Sequential Adaptive Detection for In-Situ Transmission Electron Microscopy (TEM)

We develop new efficient online algorithms for detecting transient sparse signals in TEM video sequences, by adopting the recently developed framework for sequential detection jointly with online convex optimization [1]. We cast the problem as detecting an unknown sparse mean shift of Gaussian observations, and develop adaptive CUSUM and adaptive SSRS procedures, which are based on likelihood ratio statistics with post-change mean vector being online maximum likelihood estimators with $\ell_1$. We demonstrate the meritorious performance of our algorithms for TEM imaging using real data

The effect of environment and superalloy composition on TBC lifetime

While the water vapor content of the combustion gas in natural gas-fired land based turbines is ~10%, it can be 20-85% with coal-derived (syngas or H2) fuels or innovative turbine concepts for more efficient carbon capture. Additional concepts envisage working fluids with high CO2 contents to facilitate carbon capture and sequestration. Also, for land-based, power-generation turbines, there is industry interest in reducing alloy costs by decreasing the superalloy Re content, either by developing new alloys or employing earlier generation superalloys. To investigate the effects of these variables on thermal barrier coating (TBC) lifetime, furnace cycling tests (1h cycles) were performed in air with 10, 50 and 90 vol.% water vapor, O2-50%H2O and CO2-10%H2O and compared to prior results in dry air or O2. Two types of TBC’s were investigated: (1) diffusion bond coatings (Pt diffusion or simple or Pt-modified aluminide) with commercially vapor-deposited yttria-stabilized zirconia (YSZ) top coatings on second-generation superalloy N5 and N515 (1.5%Re) substrates and (2) high velocity oxygen fuel (HVOF) sprayed MCrAlYHfSi bond coatings with air-plasma sprayed YSZ top coatings on superalloy X4, 1483 and 247 substrates. For both types of coatings, a 20-50% decrease in coating lifetime was observed with the addition of water vapor for all but the Pt diffusion coatings which were unaffected by the environment. However, the higher water vapor contents in air did not further decrease the coating lifetime. Initial results for similar diffusion bond coatings in CO2-10%H2O also did not show a decrease in lifetime due to the addition of CO2. Characterization of the failed coating microstructures showed only minor effects of water vapor and CO2 additions that do not appear to account for the changes in lifetimes observed. Reductions in TBC lifetime were observed for 1483 substrates (compared to X4), which were attributed to the lower Al content and possible the higher Ti content. The higher Hf content in N515 (compared to N5) likely explains the higher TBC lifetimes observed for this substrate. More recent work with 247 substrates is in progress as well as furnace testing with 100h cycles to better simulate the base load duty cycle. Future work also is planned to investigate the role of SO2 on TBC lifetime as increased water vapor contents in the exhaust do not explain the current 50°-100°C de-rating of syngas-fired turbines

Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extract information from atomically resolved images including location of the atomic species and type of defects. We develop a 'weakly-supervised' approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular 'rotor'. This deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data

Multilayer Lateral Heterostructures of Van Der Waals Crystals with Sharp, Carrier–Transparent Interfaces

Research on engineered materials that integrate different 2D crystals has largely focused on two prototypical heterostructures: Vertical van der Waals stacks and lateral heterostructures of covalently stitched monolayers. Extending lateral integration to few layer or even multilayer van der Waals crystals could enable architectures that combine the superior light absorption and photonic properties of thicker crystals with close proximity to interfaces and efficient carrier separation within the layers, potentially benefiting applications such as photovoltaics. Here, the realization of multilayer heterstructures of the van der Waals semiconductors SnS and GeS with lateral interfaces spanning up to several hundred individual layers is demonstrated. Structural and chemical imaging identifies {110} interfaces that are perpendicular to the (001) layer plane and are laterally localized and sharp on a 10 nm scale across the entire thickness. Cathodoluminescence spectroscopy provides evidence for a facile transfer of electron-hole pairs across the lateral interfaces, indicating covalent stitching with high electronic quality and a low density of recombination centers

A Microstructural and Kinetic Investigation of the KCl-Induced Corrosion of an FeCrAl Alloy at 600 A degrees C

The corrosion behaviour of a FeCrAl alloy was investigated at 600 A degrees C in O-2 + H2O with solid KCl applied. A kinetics and microstructural investigation showed that KCl accelerates corrosion and that potassium chromate formation depletes the protective scale in Cr, thus triggering the formation of a fast-growing iron-rich scale. Iron oxide was found to grow both inward and outward, on either side of the initial oxide. A chromia layer is formed with time underneath the iron oxide. It was found that although the alloy does not form a continuous pure alumina scale at the investigated temperature, aluminium is, however, always enriched at the oxide/alloy interface

Real-time insight into the multistage mechanism of nanoparticle exsolution from a perovskite host surface

In exsolution, nanoparticles form by emerging from oxide hosts by application of redox driving forces, leading to transformative advances in stability, activity, and efficiency over deposition techniques, and resulting in a wide range of new opportunities for catalytic, energy and net-zero-related technologies. However, the mechanism of exsolved nanoparticle nucleation and perovskite structural evolution, has, to date, remained unclear. Herein, we shed light on this elusive process by following in real time Ir nanoparticle emergence from a SrTiO3 host oxide lattice, using in situ high-resolution electron microscopy in combination with computational simulations and machine learning analytics. We show that nucleation occurs via atom clustering, in tandem with host evolution, revealing the participation of surface defects and host lattice restructuring in trapping Ir atoms to initiate nanoparticle formation and growth. These insights provide a theoretical platform and practical recommendations to further the development of highly functional and broadly applicable exsolvable materials