Publications

Abstract not Available.

Significance Halide perovskites, especially layered perovskites, offer a number of advantages to creating bright and efficient light-emitting devices and other optoelectronic applications. The organic–inorganic hybrid layered perovskite features complex lattice dynamics due to the ionic character of the crystal and the softness arising from noncovalent bonds between molecular moieties and the inorganic network. Especially, the packing geometry of the organic barriers leads to a structural deformation of inorganic octahedral, which strongly affects the properties that are crucial for device applications. In this work, we use high-resolution resonant impulsive stimulated Raman spectroscopy of a variety of ligand substitutions in layered perovskites. We find the composition of organic ligands can substantially change the dephasing rate of optical phonons and their temperature dependence, due to varying degrees of anharmonicity in the lattice and dynamic structural disorder.

Medical devices, particularly endovascular stents, manufactured from superelastic Nitinol, a near-equiatomic alloy of Ni and Ti, are subjected to complex mixed-mode loading conditions in vivo, including axial tension and compression, radial compression, pulsatile, bending and torsion. Fatigue lifetime prediction methodologies for Nitinol, however, are invariably based on uniaxial loading and thus fall short of accurately predicting the safe lifetime of stents under the complex multiaxial loading conditions experienced physiologically. While there is a considerable body of research documented on the cyclic fatigue of Nitinol in uniaxial tension or bending, there remains an almost total lack of comprehensive fatigue lifetime data for other loading conditions, such as torsion and tension/torsion. In this work, thin-walled Nitinol tubes were cycled in torsion at various mean and alternating strains to investigate the fatigue life behavior of Nitinol and results compared to equivalent fatigue data collected under uniaxial tensile/bending loads. Using these strain-life results for various loading modes and an equivalent referential (Lagrangian) strain approach, a strategy for normalizing these data is presented. Based on this strategy, a fatigue lifetime prediction model for the multiaxial loading of Nitinol is presented utilizing a modified Coffin–Manson approach where the number of cycles to failure is related to the equivalent alternating transformation strain.

Significance The electroconversion of CO 2 to value-added products is a promising path to sustainable fuels and chemicals. However, the microenvironment that is created during CO 2 electroreduction near the surface of heterogeneous Cu electrocatalysts remains unknown. Its understanding can lead to the development of ways to improve activity and selectivity toward multicarbon products. This work introduces a method called on-stream substitution of reactant isotope that provides quantitative information of the CO intermediate species present on Cu surfaces during electrolysis. An intermediary CO reservoir that contains more CO molecules than typically expected in a surface adsorbed configuration was identified. Its size was shown to be a factor closely associated with the formation of multicarbon products.

Interesting transport phenomena arise when fluids are confined to nanoscale dimensions in the range of 1–100 nm. We examine three distinct effects that influence ionic and molecular transport as the size of fluidic channels is decreased to the nanoscale. First, the length scale of electrostatic interactions in aqueous solutions becomes comparable to nanochannel size and the number of surface charges becomes comparable to the number of ions in the channel. Second, the size of the channel becomes comparable to the size of biomolecules such as proteins and DNA. Third, large surface area-to-volume ratios result in rapid rates of surface reactions and can dramatically affect transport of molecules through the channel. These phenomena enable us to control transport of ions and molecules in unique ways that are not possible in larger channels. Electrostatic interactions enable local control of ionic concentrations and transport inside nanochannels through field effect in a nanofluidic transistor, which is analogous to the metal-oxide-semiconductor field effect transistor. Furthermore, by controlling surface charge in nanochannels, it is possible to create a nanofluidic diode that rectifies ionic transport through the channel. Biological binding events result in partial blockage of the channel, and can thus be sensed by a decrease in nanochannel conductance. At low ionic concentrations, the effect of biomolecular charge is dominant and it can lead to an increase in conductance. Surface reactions can also be used to control transport of molecules though the channel due to the large surface area-to-volume ratios. Rapid surface reactions enable a new technique of diffusion-limited patterning (DLP), which is useful for patterning of biomolecules and surface charge in nanochannels. These examples illustrate how electrostatic interactions, biomolecular size, and surface reactions can be used for controlling ionic and molecular transport through nanochannels. These phenomena may be useful for operations such as analyte focusing, pH and ionic concentration control, and biosensing in micro- and nanofluidic devices.

Patat et al. recently inferred the existence of circumstellar material around a normal Type Ia supernova (SN Ia) for the first time, finding time-variable Na I D absorption lines in the spectrum of SN 2006X. We present high-resolution spectroscopy of the bright SN Ia 2007af at three epochs and search for variability in any of the Na D absorption components. Over the time range from 4 days before to 24 days after maximum light, we find that the host-galaxy Na D lines appear to be of interstellar rather than circumstellar origin and do not vary down to the level of 18 m angstrom (column density of 2 x 10(11) cm(-2)). We limit any circumstellar absorption lines to be weaker than similar to 10 m angstrom (6 x 10(10) cm(-2)). For the case of material distributed in spherically symmetric shells of radius similar to 10(16) cm surrounding the progenitor system, we place an upper limit on the shell mass of similar to(3 x 10(-8))/X M circle dot, where X is the Na ionization fraction. We also show that SN 2007af is a photometrically and spectroscopically normal SN Ia. Assuming that the variable Na D lines in SN 2006X came from circumstellar matter, we therefore conclude that either there is a preferred geometry for the detection of variable absorption components in SNe Ia, or SN 2007af and SN 2006X had different types of progenitor systems.

Abstract Pyrrolidine and piperidine adducts are formed with high regio‐and enantioselectivities only in the presence of menthol.

In recent years, the pace of innovations in the fields of machine learning (ML) has accelerated, researchers in SysML have created algorithms and systems that parallelize ML training over multiple devices or computational nodes. As ML models become more structurally complex, many systems have struggled to provide all-round performance on a variety of models. Particularly, ML scale-up is usually underestimated in terms of the amount of knowledge and time required to map from an appropriate distribution strategy to the model. Applying parallel training systems to complex models adds nontrivial development overheads in addition to model prototyping, and often results in lower-than-expected performance. This tutorial identifies research and practical pain points in parallel ML training, and discusses latest development of algorithms and systems on addressing these challenges in both usability and performance. In particular, this tutorial presents a new perspective of unifying seemingly different distributed ML training strategies. Based on it, introduces new techniques and system architectures to simplify and automate ML parallelization. This tutorial is built upon the authors' years' of research and industry experience, comprehensive literature survey, and several latest tutorials and papers published by the authors and peer researchers.

Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is challenging due to inherent nOnlinearities in cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine calibration method. In Phase I (coarse), we place a red calibration marker on the end effector and let it randomly move through a set of open-loop trajectories to obtain a large sample set of camera pixels and internal robot end-effector configurations. This coarse data is then used to train a Deep Neural Network (DNN) to learn the coarse transformation bias. In Phase II (fine), the bias from Phase I is applied to move the end -effector toward a small set of specific target points on a printed sheet. For each target, a human operator manually adjusts the end -effector position by direct contact (not through teleoperation) and the residual compensation bias is recorded. This fine data is then used to train a Random Forest (RF) to learn the fine transformation bias. Subsequent experiments suggest that without calibration, position errors average 4.55mm. Phase I can reduce average error to 2.14mm and the combination of Phase I and Phase II can reduces average error to 1.08mm. We apply these results to debridement of raisins and pumpkin seeds as fragment phantoms. Using an endoscopic stereo camera with standard edge detection, experiments with 120 trials achieved average success rates of 94.5 %, exceeding prior results with much larger fragments (89.4%) and achieving a speedup of 2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source code, data, and videos are available at https://sites.google.com/view/calib-icra/.