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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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The possibility to use diffuse reflections of a laser beam to exert a force on levitating dust particles is studied experimentally. Measurements and theoretical predictions are found to be in good agreement. Further, the method is applied to test the selective excitation of breathing-like modes in finite dust clusters.
Oxytocin plays a critical role in regulating social behaviors, yet our understanding of its function in both neurological health and disease remains incomplete. Real-time oxytocin imaging probes with spatiotemporal resolution relevant to its endogenous signaling are required to fully elucidate oxytocin’s role in the brain. Herein, we describe a near-infrared oxytocin nanosensor (nIROXT), a synthetic probe capable of imaging oxytocin in the brain without interference from its structural analogue, vasopressin. nIROXT leverages the inherent tissue-transparent fluorescence of single-walled carbon nanotubes (SWCNT) and the molecular recognition capacity of an oxytocin receptor peptide fragment to selectively and reversibly image oxytocin. We employ these nanosensors to monitor electrically stimulated oxytocin release in brain tissue, revealing oxytocin release sites with a median size of 3 µm in the paraventricular nucleus of C57BL/6 mice, which putatively represents the spatial diffusion of oxytocin from its point of release. These data demonstrate that covalent SWCNT constructs, such as nIROXT, are powerful optical tools that can be leveraged to measure neuropeptide release in brain tissue.
We report that intercalation compounds are popular candidate electrode materials for sodium-ion batteries and other ‘beyond lithium-ion’ technologies including potassium- and magnesium-ion batteries. We summarize first-principles efforts to elucidate the behaviour of such compounds in the layered and spinel structures. Trends based on the size and valence of the intercalant and the ionicity of the host are sufficient to explain phase stability and ordering phenomena, which in turn determine the equilibrium voltage profile. For the layered structures, we provide an overarching view of intercalant orderings in prismatic coordination based on antiphase boundaries, which has important consequences for diffusion. We examine details of stacking sequence transitions between different layered structures by calculating stacking fault energies and discussing the nature of dislocations. A better understanding of these transitions will likely aid the development of batteries with improved cyclability. This article is part of a discussion meeting issue ‘Energy materials for a low carbon future’.
Simulation is crucial for all aspects of collider data analysis, but the available computing budget in the High Luminosity LHC era will be severely constrained. Generative machine learning models may act as surrogates to replace physics-based full simulation of particle detectors, and diffusion models have recently emerged as the state of the art for other generative tasks. Here, we introduce CaloDiffusion, a denoising diffusion model trained on the public CaloChallenge datasets to generate calorimeter showers. Our algorithm employs 3D cylindrical convolutions, which take advantage of symmetries of the underlying data representation. To handle irregular detector geometries, we augment the diffusion model with a new geometry latent mapping (GLaM) layer to learn forward and reverse transformations to a regular geometry that is suitable for cylindrical convolutions. The showers generated by our approach are nearly indistinguishable from the full simulation, as measured by several different metrics.
Small-angle scattering X-ray photon correlation spectroscopy (XPCS) studies were performed using a novel photon-counting pixel array detector with dual counters for each pixel. Each counter can be read out independently from the other to ensure there is no readout dead-time between the neighboring frames. A maximum frame rate of 11.8 kHz was achieved. Results on test samples show good agreement with simple diffusion. Lastly, the potential of extending the time resolution of XPCS beyond the limit set by the detector frame rate using dual counters is also discussed.
The ion dynamics in a collisionless magnetic reconnection layer are studied in a laboratory plasma. The measured in-plane plasma potential profile, which is established by electrons accelerated around the electron diffusion region, shows a saddle-shaped structure that is wider and deeper towards the outflow direction. This potential structure ballistically accelerates ions near the separatrices toward the outflow direction. Ions are heated as they travel into the high pressure downstream region.
Laser technology is being widely studied for controlled energy deposition for a range of applications, including flow control, ignition, combustion, and diagnostics. The absorption and scattering of laser radiation by liquid droplets in aerosols affects propagation of the laser beam in the atmosphere, while the ignition and combustion characteristics in combustion chambers are influenced by the evaporation rate of the sprayed fuel. In this work, we present a mathematical model built on OpenFOAM for laser heating and evaporation of a single droplet in the diffusion-dominated regime taking into account absorption of the laser radiation, evaporation process, and vapor flow dynamics. The developed solver is validated against available experimental and numerical data for heating and evaporation of ethanol and water droplets. The two main regimes—continuous and pulsed laser heating—are explored. For continuous laser heating, the peak temperature is higher for larger droplets. For pulsed laser heating, when the peak irradiance is close to transition to the boiling regime, the temporal dynamics of the droplet temperature does not depend on the droplet size. With the empirical normalization of time, the dynamics of the droplet shrinkage and cooling are found to be independent of droplet sizes and peak laser intensities.
A careful examination of the effects of collisions on resonant wave-particle interactions leads to an alternate interpretation and deeper understanding of the quasilinear operator originally formulated by Kennel and Engelmann (Phys. Fluids vol. 9, 1966, pp. 2377- 2388) for collisionless, magnetized plasmas, and widely used to model radio frequency heating and current drive. The resonant and nearly resonant particles are particularly sensitive to collisions that pitch angle scatter them out of and into resonance. As a result, the resonant particle-wave interactions occur in the center of a narrow collisional boundary when the collision frequency nu is very small compared to the wave frequency omega. The diffusive nature of the pitch angle scattering combined with the wave-particle resonance condition enhances the collision frequency by (omega/nu)2/3 >>1, resulting in an effective resonant particle collision time of tau_int ~ (nu /omega)2/3 nu <<1/ nu . A rigorous collisional boundary layer analysis generalizes the standard quasilinear operator to a form that is fully consistent with Kennel-Englemann, but allows replacing the delta function appearing in the diffusivity with a simple integral (having the appropriate delta function limit) retaining the new physics associated with the narrow boundary layer, while preserving the entropy production principle. The limitations of the collisional boundary layer treatment are also estimated, and indicate that substantial departures from Maxwellian are not permitted.
Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. Here, the porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I-V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag + ion and electron diffusion pathways. Ag + ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ~95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.
Electrical transport in semiconducting and metallic particle suspensions is an enabling feature of emerging grid-scale battery technologies. Although the physics of the transport process plays a key role in these technologies, no universal framework has yet emerged. Here, we examine the important contribution of shear flow to the electrical transport of non-Brownian suspensions. We find that these suspensions exhibit a strong dependence of the transport rate on the particle volume fraction and applied shear rate, which enables the conductivity to be dynamically changed by over 10 7 decades based on the applied shear rate. We combine experiments and simulations to conclude that the transport process relies on a combination of charge and particle diffusion with a rate that can be predicted using a quantitative physical model that incorporates the self-diffusion of the particles.
Here we assessed the hydrogen migration and redistribution model implemented in the BISON fuel performance code for component-level tritium transport applications in fusion and fission example scenarios. We developed BISON models of an ITER heat exchanger, light water reactor fuel cladding, and a fluoride salt cooled high temperature reactor heat exchanger. Reasonable agreement between reported values in reference studies and BISON predictions demonstrated the ability of the BISON models to predict tritium transport behavior through steel materials. Next, sensitivity and uncertainty analysis was used to understand key parameter sensitivities within the BISON model for evaluation of tritium migration and redistribution. . The sensitivity analysis showed that the diffusivity activation energy was the most important parameter and that the heat of transport was the least important parameter for tritium migration in steels. Overall, the capabilities of the BISON code for component level modeling of tritium transport are promising.
Opaque scintillators are designed to have a short scattering length such that scintillation photons are probabilistically confined to a small region of space about their origin. The benefit of this feature is that information on the interaction event topology can be recorded with greater fidelity than in traditional highly transparent media with sensors at large distances from the light production region. Opaque scintillator detectors rely on wavelength-shifting fibers to extract the scintillation light; however, the efficiency of light collection has not yet been directly measured in experiment. We measured the efficiency of light collection as a function of the optical parameters of an opaque liquid and the distance from the origin of the light to the fiber. We use the experimental data to validate a Monte Carlo model of light transport and collection and discuss a simple diffusion model that reproduces the results of Monte Carlo simulation with high fidelity. Furthermore, this combination of validated models has the potential for use in predictions of performance in various designs of future opaque scintillator detectors such as LiquidO.
Keyhole porosity is a key concern in laser powder-bed fusion (LPBF), potentially impacting component fatigue life. However, some keyhole porosity formation mechanisms, e.g., keyhole fluctuation, collapse and bubble growth and shrinkage, remain unclear. Using synchrotron X-ray imaging we reveal keyhole and bubble behaviour, quantifying their formation dynamics. The findings support the hypotheses that: (i) keyhole porosity can initiate not only in unstable, but also in the transition keyhole regimes created by high laser power-velocity conditions, causing fast radial keyhole fluctuations (2.5-10 kHz); (ii) transition regime collapse tends to occur part way up the rear-wall; and (iii) immediately after keyhole collapse, bubbles undergo rapid growth due to pressure equilibration, then shrink due to metal-vapour condensation. Concurrent with condensation, hydrogen diffusion into the bubble slows the shrinkage and stabilises the bubble size. The keyhole fluctuation and bubble evolution mechanisms revealed here may guide the development of control systems for minimising porosity.
All-solid-state batteries require advanced cathode designs to realize their potential for high energy density and economic viability. Integrated all-in-one cathodes, which eliminate inactive conductive additives and heterogeneous interfaces, hold promise for substantial energy and stability gains but are hindered by materials lacking sufficient Li + /e − conductivity, mechanical robustness and structural stability. Here, in this work, we present Li 1.3 Fe 1.2 Cl 4 , a cost-effective halide material that overcomes these challenges. Leveraging reversible Fe 2+ /Fe 3+ redox and rapid Li + /e − transport within its framework, Li 1.3 Fe 1.2 Cl 4 achieves an electrode energy density of 529.3 Wh kg −1 versus Li + /Li. Critically, Li 1.3 Fe 1.2 Cl 4 shows unique dynamic properties during cycling, including reversible local Fe migration and a brittle-to-ductile transition that confers self-healing behaviour. This enables exceptional cycling stability, maintaining 90% capacity retention for 3,000 cycles at a rate of 5 C. Integration of Li 1.3 Fe 1.2 Cl 4 with a nickel-rich layered oxide further increases the energy density to 725.6 Wh kg −1 . By harnessing the advantageous dynamic mechanical and diffusion properties of all-in-one halides, this work establishes all-in-one halides as an avenue for energy-dense, durable cathodes in next-generation all-solid-state batteries.
Although most nanofabrication techniques can control nano/micro particle (NMP) size over a wide range, the majority of NMPs for biomedical applications exhibits a diameter of ,100 nm. Here, the vascular distribution of spherical particles, from 10 to 1,000 nm in diameter, is studied using intravital microscopy and computational modeling. Small NMPs (#100 nm) are observed to move with Red Blood Cells (RBCs), presenting an uniform radial distribution and limited near-wall accumulation. Larger NMPs tend to preferentially accumulate next to the vessel walls, in a size-dependent manner (,70% for 1,000 nm NMPs). RBC-NMP geometrical interference only is responsible for this behavior. In a capillary flow, the effective radial dispersion coefficient of 1,000 nm particles is ,3-fold larger than Brownian diffusion. This suggests that sub-micron particles could deposit within diseased vascular districts more efficiently than conventional nanoparticles.
Thin films of metals and other materials are often grown by physical vapor deposition. To understand such processes, it is desirable to measure the adsorption energy of the deposited species as the film grows, especially when grown on single crystal substrates where the structure of the adsorbed species, evolving interface, and thin film are more homogeneous and well-defined in structure. Our group previously described in this journal an adsorption calorimeter capable of such measurements on single-crystal surfaces under the clean conditions of ultrahigh vacuum [J. T. Stuckless, N. A. Frei, and C. T. Campbell, Rev. Sci. Instrum. 69, 2427 (1998)]. Here we describe several improvements to that original design that allow for heat measurements with ∼18-fold smaller standard deviation, greater absolute accuracy in energy calibration, and, most importantly, measurements of the adsorption of lower vapor-pressure materials which would have previously been impossible. These improvements are accomplished by: (1) using an electron beam evaporator instead of a Knudsen cell to generate the metal vapor at the source of the pulsed atomic beam, (2) changing the atomic beam design to decrease the relative amount of optical radiation that accompanies evaporation, (3) adding an off-axis quartz crystal microbalance for real-time measurement of the flux of the atomic beam during calorimetry experiments, and (4) adding capabilities for in situ relative diffuse optical reflectivity determinations (necessary for heat signal calibration). These improvements are not limited to adsorption calorimetry during metal deposition, but also could be applied to better study film growth of other elements and even molecular adsorbates.
Conformationally fluctuating, globally compact macromolecules such as polymeric rings, single-chain nanoparticles, microgels, and many-arm stars display complex dynamic behaviors due to their rich topological structure and intermolecular organization. Synthetic rings are hybrid objects with conformations that display both ideal random walk and compact globular features, which can serve as models of genomic DNA. To date, emphasis has been placed on the effect of ring molecular weight on their unusual behaviors. Here, we combine simulations and a microscopic force-level theory to build a unified understanding for how key aspects of ring dynamics depend on different tunable molecular properties including backbone rigidity, monomer concentration, degree of traditional entanglement, and molecular weight. Our large-scale molecular dynamics simulations of ring melts with very different backbone stiffnesses reveal unanticipated behaviors which agree well with our generalized theory. This includes a universal master curve for center-of-mass diffusion constants as a function of molecular weight scaled by a chemistry and thermodynamic state-dependent critical molecular weight that generalizes the concept of an entanglement cross-over for linear chains. The key physics is how backbone rigidity and monomer concentration induced changes of the entanglement length, interring packing, degree of interpenetration, and liquid compressibility slow down space-time dynamic-force correlations on macromolecular scales. A power law decay of the center-of-mass diffusion constant with inverse molecular weight squared is the first consequence, followed by an ultraslow activated hopping transport regime. Our results set the stage to address slow dynamics and kinetic arrest in different families of compact synthetic and biological polymeric systems.