell signaling molecules are phosphorylated in response to extracellular stimuli, with the phosphorylation sequence forming a signal cascade. The information gain during a signal event is given by the logarithm of the phosphorylation molecule ratio. The average information gain can be regarded as the signal transduction quantity (ST), which is identical to the Kullback–Leibler divergence (KLD), a relative entropy. We previously reported that if the total ST value in a given signal cascade is maximized, the ST rate (STR) of each signaling molecule per signal duration (min) approaches a constant value. To experimentally verify this theoretical conclusion, we measured the STR of the epidermal growth factor (EGF)-related cascade in A431 skin cancer cells following stimulation with EGF using antibody microarrays against phosphorylated signal molecules. The results were consistent with those from the theoretical analysis. Thus, signaling transduction systems may adopt a strategy that prioritizes the maximization of ST. Furthermore, signal molecules with similar STRs may form a signal cascade. In conclusion, ST and STR are promising properties for quantitative analysis of signal transduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dehydration kinetics of nanoconfined water in beryl probed by high temperature single crystal synchrotron X-ray diffraction

Understanding changes in material properties through external stimuli plays a key role in validating the expected performance of materials and engineering material properties in a controlled manner. Here, we introduce a fundamental protocol to deduce dehydration reactions kinetics of water confined in nanopore channels, with the cyclosilicate beryl as the scaffold of interest, using time-resolved synchrotron X-ray diffraction (SXRD), in the temperature interval of 298–1038 K. The temperature-dependent intensity (I) of the strongest reflection (112) was used as the crystallite variable. An estimation of an isobaric thermal crystallite coefficient, k, analogous with the isobaric thermal expansion coefficient, established the rate of relative crystallization as a function of temperature, $\frac{∂I}{∂T}$. A plot of lnk and $\frac{1}{T}$ gives rise to two kinetic steps, indicating a slow dehydration stage up to ~ 700 K and a fast dehydration stage up to the investigated temperature 1038 K. The crystal structure of beryl determined up to 1038 K, in temperature increment as small as 10 K, indicates the presence of channel ions Na and Fe and a gradual decrease of water upon heating.

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

Complexity-calibrated benchmarks for machine learning reveal when prediction algorithms succeed and mislead

Abstract Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a “next-generation” reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore the inherent limitations of finite-past memory traces in this intriguing proposal. A lower bound from Fano’s inequality shows that, on highly non-Markovian processes generated by large probabilistic state machines, next-generation reservoir computers with reasonably long memory traces have an error probability that is at least $$\sim 60\%$$ ∼ 60 % higher than the minimal attainable error probability in predicting the next observation. More generally, it appears that popular recurrent neural networks fall far short of optimally predicting such complex processes. These results highlight the need for a new generation of optimized recurrent neural network architectures. Alongside this finding, we present concentration-of-measure results for randomly-generated but complex processes. One conclusion is that large probabilistic state machines—specifically, large $$\epsilon$$ ϵ -machines—are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Insulator–metal transition in CrSiTe 3 triggered by structural distortion under pressure

van der Waals solids are well known to host remarkable phase diagrams with competing phases, unusual energy transfer processes, and elusive states of matter. Among this class of materials, chalcogenides have emerged as the most flexible and relevant platforms for unraveling charge–structure–function relationships. In order to explore the properties of complex chalcogenides under external stimuli, we measured the far infrared spectroscopic response of CrSiTe 3 under extreme pressure–temperature conditions. Analysis of the 368 cm –1 Si–Te stretching mode and the manner in which it is screened by the closure of the indirect gap reveals that the insulator–metal transition takes place immediately after the structural phase transition—once the mixed phase aspect of the lattice distortion is resolved. At the same time, the two-phase region associated with the structural transition widens with decreasing temperature, and the slope of the insulator–metal transition under pressure is consistent with increasing entropy. These trends completely revise the character of the temperature–pressure phase diagram as well as the relationship between the structural and insulator–metal transitions, leading to a critical nexus of activity that may hide a quantum critical point and allow superconductivity to emerge.

36 MATERIALS SCIENCE↗