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  1. Reduced-nitrogen compounds (RNC), such as ammonia and amines, play important roles in atmospheric aerosol nucleation, secondary organic aerosol (SOA), and cloud formation processes. Fast measurements of ammonia and amines are made with a chemical ionization mass spectrometer (CIMS). Clusters containing RNC are measured with an atmospheric pressure interface time of flight mass spectrometer (APi-TOF) or chemical ionization APi-TOF (CI-APi-TOF). Aerosol-phase amines can be detected with a single particle mass spectrometer at real-time, or with offline chemical analytical methods using filter samples. However, the application of these instruments in real atmospheric measurements is still very limited. This perspective article highlights recentmore »measurements of RNC in the atmosphere and discusses their implications in new particle formation (NPF).« less
    Free, publicly-accessible full text available April 6, 2023
  2. Free, publicly-accessible full text available May 1, 2023
  3. Free, publicly-accessible full text available April 1, 2023
  4. Abstract
    Data related to collaborative study of fluid rock interaction in the Nicobar Fan sampled at Sites 1480 and 1481 during IODP Expedition 362 off Sumatra
  5. Free, publicly-accessible full text available January 1, 2023
  6. ABSTRACT We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r = 23.6 versus r = 22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314 000 galaxies. 140 000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars,more »and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314 000 galaxies. We use active learning to focus our volunteer effort on the galaxies which, if labelled, would be most informative for training our ensemble. When measured against confident volunteer classifications, the trained networks are approximately 99 per cent accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.« less
    Free, publicly-accessible full text available December 3, 2022