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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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Hypothesis-learning-driven automated scanning probe microscopy allows physical discovery during experiments, where the hypothesis learning algorithm implemented on an operating microscope identifies the physical mechanism behind the material’s response
The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.
This study directly compares the mechanical behavior of novel molecular layer deposition (MLD) and analogous interfacial polymerization (IP) polyamide thin films in environments relevant to reverse osmosis (RO) membrane operation. The elastic modulus of the films was determined using atomic force microscopy (AFM) in dry, hydrated, and chlorinated states. Surface roughness characteristics were also obtained given their potential influence on AFM modulus measurements. The much smoother MLD films demonstrated a statistically higher modulus in all states as compared to their IP counterparts. The MLD films maintained a modulus ~3X and ~5X greater than that of IP films after hydration and chlorination, respectively. Such differences in behavior may be due to the higher density and correspondingly lower void content of the MLD films. Results from this study provide a rationale for future development of MLD for fabrication of polyamide films for incorporation in RO membranes.
The advent of optical super-resolution microscopy (OSRM) over two decades ago has transformed light-mediated interrogation of systems down to the nanoscale. This innovative set of approaches to optics breaks the so-called diffraction limit of light (~200 nm), allowing for the first time to use optics in the far field to visualize behavior on nanoscopic length scales. While these visualization tools have rapidly found widespread use in biology and related fields, their implementation in materials and, more specifically, in polymer science has been far slower. To overcome possible barriers and highlight possible future directions, we present in this paper an introduction to OSRM for polymer scientists, beginning with an overview of relevant techniques and existing optical probes. We then show and examine the first examples of OSRM adaptation across major areas of polymer science, including: polymerization and structural characterization, self-assembly and solution behavior, bulk structure and behavior, crystallization, gel structure and behavior, phase transitions, and biofunctionality. We hope and anticipate that the discussions provided in this review will draw the attention of the polymer community to the capacities of these hitherto underexplored optical visualization techniques to further transform polymer science.
The subsurface dislocation content in a Ti-5Al-2.5Sn (wt%) uniaxial tension sample deformed at ambient temperature was characterized by peak streak analysis of micro-Laue diffraction patterns collected non-destructively by differential aperture X-raymicroscopy, and with focused ion beam transmission electron microscopy of material in the same volume. This comparison reveals that micro-Laue diffraction streak analysis based on an edge dislocation assumption can accurately identify the dominant dislocation slip system history (Burgers vector and plane observed by TEM), despite the fact that dislocations have predominantly screw character. As a result, other dislocations identified by TEM were not convincingly discernible from the peak streak analysis.
We report on nm-resolution imaging of charge-carrier distribution around local potential-induced degradation (PID) shunting defects using scanning capacitance microscopy. We imaged on cross sections of heavily field-degraded module areas, cored out and selected by mm-scale photoluminescence imaging. We found localized areas with abnormal carrier behavior induced by the PID defects: the apparent n-type carrier extends vertically into the absorber to ~1-2 um from the cell surface, and laterally in similar lengths; in defect-free areas, the n-type carrier extends ~0.5 um, which is consistent with the junction depth. For comparison, we also investigated areas of the same module exhibiting the least PID stress, and we found no such heavily damaged junction area. Instead, we found slightly abnormal carrier behavior, where the carrier-type inversion in the absorber did not occur, but the p-type carrier concentration changed slightly in a much smaller lateral length of ~300 nm. These nano-electrical findings may indicate a possible mechanism that the existing extended defects, which may not be significantly harmful to cell performance, can be changed by PID to heavily damaged junction areas.
Quantum-enhanced microscopes capable of probing classically inaccessible material properties have drawn increasing interest, but most proof-of-principle quantum microscopes have not surpassed classical limits. Here, we discuss the promise and obstacles associated with the use of quantum light with reduced noise (‘squeezed’ light) for quantum-enhanced atomic force microscopy, bioimaging, magneto-optical measurements, and Raman spectroscopy.
Critical applications of spin polarized low energy electron microscopy (SPLEEM) employ this technique's vector imaging capability to resolve domain wall (DW) spin textures. Studying several thin film systems including Co/W(110), Co/Cu(001) and (Co/Ni) n /W(110), we demonstrate that an additional contrast can appear at magnetic DWs. By imaging the magnetization as a function of electron landing energy, electron energies are selected at which the magnetic domain contrast vanishes. Surprisingly, under such conditions of zero contrast between magnetic domains, we observe the appearance of magnetic contrast outlining the DWs. This DW contrast fails to depend on the DW spin texture. Instead, our measurements show that this DW contrast results from a combination of the energy-dependence of the spin reflectivity asymmetry of the magnetic film, the finite energy width of the spin polarized electron source, and the dispersion of the magnetic prism array that separates the illumination and imaging columns of the instrument. Awareness of this DW contrast mechanism is useful to aid correct interpretation of SPLEEM images.
High resolution electron backscatter diffraction (HREBSD), an SEM-based diffraction technique, may be used to measure the lattice distortion of a crystalline material and to infer the geometrically necessary dislocation content. Uncertainty in the image correlation process used to compare diffraction patterns leads to an uneven distribution of measurement noise in terms of the lattice distortion, which results in erroneous identification of dislocation type and density. This work presents a method of reducing noise in HREBSD dislocation measurements by removing the effect of the most problematic components of the measured distortion. The method is then validated by comparing with TEM analysis of dislocation pile-ups near a twin boundary in austenitic stainless steel and with ECCI analysis near a nano-indentation on a tantalum oligocrystal. Finally, the HREBSD dislocation microscopy technique is able to resolve individual dislocations visible in TEM and ECCI and correctly identify their Burgers vectors.
The development of four-dimensional (4D) scanning transmission electron microscopy (STEM) using fast detectors has opened-up new avenues for addressing some of longstanding challenges in electron imaging. One of these challenges is how to image severely distorted crystal lattices, such as at a dislocation core. Here we develop a new 4D-STEM technique, called Cepstral STEM, for imaging disordered crystals using electron diffuse scattering. In contrast to analysis based on Bragg diffraction, which measures the average and periodic scattering potential, electron diffuse scattering can detect fluctuations caused by crystal disorder. Local fluctuations of diffuse scattering are captured by scanning electron nanodiffraction (SEND) using a coherent probe. The harmonic signals in electron diffuse scattering are detected through Cepstral analysis and used for imaging. By integrating Cepstral analysis with 4D-STEM, we demonstrate that information about the distortive part of electron scattering potential can be separated and imaged at nm spatial resolution. Here, we apply the technique to the analysis of a dislocation core in SiGe and lattice distortions in a high entropy alloy.
Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacements of atomic columns from high-symmetry positions can be used to map strain, polarization, octahedra tilts, and other physical and chemical order parameter fields. The latter can be used as inputs into mesoscopic and atomistic models, providing insight into the correlative relationships and generative physics of materials on the atomic level. However, these quantitative applications of STEM necessitate understanding the microscope induced image distortions and developing the pathways to compensate them both as part of a rapid calibration procedure for in situ imaging, and the post-experimental data analysis stage. Here, we explore the spatiotemporal structure of the microscopic distortions in STEM using multivariate analysis of the atomic trajectories in the image stacks. Based on the behavior of principal component analysis (PCA), we develop the Gaussian process (GP)-based regression method for quantification of the distortion function. The limitations of such an approach and possible strategies for implementation as a part of in-line data acquisition in STEM are discussed. Here, the analysis workflow is summarized in a Jupyter notebook that can be used to retrace the analysis and analyze the reader's data.
Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, in this work, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.
The sensitivity and the precision of the Geometric Phase Analysis (GPA) method for strain characterization is a topic widely discussed in the literature and is usually difficult to quantify. Indeed, the GPA precision is intricately linked to the resolution of the strain maps defined when masking the periodic reflections in Fourier space. In this study an additional parameter, sampling, is proposed to be analyzed regarding the precision of GPA by developing the concept of a phase noise in the GPA equations. Both experimentally and theoretically, the following article demonstrates how the precision, and the sensitivity of the GPA method is improved when using a larger pixel spacing to record an electron micrograph in Scanning Transmission Electron Microscopy (STEM). In conclusion, the counterintuitive concept of increasing the field of view to improve the GPA precision results in an extension of the application of strain characterization methods in STEM towards low deformation levels.
Under illumination by a 300 keV electron beam, oxygen vacancy ordering structures are induced within cerium oxide grains. Here, our high-resolution transmission electron microscopy (HRTEM) study, supported by image simulation, reveals the evolution of these structures as vacancy concentration increases. The observed fluorite-type superlattice structures are identified as CeO 1.825 , CeO 1.75 , Ce 2 O 3 , displaying a gradient in oxygen vacancy concentration moving away from the grain surface. Correspondingly, the structural sequence transitions from Ce 2 O 3 to CeO 1.75 and then to CeO 1.825 . Without the constraints of surrounding grains, fluorite-type Ce 2 O 3 nanocrystals show a preference for transformation into an A-type trigonal structure. Notably, at temperatures up to 200°C, only the perfect fluorite structure is observed. Structural models were validated through both [110] and [001] projections. Our findings further confirm lattice expansion associated with local oxygen vacancy enrichment, which can be compensated by the formation of stacking faults, where a {111} oxygen plane is lost at defect sites.
Abstract In this study, the cross-section of electron-selective titanium oxide (TiO 2 ) contacts for n -type crystalline silicon solar cells were investigated by transmission electron microscopy. It was revealed that the excellent cell efficiency of 21.6% obtained on n -type cells, featuring SiO 2 /TiO 2 /Al rear contacts and after forming gas annealing (FGA) at 350°C, is due to strong surface passivation of SiO 2 /TiO 2 stack as well as low contact resistivity at the Si/SiO 2 /TiO 2 heterojunction. This can be attributed to the transformation of amorphous TiO 2 to a conducting TiO 2- x phase. Conversely, the low efficiency (9.8%) obtained on cells featuring an a-Si:H/TiO 2 /Al rear contact is due to severe degradation of passivation of the a-Si:H upon FGA.
Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.
Lithium-ion batteries (LIBs) commercially dominate portable energy storage and have been extended to hybrid/electric vehicles by utilizing electrode materials with enhanced energy density. However, the energy density and cycling life of LIBs must extend beyond the current reach of commercial electrodes to meet the performance requirements for transportation applications. Carbon-based anodes, serving as the main negative electrodes in LIBs, have an intrinsic capacity limitation due to the intercalation mechanism. Some nanostructured carbon materials offer very interesting reversible capacities and can be considered as future anode materials. However, their fabrication processes are often complicated and expensive. Theoretically, using a lithium metal anode is the best way of delivering high energy density due to its largest theoretical capacity of more than 3800 mAh g -1 ; however, lithium metal is highly reactive with liquid electrolytes. Alternative anodes are being explored, including other lithium-reactive metals, such as Si, Ge, Zn, V, and so forth. These metals react reversibly with a large amount of Li per formula unit to form lithium–metal alloys, rendering these materials promising candidates for next-generation LIBs with high energy density. Though, most of these pure metallic anodes experience large volume changes during lithiation and delithiation processes that often results in cracking of the anode material and a loss electrical contact between the particles.Nanosized metal sulfides were recently found to possess better cycling stability and larger reversible capacities over pure metals. Further improvements and developments of metal sulfide-based anodes rely on a fundamental understanding of their electrochemical cycling mechanisms. Not only must the specific electrochemical reactions be correctly identified, but also the microstructural evolution upon electrochemical cycling, which often dictates the cyclability and stability of nanomaterials in batteries, must be clearly understood. Probing these dynamic evolution processes, i.e. the lithiation reactions and morphology evolutions, are often challenging. It requires both high-resolution chemical analysis and microstructural identification. In situ transmission electron microscopy (TEM) coupled with electron energy loss spectroscopy (EELS) has recently been raised as one of the most powerful techniques for monitoring electrochemical processes in anode materials for LIBs.In this work, we focus on elucidating the origin of the structural stability of SnS 2 during electrochemical cycling by revealing the microstructural evolution of SnS 2 upon lithiation using in situ TEM. Crystalline SnS 2 was observed to undergo a two-step reaction after the initial lithium intercalation: (1) irreversible formation of metallic tin and amorphous lithium sulfide and (2) reversible transformation of metallic tin to Li–Sn alloys, which is determined to be the rate-determining step. More interestingly, it was discovered that a self-assembled composite framework formed during the irreversible conversion reaction, which has not been previously reported. Crystalline Sn nanoparticles are well arranged within an amorphous Li 2 S “matrix” in this self-assembled framework. This nanoscale framework confines the locations of individual Sn nanoparticles and prevents particle agglomeration during the subsequent cycling processes, therefore providing desired structural tolerance and warranting a sufficientelectron pathway. Finally, our results not only explain the outstanding cycling stability of SnS 2 over metallic tin anodes, but also provide important mechanistic insights into the design of high-performance electrodes for next-generation LIBs through the integration of a unique nanoframework.
For over 25 years, transmission electron microscopy (TEM) has provided a method for the study of aerosol particles with sizes from below the optical diffraction limit to several microns, resolving the particles as well as smaller features. The wide use of this technique to study aerosol particles has contributed important insights about environmental aerosol particle samples and model atmospheric systems. TEM produces an image that is a 2D projection of aerosol particles that have been impacted onto grids and, through associated techniques and spectroscopies, can contribute additional information such as the determination of elemental composition, crystal structure, and 3D particle structures. Soot, mineral dust, and organic/inorganic particles have all been analyzed using TEM and spectroscopic techniques. TEM, however, has limitations that are important to understand when interpreting data including the ability of the electron beam to damage and thereby change the structure and shape of particles, especially in the case of particles composed of organic compounds and salts. In this paper, we concentrate on the breadth of studies that have used TEM as the primary analysis technique. Another focus is on common issues with TEM and cryogenic-TEM. Insights for new users on best practices for fragile particles, that is, particles that are easily susceptible to damage from the electron beam, with this technique are discussed. Tips for readers on interpreting and evaluating the quality and accuracy of TEM data in the literature are also provided and explained.