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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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Careful understanding of electron-beam excitation processes is crucial to the interpretation of cathodoluminescence g 2 ( τ ) imaging of nanoscale excited state dynamics.
Carbon nanotubes (CNT) enable nanoelectromechanical systems (NEMS) because of their inherent nanostructure, intrinsic electric conductivity and mechanical resilience. The collaborative work between Stanford (experiment) and NASA Ames (theory and simulation) has made progress in two types of CNT based NEMS for nanoelectronics and sensor applications. The CNT tipped scanning probe microscopy (SPM) is a NEMS in which CNT tips are used for nanoscale probing, imaging and manipulating. It showed great improvement in probing surfaces and biological systems over conventional tips. We have recently applied it to write (lithography) and read (image) uniform SiO2 lines on large Si surface area at speed up to 0.5 mm per s. Preliminary work using approximately 10 nm multiwall nanotube tips produced approximately 10 nm structures and showed that the CNT tips didn't wear down when crashed as conventional tips often do. This presents a solution to the long standing tip-wear problem in SPM nanolithography. We have also explored potential of CNT tips in imaging DNA in water. Preliminary experiment using 10 nm CNT tips reached 5 nm resolution. The 1 nm nanolithography and 1 nm DNA imaging can be expected by using approximately 1 nm CNT tips. In contrast to CNT tipped SPM, we also fabricated CNT devices on silicon wafer in which CNTs connect patterned metallic lines on SiO2/Si by a simple chemical vapor deposition process. Using conventional lithography for silicon wafer, we have been able to obtain CNT based transistors and sensors. Investigations of the CNT NEMS as physical, biological and chemical sensors are in progress and will be discussed.
Scanning probe microscopy (SPM) is an important tool for performing measurements at the nanoscale in imaging bacteria or proteins in biology, as well as in the electronics industry. An essential element of SPM is a sharp, stable tip that possesses a small radius of curvature to enhance spatial resolution. Existing techniques for forming such tips are not ideal. High-aspect-ratio, monolithically integrated, as-grown carbon nanofibers (CNFs) have been formed that show promise for SPM applications by overcoming the limitations present in wet chemical and separate substrate etching processes.
A scanning probe microscope and methodology called resonant difference-frequency atomic force ultrasonic microscopy (RDF-AFUM), employs an ultrasonic wave launched from the bottom of a sample while the cantilever of an atomic force microscope, driven at a frequency differing from the ultrasonic frequency by one of the contact resonance frequencies of the cantilever, engages the sample top surface. The nonlinear mixing of the oscillating cantilever and the ultrasonic wave in the region defined by the cantilever tip-sample surface interaction force generates difference-frequency oscillations at the cantilever contact resonance. The resonance-enhanced difference-frequency signals are used to create images of nanoscale near-surface and subsurface features.
Deep learning (DL) is an emerging analysis tool across the sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nanoscale deeply subdiffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a time scale that is at least 3 orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.
We record nanoscale-resolved chemical images of thiobenzonitrile (TBN)-functionalized smooth gold nanospheres on silicon via tip-enhanced Raman (TER) nano-spectroscopy. The recorded images trace the nascence of the familiar doughnut-shaped scattering profile of nanoparticles on silicon at its origin (the particle surface), which appears as a horseshoe-shaped scattering pattern under our experimental conditions. The local optical field maps are in agreement with their simulated finite-difference time-domain analogues. Analysis of the recorded spectra with the aid of ab initio molecular dynamics-based Raman spectral simulations further suggests that optical rectification and molecular charging take place throughout the course of atomic force microscopy-based TER nanoscale chemical imaging.
Although it is thought that there is lateral heterogeneity of lipid and protein components within biological membranes, probing this heterogeneity has proven challenging. The difficulty in such experiments is due to both the small length scale over which such heterogeneity can occur, and the significant perturbation resulting from fluorescent or spin labeling on the delicate interactions within bilayers. Atomic recombination during dynamic nanoscale secondary ion imaging mass spectrometry (NanoSIMS) is a non-perturbative method for examining nanoscale bilayer interactions. Atomic recombination is a variation on conventional NanoSIMS imaging, whereby an isotope on one molecule combines with a different isotope on another molecule during the ionization process, forming an isotopically enriched polyatomic ion in a distance-dependent manner. In this work, we show that the recombinant ion, 13 C 2 2 H – , is formed in high yield from 13 C- and 2 H-labeled lipids. The low natural abundance of triply labeled acetylide also makes it an ideal ion to probe GM 1 clusters in model membranes and the effects of cholesterol on lipid–lipid interactions. We find evidence supporting the cholesterol condensation effect as well as the presence of nanoscale GM 1 clusters in model membranes.
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.
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Crystal growth from nanoscale constituents is a ubiquitous phenomenon in biology, geology and materials science. Numerous studies have focused on understanding the onset of nucleation and on producing high-quality crystals by empirically sampling constituents with different attributes and varying the growth conditions. However, the kinetics of post-nucleation growth processes, an important determinant of crystal morphology and properties, have remained underexplored due to experimental challenges associated with real-space imaging at the nanoscale. Here we report the imaging of the crystal growth of nanoparticles of different shapes using liquid-phase transmission electron microscopy, resolving both lateral and perpendicular growth of crystal layers by tracking individual nanoparticles. We observe that these nanoscale systems exhibit layer-by-layer growth, typical of atomic crystallization, as well as rough growth prevalent in colloidal systems. Surprisingly, the lateral and perpendicular growth modes can be independently controlled, resulting in two mixed crystallization modes that, until now, have received only scant attention. Combining analytical considerations with molecular dynamics and kinetic Monte Carlo simulations, we develop a comprehensive framework for our observations, which are fundamentally determined by the size and shape of the building blocks. Furthermore, these insights unify the understanding of crystal growth across four orders of magnitude in particle size and suggest novel pathways to crystal engineering.
The operating lifetime of Ni-YSZ fuel electrodes used in solid oxide electrolysis cells and fuel cells (SOECs and SOFCs) is limited by Ni redistribution, one of the primary degradation mechanisms that must be overcome to extend the longevity and maximize the performance of SOECs and SOFCs. To achieve this, 3D microstructural data is needed to relate both initial performance and performance loss over time to microstructural properties and their evolution throughout operation under various conditions. However, 3D microstructure data remains relatively scarce within the literature due to multiple challenges in acquiring and analyzing such data reliably. This work presents a workflow for acquiring and processing synchrotron X-ray nanoscale computed tomography (nano-CT) data from Ni-YSZ electrodes. Parameters for each step in the nano-CT workflow are described up to the final result (a 3D reconstruction), with particular emphasis on image alignment using freely available software. Following the results of a parametric sweep of the image alignment step, high contrast, low signal-to-noise 3D nano-CT data is obtained with relatively short compute times. While the exact methods best suited to samples with different microstructural qualities, or similar Ni-YSZ nano-CT data obtained from other sources may deviate from the solution found herein, this work also generalizes the decision points and evaluation of each step to provide a starting point to adapt this workflow to other datasets.
Recent developments in X-ray science provide methods to probe deeply embedded mesoscale grain structures and spatially resolve them using dark field X-ray microscopy (DFXM). Extending this technique to investigate weak diffraction signals such as magnetic systems, quantum materials and thin films prove challenging due to available detection methods and incident X-ray flux at the sample. We present a direct detection method developed in conjunction with KAImaging which focuses on DFXM studies in the hard X-ray range of 10s of keV and above capable of approaching nanoscale resolution. Additionally, we compare this direct detection scheme with routinely used scintillator-based optical detection and achieve an order of magnitude improvement in exposure times allowing for imaging of weakly diffracting ordered systems.
Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetisation patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using superconducting quantum interference devices, spin centre and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoscale magnetic resonance imaging. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, three-dimensional and geometrically curved objects of different material classes including two-dimensional materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.
Abstract Single impurities in insulators are now often used for quantum sensors and single photon sources, while nanoscale semiconductor doping features are being constructed for electrical contacts in quantum technology devices, implying that new methods for sensitive, non‐destructive imaging of single‐ or few‐atom structures are needed. X‐ray fluorescence (XRF) can provide nanoscale imaging with chemical specificity, and features comprising as few as 100 000 atoms have been detected without any need for specialized or destructive sample preparation. Presently, the ultimate limits of sensitivity of XRF are unknown – here, gallium dopants in silicon are investigated using a high brilliance, synchrotron source collimated to a small spot. It is demonstrated that with a single‐pixel integration time of 1 s, the sensitivity is sufficient to identify a single isolated feature of only 3000 Ga impurities (a mass of just 350 zg). With increased integration (25 s), 650 impurities can be detected. The results are quantified using a calibration sample consisting of precisely controlled numbers of implanted atoms in nanometer‐sized structures. The results show that such features can now be mapped quantitatively when calibration samples are used, and suggest that, in the near future, planned upgrades to XRF facilities might achieve single‐atom sensitivity.
Strong light-matter interactions in localized nano-emitters placed near metallic mirrors have been widely reported via spectroscopic studies in the optical far-field. Here, we report a near-field nano-spectroscopic study of localized nanoscale emitters on a flat Au substrate. Using quasi 2-dimensional CdSe/Cd x Zn 1-x S nanoplatelets, we observe directional propagation on the Au substrate of surface plasmon polaritons launched from the excitons of the nanoplatelets as wave-like fringe patterns in the near-field photoluminescence maps. These fringe patterns were confirmed via extensive electromagnetic wave simulations to be standing-waves formed between the tip and the edge-up assembled nano-emitters on the substrate plane. We further report that both light confinement and in-plane emission can be engineered by tuning the surrounding dielectric environment of the nanoplatelets. Our results lead to renewed understanding of in-plane, near-field electromagnetic signal transduction from the localized nano-emitters with profound implications in nano and quantum photonics as well as resonant optoelectronics.
X-ray ptychographic tomography is a nondestructive method for three dimensional (3D) imaging with nanometer-sized resolvable features. The size of the volume that can be imaged is almost arbitrary, limited only by the penetration depth and the available scanning time. Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors. The proposed 3D reconstruction method “RAPID” relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth. It is then trained to reproduce equal fidelity from much fewer angles. After training, it performs with similar fidelity on the hitherto unexamined portions of the object, previously not shown during training, with a limited set of acquisitions. In our experimental demonstration, the nominal number of angles was 349 and the reduced number of angles was 21, resulting in a x140 aggregate speedup over a volume of 4.48 x 93.18 x 3.92 μm 3 and with (14nm) 3 feature size, i.e. ~ 10 8 voxels. RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way. We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’ range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.
Vapor deposited patinas (VDP) on lunar soil grains consist of a thin (less than 1 micron) layer of amorphous silicate (glass) embedded with nanoscale Fe(sup 0) globules as seen in many TEM images. VDPs are also present on larger space-weathered lunar rocks; these larger samples will not be discussed here although the process of vapor deposition is common to exposed grains of all sizes. Whether or not the majority of the Fe(sup 0) globules present in lunar soils reside in vapor deposited patina is a matter of some concern. Some Fe(sup 0) globules are clearly seen to reside within the glass of agglutinates and might represent remobilized Fe(sup 0) in agglutinitic melts. remobilized Fe(sup 0) in agglutinitic melts. We argue that because VDP coatings are present only on the surfaces of lunar soil grains, their distribution as a surface correlated component (SCC) of lunar soils should parallel those of Solar Wind Elements (SWE) implanted in the outermost rinds of lunar soil grains. SWE residing in the interior of soils grains make up the volume correlated component (VCC). Relative to Fe(sup 0) in VDP, the distribution of various SWE have been studied well. The reason is understandable because instrumentation for nanoscale imaging is not ubiquitous. In this study we use the distribution of SWE in lunar soils as a guide to understanding the fate of Fe(sup 0) in VDP.