Spatial-Temporal Imaging of Anisotropic Photocarrier Dynamics in Black Phosphorus

As an emerging single elemental layered material with a low symmetry in-plane crystal lattice, black phosphorus (BP) has attracted significant research interest owing to its unique electronic and optoelectronic properties, including its widely tunable bandgap, polarization dependent photoresponse and highly anisotropic in-plane charge transport. Despite extensive study of the steady-state charge transport in BP, there has not been direct characterization and visualization of the hot carriers dynamics in BP immediately after photoexcitation, which is crucial to understanding the performance of BP-based optoelectronic devices. Here we use the newly developed scanning ultrafast electron microscopy (SUEM) to directly visualize the motion of photo-excited hot carriers on the surface of BP in both space and time. We observe highly anisotropic in-plane diffusion of hot holes, with a 15-times higher diffusivity along the armchair (x-) direction than that along the zigzag (y-) direction. Our results provide direct evidence of anisotropic hot carrier transport in BP and demonstrate the capability of SUEM to resolve ultrafast hot carrier dynamics in layered two-dimensional materials.Comment: 21 pages, 6 figure

Strong dimerization of wild-type ErbB2/Neu transmembrane domain and the oncogenic Val664Glu mutant in mammalian plasma membranes

AbstractHere, we study the homodimerization of the transmembrane domain of Neu, as well as an oncogenic mutant (V664E), in vesicles derived from the plasma membrane of mammalian cells. For the characterization, we use a Förster resonance energy transfer (FRET)-based method termed Quantitative Imaging-FRET (QI-FRET), which yields the donor and acceptor concentrations in addition to the FRET efficiencies in individual plasma membrane-derived vesicles. Our results demonstrate that both the wild-type and the mutant are 100% dimeric, suggesting that the Neu TM helix dimerizes more efficiently than other RTK TM domains in mammalian membranes. Furthermore, the data suggest that the V664E mutation causes a very small, but statistically significant change in dimer structure. This article is part of a Special Issue entitled: Interfacially Active Peptides and Proteins. Guest Editors: William C. Wimley and Kalina Hristova

(R)-1-Phenyl­ethanaminium (S)-4-chloro­mandelate

The absolute configuration of the title complex, C8H12N+·C8H6ClO3 − or [R-C6H5C(H)CH3NH3][S-4-ClC6H4C(H)(OH)CO2], has been confirmed by the structure determination. In the crystal structure, inter­molecular O—H⋯O and N—H⋯O hydrogen bonds form a two-dimensional network perpendicular to the c axis

Identification of phases, symmetries and defects through local crystallography

Advances in electron and probe microscopies allow 10 pm or higher precision in measurements of atomic positions. This level of fidelity is sufficient to correlate the length (and hence energy) of bonds, as well as bond angles to functional properties of materials. Traditionally, this relied on mapping locally measured parameters to macroscopic variables, for example, average unit cell. This description effectively ignores the information contained in the microscopic degrees of freedom available in a high-resolution image. Here we introduce an approach for local analysis of material structure based on statistical analysis of individual atomic neighbourhoods. Clustering and multivariate algorithms such as principal component analysis explore the connectivity of lattice and bond structure, as well as identify minute structural distortions, thus allowing for chemical description and identification of phases. This analysis lays the framework for building image genomes and structure–property libraries, based on conjoining structural and spectral realms through local atomic behaviour

A Reusable Framework for Fault Detection and Isolation in Small Satellites

Nearly half of all small satellites launched between 2000 and 2016 have experienced partial or complete failures. Detecting the anomalies and faults responsible for such failures and responding to them rapidly may help increase the success rates of future small satellite missions. However, developing and implementing platform-specific and comprehensive fault management solutions can be cost-prohibitive to most small satellite teams. To enable such teams to achieve this capability quickly and cost-efficiently, we have developed a reusable and fully data-driven framework and associated algorithms to detect and isolate anomalous behaviors. This work is supported by NASA and utilizes correlations between system operational variables and Machine Learning (ML) techniques to generate real-time estimates of expected nominal behavior. Large differences between expected and observed behavior captured through sensor measurements may indicate the presence of anomalies or faults. Since this approach monitors sensor streams for new behavior, a faults database is not required, and anomalies or faults not previously known are also expected to be detected. To automate this framework, a set of algorithms are developed that use a small amount of normal operational data from a satellite system (or a subsystem) to train the required models. The algorithms utilize physical dependencies between the system’s operational variables that are extracted using a Dynamic Time Warping (DTW) technique and ML regression models. The system’s battery metrics, current and voltage, are considered the roots of trust to diagnose other system operational variables selected based on the DTW correlation strengths. Battery metrics are selected as they can be independently and reliably measured. Fluctuations in a rolling window of battery current and voltage measurements are extracted into features such as window average, window maximum, window minimum, and several others. These features (predictors) and the individual sensors’ readings (targets) are then utilized for training the ML regression models. During a mission, the same battery features are extracted in real-time and fed to the trained ML models to estimate sensors’ measurements expected during nominal system behavior. Then, the cumulative error between predicted and observed measurements and its slope are calculated. An anomaly flag is raised when these two values cross dynamic thresholds computed based on their recent values and some preset weights. Due to the one-to-one nature of the independent mappings from battery metrics to each operational variable, the anomaly is also simultaneously isolated to the sensor itself or the subsystem where it is located. The framework also includes automated testing of the trained ML models and anomaly detection parameters selected by artificially injecting different types of anomalies. The injected anomalies relate to loose connections, abrupt sensor failure, sensor drift, data corruption, and others. In this work, the implementation of this framework on datasets generated from laboratory tests on a CubeSat platform is discussed. Results show nearly 90% average detection rate and less than 1% average false positives rates for many analog operational variables strongly correlated to battery metrics

Strongly coupled fluid-particle flows in vertical channels. II. Turbulence modeling

In Part I, simulations of strongly coupled fluid-particle flow in a vertical channel were performed with the purpose of understanding, in general, the fundamental physics of wall-bounded multiphase turbulence and, in particular, the roles of the spatially correlated and uncorrelated components of the particle velocity.The exact Reynolds-averaged (RA) equations for high-mass-loading suspensions were presented, and the unclosed terms that are retained in the context of fully developed channel flow were evaluated in an Eulerian–Lagrangian (EL) framework. Here, data from the EL simulations are used to validate a multiphase Reynolds-stress model (RSM) that predicts the wall-normal distribution of the two-phase, one-point turbulence statistics up to second order. It is shown that the anisotropy of the Reynolds stresses both near the wall and far away is a crucial component for predicting the distribution of the RA particle-phase volume fraction. Moreover, the decomposition of the phase-average (PA) particle-phase fluctuating energy into the spatially correlated and uncorrelated components is necessary to account for the boundary conditions at the wall. When these factors are properly accounted for in the RSM, the agreement with the EL turbulence statistics is satisfactory at first order (e.g., PA velocities) but less so at second order (e.g., PA turbulent kinetic energy). Finally, an algebraic stress model for the PA particle-phase pressure tensor and the Reynolds stresses is derived from the RSM using the weak-equilibrium assumption

Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 +/- 0.13) and the fixed effects model (0.62 +/- 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 +/- 0.11), GBLUP (0.55 +/- 0.1), and ABLUP (0.48 +/- 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical