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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A High-Throughput MEMS-Based Differential Scanning Calorimeter for Direct Thermal Characterization of Antibodies

Calorimeters, which can be used for rapid thermal characterization of biomolecules, are getting intense attention in drug development. This paper presents a novel MEMS-based differential scanning calorimeter (DSC) for direct thermal characterization of protein samples. The DSC consisted of a pair of temperature sensors made by vanadium oxide (VOx) film with a temperature coefficient of resistivity of –0.025/K at 300 K, a microfluidic device with high thermal insulation (2.8 K/mW), and a Peltier heater for linear temperature scanning. The DSC exhibited high sensitivity (6.1 µV/µW), low noise (0.4 µW), high scanning rate (45 K/min), and low sample consumption volume (0.63 µL). The MEMS DSC was verified by measuring the temperature-induced denaturation of lysozyme at different pH, and then used to study the thermal stability of a monoclonal antibody (mAb), an antigen-binding fragment (Fab), and a dual variable domain immunoglobulin (DVD-Ig) at pH = 6. The results showed that lysozyme is a stable protein in the pH range of 4.0–8.0. The protein stability study revealed that the transition temperatures of the intact Fab fragment, mAb, and DVD proteins were comparable with conformational stability results obtained using conventional commercial DSC. These studies demonstrated that the MEMS DSC is an effective tool for directly understanding the thermal stability of antibodies in a high-throughput and low-cost manner compared to conventional calorimeters.

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

In situ plasmonic tip preparation and validation techniques for scanning tunneling microscopy

Among the many parts constituting a scanning tunneling microscope, the metallic tip is the component that directly interacts with the specimen and plays a critical role in visualizing the physical quantity of interest. While tip materials such as W and Pt–Ir are commonly used for topographic imaging and their preparation is well-documented, the preparation of plasmonic materials such as Ag for tip-enhanced Raman spectroscopy is relatively less standardized. Furthermore, we present several in situ Ag tip preparation and validation techniques for the microscopist to use depending on their intended application, including atomic resolution imaging, scanning tunneling spectroscopy (STM), and tip-enhanced Raman spectro-microscopy in ultrahigh vacuum. Besides optical applications, these methods are not limited to Ag but also applicable to other STM tip materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Machine Learning for Optical Scanning Probe Nanoscopy

Abstract The ability to perform nanometer‐scale optical imaging and spectroscopy is key to deciphering the low‐energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering‐type scanning near‐field optical microscopy (s‐SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s‐SNOM, together with scanning probe research in general, can benefit in many ways from artificial‐intelligence (AI) and machine‐learning (ML) algorithms. Augmented with AI‐ and ML‐enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.

Chen, Xinzhong↗
DOE OSTI2023

Frequency‐Scanning Considerations in Axionlike Dark Matter Spin‐Precession Experiments

Abstract Galactic dark matter may consist of axionlike particles (ALPs) that can be described as an “ultralight bosonic field” oscillating at the ALP Compton frequency. The ALP field can be searched for using nuclear magnetic resonance (NMR), where resonant precession of spins of a polarized sample can be sensitively detected. The ALP mass to which the experiment is sensitive is scanned by sweeping the bias magnetic field. The scanning either results in detection of ALP dark matter or rules out ALP dark matter with sufficiently strong couplings to nuclear spins over the range of ALP masses corresponding to the covered span of Larmor frequencies. In this work, scanning strategies are analyzed with the goal of optimizing the parameter‐space coverage via a proper choice of experimental parameters (e.g., the effective transverse relaxation time).

Zhang, Yuzhe (ORCID:0009000660829066)↗
DOE OSTI2026

Speedup of UEDGE Parameter Scans Using Machine-Learning Optimized OpenMP Parallelization and a Continuation Solver

This article presents the OpenMP parallelization of the preconditioning Jacobian assembly and right‐hand side residual evaluation in UEDGE. A continuation algorithm, utilizing the internal NKSOL implicit Jacobian‐Free Newton‐Krylov solver to efficiently scan physical parameters, is also presented. The implemented parallelization reduces the computational time for a benchmark scan run on 32 threads by compared to the serial version when using trained random forest regression models to identify the optimal decomposition of the system of equations. Random forest regression models applied to the UEDGE time‐dependent and continuation solver algorithms did not yield meaningful improvement in computational performance. A benchmark DIII‐D gas injection rate scan in the 0.35–0.75 kA interval, performed on a test cluster using the parallelized code and continuation solver, produced 1066 steady‐state solutions with a 22 s average wall‐clock computational time per steady‐state solution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Impact of simulated reduced injected dose on the assessment of amyloid PET scans

To investigate the impact of reduced injected doses on the quantitative and qualitative assessment of the amyloid PET tracers [ 18 F]flutemetamol and [ 18 F]florbetaben. Cognitively impaired and unimpaired individuals (N = 250, 36% Aβ-positive) were included and injected with [ 18 F]flutemetamol (N = 175) or [ 18 F]florbetaben (N = 75). PET scans were acquired in list-mode (90–110 min post-injection) and reduced-dose images were simulated to generate images of 75, 50, 25, 12.5 and 5% of the original injected dose. Images were reconstructed using vendor-provided reconstruction tools and visually assessed for Aβ-pathology. SUVRs were calculated for a global cortical and three smaller regions using a cerebellar cortex reference tissue, and Centiloid was computed. Absolute and percentage differences in SUVR and CL were calculated between dose levels, and the ability to discriminate between Aβ- and Aβ + scans was evaluated using ROC analyses. Finally, intra-reader agreement between the reduced dose and 100% images was evaluated. At 5% injected dose, change in SUVR was 3.72% and 3.12%, with absolute change in Centiloid 3.35CL and 4.62CL, for [ 18 F]flutemetamol and [ 18 F]florbetaben, respectively. At 12.5% injected dose, percentage change in SUVR and absolute change in Centiloid were < 1.5%. AUCs for discriminating Aβ- from Aβ + scans were high (AUC ≥ 0.94) across dose levels, and visual assessment showed intra-reader agreement of > 80% for both tracers. This proof-of-concept study showed that for both [ 18 F]flutemetamol and [ 18 F]florbetaben, adequate quantitative and qualitative assessments can be obtained at 12.5% of the original injected dose. However, decisions to reduce the injected dose should be made considering the specific clinical or research circumstances.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Effect of the scanning strategy on the formation of residual stresses in additively manufactured Ti-6Al-4V

During the laser-powder bed fusion (L-PBF) process, high laser intensities, short interaction times and highly localized heat input drive large thermal gradients that result in a state of high residual stresses. Generally, the residual stresses that develop during the L-PBF process can compromise the performance of the component. Up to now, the literature has indicated that the magnitude of the residual stresses can be affected by various process parameters. In this study, all process parameters such as laser power and speed are held fixed and the focus is solely on the effect of the laser scan strategy on the three-dimensional residual stress state of L-PBF metallic components. Four Ti-6Al-4V bridge shaped components were built using island and continuous scanning patterns parallel and offset 45° from the sample axes. High-energy X-ray diffraction was used to determine the residual stress field in each of the components. Two of them were re-measured after being partially removed from the build plate. The assumptions implicit in diffraction measurements of stress are reviewed and discussed in depth because the unique microstructure associated with L-PBF Ti-6Al-4V renders the validity of those assumptions uncertain. Specifically, additional data was collected and analyzed to evaluate the relationship between grain scale and macroscopic scale stresses. The observed residual stresses were large, ½ to ¾ of the yield strength, particularly the build direction stresses near the lateral edges of the bridges. Here in this work, the higher stresses were observed in the bridges built via the island scan strategies, chiefly near the edges of the parts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Role of scan strategies and heat treatment on grain structure evolution in Fe-Si soft magnetic alloys made by laser-powder bed fusion

A major goal in printing soft magnetic Fe-Si steels using additive manufacturing is to take advantage of the potential for complex geometric designs and site-specific grain control. One major step in the processing of these alloys is understanding how processing parameters might impact how the as-built microstructure responds to annealing (i.e. the annealing response). The impact of scan strategy on the annealing response for thin wall geometries is specifically explored. Two scan strategies were explored for a thin wall geometry that produced a strongly columnar grain structure and equiaxed grain structure. Additionally, samples from both scan strategies annealed at 1200 °C showed a marked difference in annealing response with the more equiaxed sample seeing full recrystallization and grain growth, while the more columnar grain structure saw little change in microstructure. After analysis through characterization techniques and thermal-mechanical simulations Differences in internal energy within the grains were ruled out because calculated GND density values were similar for both samples. The formation of secondary particles was ruled out as a contributing factor due to the type of oxide formations and their size. It was concluded that the contributing factor to the difference in the annealing response were a difference in the resulting grain size and the density of high angle grain boundaries. These two differences were largely attributed to differences in the thermal gradient conditions due to grains preferentially growing in the direction of the steepest thermal gradient.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images

Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.

58 GEOSCIENCES↗
DOE OSTI2026

Ab Initio Modeling of Aqueous Methanol Mixtures at DFT-SCAN Level Using Machine Learning Interatomic Potentials

Abstract Methanol–water mixtures find use in many applications, particularly catalytic energy conversion processes. Their importance has motivated numerous computational studies, most of which employed molecular dynamics based on classical force fields. These enable simulations of large systems on long time scales but do not reliably describe reactive dynamics involving bond breaking and bond formation. In contrast, ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is generally more reliable for such applications but has a high computational cost, which discourages systematic studies of alcohol-water mixtures. To remedy this, we trained a machine learning interatomic potential capable of probing the properties of aqueous methanol mixtures at the DFT level using the SCAN functional. Our results show that SCAN qualitatively reproduces multiple key experimental features arising from the amphiphilic nature of methanol, including density, diffusion coefficients, X-ray structure factors, and Kirkwood–Buff integrals. We also find that structural correlations between water molecules are somewhat overestimated, leading to a stronger preferential association than that predicted by experiments. However, increasing the temperature by 30 K mitigates this effect and also recovers the correct mobilities of both methanol and water. These results indicate that SCAN provides an accurate description of methanol–water mixtures, making it a reliable choice for investigating the reactive dynamics in such systems.

Park, Sanghyun J. [Princeton University , , , ,]↗
DOE OSTI2021

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Simultaneous Observations of Surface Layer Profiles of Humidity, Temperature, and Wind Using Scanning Lidar Instruments

We demonstrate the combination of three Doppler lidars (DLs), a water vapor differential absorption lidar and a temperature rotational Raman lidar for the investigation of the interactions between the land-surface and the atmospheric boundary layer. This combination of scanning lidars was operated for the first time during the Land-Atmosphere Feedback Experiment at the Atmospheric Radiation Measurement program's Southern Great Plain site, Oklahoma, USA, in August 2017, and provided simultaneous surface layer profiles of horizontal wind, humidity and temperature. The horizontal wind profiles were determined using the dual-Doppler method with two DLs. The scans were performed above four towers providing atmospheric variables and fluxes. These combined lidar data allowed for the estimation of the friction velocity as well as the surface latent and sensible heat fluxes. For this purpose, profiles calculated with Monin-Obukhov similarity theory were fitted with the Levenberg-Marquardt nonlinear least squares curve-fitting method to the measured surface-layer lidar profiles. We present case studies of three 50-min periods to illustrate the new method. The measurements resulted in friction velocities of 0.31, 0.29, and 0.38 ms –1 , sensible surface heat fluxes of 312.1, 234.1, and 183.3 Wm –2 and latent surface heat fluxes of 251.8, 227.3, and 274.4 Wm –2 . These values were compared with the in-situ measurements of the towers. Considering the sampling differences and the error analyses, the results agree within 25.1%, 42.5%, and 28.8%, respectively, demonstrating that this synergy of scanning active remote sensing systems can be used to derive surface fluxes with reasonable accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Nanophotonic waveguide chip-to-world beam scanning

A seamless chip-to-world photonic interface enables broad advancements in optical ranging, display, communication, computation and quantum information science. The ideal solution enables two-dimensional scanning of a diffraction-limited beam from anywhere on a photonic integrated circuit to a large number of resolvable spots. Current beam-scanning technologies are limited by a fundamental trade-off: photonic-integrated-circuits with diffractive optics offer scalability but have poor mode quality, whereas inertially limited micromechanical scanners provide high-quality beams but lack scalable integration. Here we report a photonic ski-jump—a nanoscale waveguide monolithically integrated on a piezoelectric cantilever—to overcome these limitations. It passively curls ~90° out-of-plane within a less-than-0.1 mm 2 footprint, emits a submicrometre, broadband diffraction-limited beam, and exhibits kilohertz-rate mechanical resonances with quality factors of over 10,000. Fabricated in a volume complementary metal–oxide–semiconductor (CMOS) foundry, our device enables scalable two-dimensional beam scanning. Driven on-resonance at CMOS-level voltages, it achieves a footprint-adjusted spot rate of 68.6 mega spots s –1 mm–², exceeding state-of-the-art micro-electro-mechanical systems mirrors by more than 50-fold, which is sufficient for one million pixels at 100 Hz from an approximately 1.5 mm diameter footprint. We demonstrate full-colour image and video projection, and single-photon initialization and readout from silicon vacancy centres in diamond. Finally, by demonstrating uniformity across a 64 ski-jump array, we establish a pathway to achieving greater than one gigaspot resolution at kilohertz rates within a sub-5-cm-diameter footprint, creating a seamless optical pipeline between integrated photonic processors and the free-space world.

Displays↗
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 OSTI2020

Localization in the SCAN meta-generalized gradient approximation functional leading to broken symmetry ground states for graphene and benzene

Density functional theory calculations play a central role in understanding chemical and solid-state systems. Progress depends on density functionals that accurately reproduce both energies, for thermochemistry, and properly describe ground states and other properties that are of interest. The Cr dimer, benzene and graphene are especially important benchmark systems for quantum chemistry and condensed matter physics. The Strongly Constrained and Appropriately Normed (SCAN) functional, which is an advanced meta-generalized gradient approximation functional that significantly improves molecular energies is shown to perform poorly for the Cr dimer. This is connected with its poor performance for itinerant solid-state magnets and is a consequence of over localization of electrons, thus illustrating an analogy between the Cr dimer and itinerant magnets. The Cr dimer is a notoriously difficult system for density functionals. However, we additionally find that SCAN predicts an incorrect symmetry broken ground state for 2D graphene and for the benzene molecule, which is surprising considering that ground states of these are known to be well described even by the simplest local density approximation. We show that SCAN overly favors localized spin polarized states, which is a serious deficiency of this approach. Thus, the challenge of finding density functionals that accurately treat both localized and delocalized electronic systems remains.

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

Simplified feedback control system for scanning tunneling microscopy

A Scanning Tunneling Microscope (STM) is one of the most important scanning probe tools available to study and manipulate matter at the nanoscale. In a STM, a tip is scanned on top of a surface with a separation of a few Å. Often, the tunneling current between the tip and the sample is maintained constant by modifying the distance between the tip apex and the surface through a feedback mechanism acting on a piezoelectric transducer. This produces very detailed images of the electronic properties of the surface. The feedback mechanism is nearly always made using a digital processing circuit separate from the user computer. Here, we discuss another approach using a computer and data acquisition through the universal serial bus port. We find that it allows successful ultralow noise studies of surfaces at cryogenic temperatures. We show results on different compounds including a type II Weyl semimetal (WTe2), a quasi-two-dimensional dichalcogenide superconductor (2H–NbSe2), a magnetic Weyl semimetal (Co3Sn2S2), and an iron pnictide superconductor (FeSe).

Martín-Vega, Francisco (ORCID:0000000333177833)↗
DOE OSTI2023

Heat capacity of microgram oxide samples by fast scanning calorimetry

Quantitative scanning calorimetry on microgram-sized samples opens a broad, new range of opportunities for studying the thermodynamic properties of quantity-limited materials, including those produced under extreme conditions or found as rare accessory minerals in nature. We calibrated the Mettler Toledo Flash DSC 2+ calorimeter to obtain quantitative heat capacities in the range 200–350 °C, using samples weighing between 2 and 11.5 μg. Our technique is applied to a new set of oxide materials to which it has never been used before, without the need for melting, glass transitions, or phase transformations. Heat capacity data were obtained for silica in the high pressure stishovite (rutile) structure, dense post-stishovite glass, standard fused quartz, and for TiO2 rutile. These heat capacities agree within 5%–15% with the literature values reported for rutile, stishovite, and fused SiO2 glass. The heat capacity of post-stishovite glass, made by heating stishovite to 1000 °C, is a newly reported value. After accurate calibrations, measured heat capacities were then used to calculate masses for samples in the microgram range, a substantial improvement over measurement in conventional microbalances, which have uncertainties approaching 50%–100% for such small samples. Since the typical uncertainty of heat capacities measured on 10–100 mg samples in conventional differential scanning calorimetry is typically 7% (1%–5% with careful work), flash differential scanning calorimetry, using samples a factor of 1000 smaller, increases the uncertainty of heat capacity measurements by a factor of <3, opening the door for meaningful measurements on ultra-small, high-pressure samples and other quantity-limited materials.

Instruments & Instrumentation↗
DOE OSTI2024

Image plate multi-scan response to fusion protons in the range of 1–14 MeV

Image plates (IPs) are a quickly recoverable and reusable radiation detector often used to measure proton and x-ray fluence in laser-driven experiments. Recently, IPs have been used in a proton radiography detector stack on the OMEGA laser, a diagnostic historically implemented with CR-39, or radiochromic film. The IPs used in this and other diagnostics detect charged particles, neutrons, and x-rays indiscriminately. IPs detect radiation using a photo-stimulated luminescence (PSL) material, often phosphor, in which electrons are excited to metastable states by ionizing radiation. Protons at MeV energies deposit energy deeper into the IP compared with x rays below ∼20 keV due to the Bragg peak present for protons. This property is exploited to discriminate between radiation types. Doses of mono-energetic protons between 1.7 and 14 MeV are applied to IPs using the MIT linear electrostatic ion accelerator. This paper presents the results from consecutive scans of IPs irradiated with different proton energies. The PSL ratios between subsequent scans are shown to depend on proton energy, with higher energy protons having lower PSL ratios for each scan. This finding is separate from the known energy dependence in the absolute sensitivity of IPs. The results can be compared to complimentary work on x rays, showing a difference between protons and x rays, forging a path to discriminate between proton and x-ray fluence in mixed radiation environments.

Instruments & Instrumentation↗