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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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Operando 3-D microscopy of individual pits during Li metal anode stripping demonstrates anisotropic expansion, edge faceting, and surface microstructure effects.
In femtosecond (fs) 4D ultrafast electron microscopy (UEM), a tradeoff is made between photoelectrons per packet and time resolution. One consequence of this can be longer-than-desirable acquisition times for low-density packets, and particularly for low repetition rates when complete photothermal dissipation is required. Thus, gaining an understanding of photoelectron trajectories in the gun region is important for identifying factors that limit collection efficiency (CE; fraction of photoelectrons that enter the illumination system). Here, we continue our work on the systematic study of photoelectron trajectories in the gun region of a Thermo Fisher/FEI Tecnai Femto UEM, focusing specifically on CE in the single-electron regime. Using General Particle Tracer, calculated field maps, and the exact architecture of the Tecnai Femto UEM, we simulated the effects of fs laser parameters and key gun elements on CE. The results indicate CE strongly depends upon the laser spot size on the source, the (unbiased) Wehnelt aperture diameter, and the incident photon energy. The CE dispersion with laser spot size is found to be strongly dependent on aperture diameter, being nearly dispersionless for the largest apertures. A gun crossover is also observed, with the beam-waist position being dependent on the aperture diameter, further illustrating that the Wehnelt aperture acts as a simple, fixed electrostatic lens in UEM mode. This work provides further insights into the operational aspects of fs 4D UEM.
Using atomically flat SrIrO 3 as a model electrocatalyst for water oxidation, we study the interplay between Sr 2+ leaching and perovskite dissolution at the nanometer scale through electrochemical atomic force microscopy.
Electrostatic forces complicate the interpretation of piezoresponse force microscopy (PFM). Electrostatic blind spot (ESBS) PFM overcomes these complications by placing the detection laser where it is sensitive piezoresponse but not electrostatics.
Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42,650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. Further, a classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr 3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.
In this study, the thermal stability of Au–Al 2 O 3 core–shell and Au nanowires was investigated by in situ scanning transmission electron microscopy and other techniques. Here, the nanowires were synthesized by the helium droplets method and deposited on various substrates. The in situ characterization of Au–Al 2 O 3 thermal stability demonstrated a substantially enhanced stability as compared to that of pure Au nanowires, which can be a transformative approach to design more durable Au-based nanocatalysts. Our study also revealed the existence of strong metal–support bonding in the Au/Al 2 O 3 system.
In operando Raman microscopy has revealed that the reduction of Li 1.5 Al 0.5 Ge 1.5 (PO 4 ) 3 (LAGP) to Ge particles surrounded by Li-phosphates occurs in reaction hotspots at the Cu/LAGP interface.
The rise of electron microscopy has expanded our ability to acquire nanometer and atomically resolved images of complex materials. The resulting vast datasets are typically analyzed by human operators, an intrinsically challenging process due to the multiple possible analysis steps and the corresponding need to build and optimize complex analysis workflows. We present a methodology based on the concept of a Reward Function coupled with Bayesian Optimization, to optimize image analysis workflows dynamically. The Reward Function is engineered to closely align with the experimental objectives and broader context and is quantifiable upon completion of the analysis. Here, cross-section, high-angle annular dark field (HAADF) images of ion-irradiated (Y, Dy)Ba 2 Cu 3 O 7–δ thin-films were used as a model system. The reward functions were formed based on the expected materials density and atomic spacings and used to drive multi-objective optimization of the classical Laplacian-of-Gaussian (LoG) method. These results can be benchmarked against the DCNN segmentation. This optimized LoG* compares favorably against DCNN in the presence of the additional noise. We further extend the reward function approach towards the identification of partially-disordered regions, creating a physics-driven reward function and action space of high-dimensional clustering. We pose that with correct definition, the reward function approach allows real-time optimization of complex analysis workflows at much higher speeds and lower computational costs than classical DCNN-based inference, ensuring the attainment of results that are both precise and aligned with the human-defined objectives.
In this paper, we present a new machine learning (ML) workflow with unsupervised learning techniques to identify domains within atomic force microscopy (AFM) images obtained from polymer films.
Scanning probe microscopy can be used to obtain topographical, mechanical, electrical, and electrochemical information on a wide range of materials in a variety of environments, including in situ and operando studies for rechargeable battery systems.
Through scanning electrochemical microscopy and computational simulation, we show that hydrogen functionalization on graphitic carbon electrodes increases electron transfer kinetics to redox-active species in non-aqueous media.
We introduce operando quantitative electrochemical fluorescence state of charge mapping (QEFSM), a non-invasive technique to study operating electrochemical systems along with a new design of optically transparent microfluidic redox flow cells compatible with the most demanding optical requirements. QEFSM allows quantitative mappings of the concentration of a particular oxidation state of a redox-active species within a porous electrode during its operation. In this study, we used confocal microscopy to map the fluorescence signal of the reduced form of 2,7-anthraquinone disulfonate (AQDS) in a set of multistep-chronoamperometry experiments. Calibrating these images and incorporating an analytical model of quinhydrone heterodimer formation with no free parameters, and accounting for the emission of each species involved, we determined the local molecular concentration and the state of charge (SOC) fields within a commercial porous electrode during operation. With this method, electrochemical conversion and species advection, reaction and diffusion can be monitored at heretofore unprecedented transverse and axial resolution (1 μm and 25 μm, respectively) at frame rates of 0.5 Hz, opening new routes to understanding local electrochemical processes in porous electrodes. Here, we observed pore-scale SOC inhomogeneities appearing when the fraction of electroactive species converted in a single pass through the electrode becomes large.
Here, we investigate the impact of X-ray-induced damage on the evolution of battery cathodes using synchrotron transmission X-ray microscopy. In an operando coin cell, exposure to the X-ray beam can induce bubble formation, potentially rendering cathode particles electrochemically inactive and displacing particles from their initial position. However, utilizing external pressure on a pouch cell can mitigate bubble formation and its associated effects.
Rare-earth nickelates, such as LaNiO 3 (LNO), exhibit complex electronic properties, with ordered oxygen vacancies (OOV) influencing conductivity and magnetic behavior. We investigate the structural stability of strain-induced OOV phases in LNO thin films grown on SrTiO 3 substrates and the impact of Ruddlesden–Popper (RP) faults. Using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and integrated differential phase contrast (iDPC) STEM imaging, we conducted atomic-scale structural and compositional analyses of OOV. Geometric phase analysis (GPA) was employed to measure the strain in fault-free and RP fault regions, while density functional theory (DFT) calculations explored different OOV arrangements in the LNO phase. Simulated iDPC-STEM imaging of energy-stabilized structures was performed to correlate with experimental results. Here, our findings reveal superstructure modulation in the chemical composition and atomic-scale lattice structure in LNO, primarily due to the formation of the OOV in Ni–O layers of the LaNiO 2.5 phase. The out-of-plane compressive strain of about 2% stabilizes this phase, reducing the strain, diminishing OOV, and transforming them into LNO.
Quantum entanglement is a fundamental resource for quantum information processing, and its controlled generation and detection remain key challenges in scalable quantum architectures. Here, we numerically demonstrate the deterministic generation of entangled spin states in a solid-state platform by implementing quantum gates via electron spin resonance combined with scanning tunneling microscopy (ESR-STM). Using two titanium atoms on a MgO/Ag(100) substrate as a model, we construct a two-qubit system whose dynamics are coherently manipulated through tailored microwave pulse sequences. We generate Bell states by implementing a Hadamard gate followed by a controlled-NOT gate, and evaluate its fidelity and concurrence using the quantum-master equation-based code TimeESR. Our results demonstrate that ESR-STM can create entangled states with significant fidelity. This study paves the way for the realization of atom-based quantum circuits and highlights ESR-STM as a powerful tool for probing and engineering entangled states on surfaces.
Polysaccharides and proteoglycans are widely associated with the organic matrix at sites of CaCO 3 biomineralization, and previous studies indicate that these macromolecules may confer greater roles in mineral nucleation than previously recognized. This investigation uses in situ liquid-phase transmission electron microscopy (LP-TEM) to observe CaCO 3 nucleation onto aminated silica (SiO 2 –NH 3 + ) nanoparticles treated with a layer of chitosan (near-neutral derivative of chitin) or heparin (a carboxylated and highly sulfated glycosaminoglycan). In the absence of polysaccharides, few CaCO 3 particles formed and exhibited mobility. However, the SiO 2 –NH 3 + nanoparticles were enveloped in a region of higher mass density relative to the bulk solution, suggesting the development of a local solute-rich environment that surrounds the charged NH 3 + groups. The heparin- or chitosan-coated silica particles also exhibited regions of higher mass density around the nanoparticles. In the presence of these polysaccharide coatings, we observed the nucleation of abundant CaCO 3 particles whereby the polyanionic heparin promoted more nucleation than the weakly cationic chitosan. Many crystallites appeared to form at the polysaccharide–TEM cell membrane–solution interface, further indicating interfacial and macromolecule-specific control on crystallization. The combined results demonstrate that chitosan and heparin have an appreciable effect on the timing, size, and location of CaCO 3 nucleation compared to the polysaccharide-free nanoparticles.
Scanning tunneling microscopy was used to investigate the arrangement of ferrocenecarboxylic acid (FcCOOH) monolayers on the Ag(111) surface. Four distinct structures were observed, none of which had previously been observed on other surfaces. Structural analysis indicates that these assemblies are primarily composed of dimers. VASP calculations support the molecular assignments of the monolayer structures, while ESI-MS experiments confirm that dimers are the predominant species in solution. Of particular note, the cyclic pentamers and aperiodic packing observed for FcCOOH on Au(111) were not observed, despite (1) the similarity of the Ag(111) and Au(111) surfaces in reactivity, flatness, and lattice constant, and (2) prior explanation of the Au(111) monolayer in terms of molecule–molecule interactions alone. It is clear that while the surface does not have a template structure, it has a significant influence on which structures are formed.
Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.