Chiral photocurrent in a Quasi-1D TiS3 (001) phototransistor

The presence of in-plane chiral effects, hence spin–orbit coupling, is evident in the changes in the photocurrent produced in a TiS3(001) field-effect phototransistor with left versus right circularly polarized light. The direction of the photocurrent is protected by the presence of strong spin–orbit coupling and the anisotropy of the band structure as indicated in NanoARPES measurements. Dark electronic transport measurements indicate that TiS3 is n-type and has an electron mobility in the range of 1–6 cm2V−1s−1. I–V measurements under laser illumination indicate the photocurrent exhibits a bias directionality dependence, reminiscent of bipolar spin diode behavior. Because the TiS3 contains no heavy elements, the presence of spin–orbit coupling must be attributed to the observed loss of inversion symmetry at the TiS3(001) surface

Structural disorder and magnetism in the spin-gapless semiconductor CoFeCrAl

Disordered CoFeCrAl and CoFeCrSi0.5Al0.5 alloys have been investigated experimentally and by first-principle calculations. The melt-spun and annealed samples all exhibit Heusler-type superlattice peaks, but the peak intensities indicate a substantial degree of B2-type chemical disorder. Si substitution reduces the degree of this disorder. Our theoretical analysis also considers several types of antisite disorder (Fe-Co, Fe-Cr, Co-Cr) in Y-ordered CoFeCrAl and partial substitution of Si for Al. The substitution transforms the spin-gapless semiconductor CoFeCrAl into a halfmetallic ferrimagnet and increases the half-metallic band gap by 0.12 eV. Compared CoFeCrAl, the moment of CoFeCrSi0.5Al0.5 is predicted to increase from 2.01 μB to 2.50 μB per formula unit, in good agreement with experiment

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Precursor-surface interactions revealed during plasma-enhanced atomic layer deposition of metal oxide thin films by in-situ spectroscopic ellipsometry

We find that a five-phase (substrate, mixed native oxide and roughness interface layer, metal oxide thin film layer, surface ligand layer, ambient) model with two-dynamic (metal oxide thin film layer thickness and surface ligand layer void fraction) parameters (dynamic dual box model) is sufficient to explain in-situ spectroscopic ellipsometry data measured within and across multiple cycles during plasma-enhanced atomic layer deposition of metal oxide thin films. We demonstrate our dynamic dual box model for analysis of in-situ spectroscopic ellipsometry data in the photon energy range of 0.7–3.4 eV measured with time resolution of few seconds over large numbers of cycles during the growth of titanium oxide (TiO2) and tungsten oxide (WO3) thin films, as examples. We observe cyclic surface roughening with fast kinetics and subsequent roughness reduction with slow kinetics, upon cyclic exposure to precursor materials, leading to oscillations of the metal thin film thickness with small but positive growth per cycle. We explain the cyclic surface roughening by precursor-surface interactions leading to defect creation, and subsequent surface restructuring. Atomic force microscopic images before and after growth, x-ray photoelectron spectroscopy, and x-ray diffraction investigations confirm structural and chemical properties of our thin films. Our proposed dynamic dual box model may be generally applicable to monitor and control metal oxide growth in atomic layer deposition, and we include data for Sio2 and Al2o3 as further examples

Surface termination and Schottky-barrier formation of In\u3csub\u3e4\u3c/sub\u3eSe\u3csub\u3e3\u3c/sub\u3e(001)

The surface termination of In4Se3(001) and the interface of this layered trichalcogenide, with Au, was examined using x-ray photoemission spectroscopy. Low energy electron diffraction indicates that the surface is highly crystalline, but suggests an absence of C2v mirror plane symmetry. The surface termination of the In4Se3(001 is found, by angle-resolved x-ray photoemission spectroscopy, to be In, which is consistent with the observed Schottky barrier formation found with this n-type semiconductor. Transistor measurements confirm earlier results from photoemission, suggesting that In4Se3(001 is an n-type semiconductor, so that Schottky barrier formation with a large work function metal, such as Au, is expected. The measured low carrier mobilities could be the result of the contacts and would be consistent with Schottky barrier formation

Corrigendum: Surface termination and Schottky-barrier formation of In\u3csub\u3e4\u3c/sub\u3eSe\u3csub\u3e3\u3c/sub\u3e(001) [\u3ci\u3eSemiconductor Science and Technology\u3c/i\u3e (2020) 35 (065009) DOI: 10.1088/1361-6641/ab7e45]

Through the description of various surface terminations, the chain direction of In4Se3 in this paper [1] is implied to be in the plane of its surface. Even though the common convention for photoemission spectroscopy is to place z-axis along the surface normal, the axis perpendicular to the growth direction for this indium selenide is the crystallographic a-axis (and not the c-axis) [2–4]. Therefore, in our work the surface of In4Se3 should have been labeled (100), and not (001), to prevent any confusion that may have resulted from a less than conventional index notation. Data availability statement The data that support the findings of this study are available upon reasonable request from the authors