The Apache Point Observatory Galactic Evolution Experiment (APOGEE)

The Apache Point Observatory Galactic Evolution Experiment (APOGEE), one of the programs in the Sloan Digital Sky Survey III (SDSS-III), has now completed its systematic, homogeneous spectroscopic survey sampling all major populations of the Milky Way. After a three-year observing campaign on the Sloan 2.5 m Telescope, APOGEE has collected a half million high-resolution (R ~ 22,500), high signal-to-noise ratio (>100), infrared (1.51–1.70 μm) spectra for 146,000 stars, with time series information via repeat visits to most of these stars. This paper describes the motivations for the survey and its overall design—hardware, field placement, target selection, operations—and gives an overview of these aspects as well as the data reduction, analysis, and products. An index is also given to the complement of technical papers that describe various critical survey components in detail. Finally, we discuss the achieved survey performance and illustrate the variety of potential uses of the data products by way of a number of science demonstrations, which span from time series analysis of stellar spectral variations and radial velocity variations from stellar companions, to spatial maps of kinematics, metallicity, and abundance patterns across the Galaxy and as a function of age, to new views of the interstellar medium, the chemistry of star clusters, and the discovery of rare stellar species. As part of SDSS-III Data Release 12 and later releases, all of the APOGEE data products are publicly available

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Lamellar ionenes with highly dissociative, anionic channels provide low barriers for cation transport

Solid polymer electrolytes have the potential to enable safer and more energy dense batteries; however, a deeper understanding of their ion conduction mechanisms, and how they can be optimized by rational molecular design, is needed to realize this goal. Here, we investigate the impact of anion dissociation energy on ion conduction in solid polymer electrolytes via a novel class of ionenes prepared using acyclic diene metathesis polymerization of highly dissociative, liquid crystalline fluorinated aryl sulfonimide-tagged ("FAST”) anion monomers. These polyanions with various cations (Li+, Na+, K+, and Cs+) form well-ordered lamellae that are thermally stable up to 180 °C and feature domain spacings that correlate with cation size, providing channels lined with dissociative FAST anions. Electrochemical impedance spectroscopy (EIS) and differential scanning calorimetry (DSC) experiments, along with nudged elastic band (NEB) calculations, suggest that cation motion in these materials operates via an ion hopping mechanism. Moreover, the activation energy for Li+ conduction is 59 kJ/mol, which is amongst the lowest for systems that are proposed to operate via an ion conduction mechanism that is decoupled from polymer segmental motion. Furthermore, the addition of a 1 equivalent of a cation-coordinating solvent to these materials led to a >1000-fold increase in ionic conductivity without detectable disruption of the lamellar structure, suggesting selective solvation of the lamellar ion channels. This work demonstrates a novel molecular design strategy to facilitate controlled formation of dissociative anionic channels, which translates to significant enhancements in ion conduction in solid polymer electrolytes

Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach

Ecosystemmonitoring is central to effectivemanagement, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection formonitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reefmonitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery inmonitoring with automated image annotation can dramatically improve how wemeasure andmonitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and acrossmanagement areas.</p