164 research outputs found
A fundamental investigation of process efficiencies in the Laser Engineered Net Shaping (LENS) solid freeform fabrication process
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 . We demonstrate the meritorious performance
of our algorithms for TEM imaging using real 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
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
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
Mechanisms of Creep Deformation in Polycrystalline Ni-Base Disk Superalloys
Abstract This paper reviews the presently proposed mechanisms for creep of ␥ strengthened Ni-base superalloys that are typically used for disk applications. Distinct creep strength controlling modes, such as dislocation-coupled antiphase-boundary shearing, shearing configurations involving superlattice stacking faults, Orowan looping, climb by-pass, and microtwinning have been observed. These are strongly influenced by the scale of the ␥ precipitating phase and are operative within specific ranges of temperature and stress. Insight from more recent experimental findings concerning microtwinning and extending stacking fault mechanisms suggest important similarities between these deformation modes. It is suggested that local atomic reordering in the wake of Shockley partials is responsible for the temperature dependence exhibited in this regime
In situ electrochemical scanning/transmission electron microscopy of electrode-electrolyte interfaces
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