Iodine-Catalysed Dissolution of Elemental Gold in Ethanol

Gold is a scarce element in the Earth's crust but indispensable in modern electronic devices. New, sustainable methods of gold recycling are essential to meet the growing eco-social demand of gold. Here, we describe a simple, inexpensive, and environmentally benign dissolution of gold under mild conditions. Gold dissolves quantitatively in ethanol using 2-mercaptoben-zimidazole as a ligand in the presence of a catalytic amount of iodine. Mechanistically, the dissolution of gold begins when I-2 oxidizes Au-0 and forms a [(AuI2)-I-1](-) species, which undergoes subsequent ligand-exchange reactions and forms a stable bis-ligand Au-1 complex. H2O2 oxidizes free iodide and regenerated I-2 returns back to the catalytic cycle. Addition of a reductant to the reaction mixture precipitates gold quantitatively and partially regenerates the ligand. We anticipate our work will open a new pathway to more sustainable metal recycling with the utilization of just catalytic amounts of reagents and green solvents.Peer reviewe

The Full Event Interpretation -- An exclusive tagging algorithm for the Belle II experiment

The Full Event Interpretation is presented: a new exclusive tagging algorithm used by the high-energy physics experiment Belle II. The experimental setup of Belle II allows the precise measurement of otherwise inaccessible $B$ meson decay-modes. The Full Event Interpretation algorithm enables many of these measurements. The algorithm relies on machine learning to automatically identify plausible $B$ meson decay chains based on the data recorded by the detector. Compared to similar algorithms employed by previous experiments, the Full Event Interpretation provides a greater efficiency, yielding a larger effective sample size usable in the measurement.Comment: 11 pages, 7 figures, 1 tabl

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities

Monte Carlo matching in the Belle II software

The Belle II experiment is an upgrade to the Belle experiment, and is located at the SuperKEKB facility in KEK, Tsukuba, Japan. The Belle II software is completely new and is used for everything from triggering data, generation of Monte Carlo events, tracking, clustering, to high-level analysis. One important feature is the matching between the combinations of reconstructed objects which form particle candidates and the underlying simulated particles from the event generators. This is used to study detector effects, analysis backgrounds, and efficiencies. This document describes the algorithm that is used by Belle II

Monte Carlo matching in the Belle II software

The Belle II experiment is an upgrade to the Belle experiment, and is located at the SuperKEKB facility in KEK, Tsukuba, Japan. The Belle II software is completely new and is used for everything from triggering data, generation of Monte Carlo simulation, tracking, clustering, and for high-level analysis. One important feature is the matching between the combinations of reconstructed objects which form particle candidates and the underlying simulated particles from the event generators. This is used to study detector effects, analysis backgrounds, and efficiencies. This document describes the algorithm that is used by Belle II