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  • Upwelling  (18)
  • Environmental sensing
  • Lipids
  • Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu  (19)
  • American Chemical Society  (2)
  • 1
    Publication Date: 2022-10-21
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Colson, B. C., & Michel, A. P. M. Flow-through quantification of microplastics using impedance spectroscopy. ACS Sensors, 6(1), (2021): 238–244, doi:10.1021/acssensors.0c02223.
    Description: Understanding the sources, impacts, and fate of microplastics in the environment is critical for assessing the potential risks of these anthropogenic particles. However, our ability to quantify and identify microplastics in aquatic ecosystems is limited by the lack of rapid techniques that do not require visual sorting or preprocessing. Here, we demonstrate the use of impedance spectroscopy for high-throughput flow-through microplastic quantification, with the goal of rapid measurement of microplastic concentration and size. Impedance spectroscopy characterizes the electrical properties of individual particles directly in the flow of water, allowing for simultaneous sizing and material identification. To demonstrate the technique, spike and recovery experiments were conducted in tap water with 212–1000 μm polyethylene beads in six size ranges and a variety of similarly sized biological materials. Microplastics were reliably detected, sized, and differentiated from biological materials via their electrical properties at an average flow rate of 103 ± 8 mL/min. The recovery rate was ≥90% for microplastics in the 300–1000 μm size range, and the false positive rate for the misidentification of the biological material as plastic was 1%. Impedance spectroscopy allowed for the identification of microplastics directly in water without visual sorting or filtration, demonstrating its use for flow-through sensing.
    Description: The authors thank the Richard Saltonstall Charitable Foundation and the National Academies Keck Futures Initiative (NAKFI DBS13) for their funding support.
    Keywords: Microplastics ; Plastics ; Impedance spectroscopy ; Dielectric properties ; Instrumentation ; Particle detection ; Flow-through ; Environmental sensing
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: Water Chemistry
    Description: This dataset contains information about discrete seawater samples collected from 2012-2016 on Jarvis Island for salinity, nutrients, total alkalinity (TA), and dissolved inorganic carbon (DIC) during each sampling period. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/775834
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1737311
    Keywords: Coral bleaching ; Coral mortality ; Jarvis Island ; Central equatorial Pacific ; El Nino ; Sclerochronology ; Upwelling
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 3
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: Dissolved Oxygen Times Series
    Description: This dataset contains dissolved oxygen measurements collected at the height of El Niño on 12-15 November 2015 and after severe El Niño conditions had subsided on 16-24 May 2016 for the west and east sides of Jarvis Island at 7-10 m depth. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/775836
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1737311
    Keywords: Coral bleaching ; Coral mortality ; Jarvis Island ; Central equatorial Pacific ; El Nino ; Sclerochronology ; Upwelling
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 4
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: Stress bands Jarvis
    Description: The dataset contains historical coral reef bleaching events on Jarvis Island reconstructed from stress bands in Porites coral cores. Samples are collected between 2008 and 2016. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/775828
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1737311
    Keywords: Coral bleaching ; Coral mortality ; Jarvis Island ; Central equatorial Pacific ; El Nino ; Sclerochronology ; Upwelling
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 5
    Publication Date: 2022-10-31
    Description: Dataset: Kelp forest pumping data - pH
    Description: pH measured in situ over depth in the kelp forest (36° 37.3’ N, 121° 54.1’ W) recorded in July 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/826162
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1737096, NSF Division of Ocean Sciences (NSF OCE) OCE-1737176
    Keywords: Ocean acidification ; Kelp forest ; Biogeochemistry ; Spatiotemporal variability ; Upwelling ; In situ pumping ; PH
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 6
    Publication Date: 2022-10-31
    Description: Dataset: Nighttime Surface Chlorophyll a
    Description: Nighttime surface chlorophyll-a concentrations at the MBARI OA1 Buoy (36° 37.373’ N, 121 ° 54.000’ W) from June to October 2018 For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/822494
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1737096, NSF Division of Ocean Sciences (NSF OCE) OCE-1737176
    Keywords: Ocean acidification ; Kelp forest ; Biogeochemistry ; Spatiotemporal variability ; Upwelling ; Chlorophyll a
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 7
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: pH Time Series
    Description: This dataset contains pH measurements collected at the height of El Niño on 12-15 November 2015 and after severe El Niño conditions had subsided on 16-24 May 2016 for the west and east sides of Jarvis Island at 7-10 m depth. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/775838
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1737311
    Keywords: Coral bleaching ; Coral mortality ; Jarvis Island ; Central equatorial Pacific ; El Nino ; Sclerochronology ; Upwelling
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 8
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: Carbon Flux Explorer CCE_LTER P1706 - SOLO CTD Profiles
    Description: CTD profile data from Carbon Flux Explorers deployed 100-500m in the California Current Regime, during the CCE-LTER process study (P1706) between June 2 and July 1, 2017 For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/825602
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1538686
    Keywords: Twilight Zone ; California Current Ecosystem ; Upwelling ; Carbon FLux Explorer ; CTD profiles
    Repository Name: Woods Hole Open Access Server
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  • 9
    Publication Date: 2022-10-31
    Description: Dataset: ADCP - Inside Kelp Forest
    Description: Cross-shore and alongshore velocity inside the kelp forest at Hopkins Marine Station (36° 37.297’ N, 121° 54.102’ W) recorded between June and October, 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/826431
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1737096, NSF Division of Ocean Sciences (NSF OCE) OCE-1737176
    Keywords: Ocean acidification ; Kelp forest/outside kelp forest ; Biogeochemistry ; Spatiotemporal variability ; Upwelling ; Current velocity
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 10
    Publication Date: 2022-10-31
    Description: Dataset: Kelp Forest Estimated Carbonate Parameters
    Description: Kelp forest mooring DIC, TA, pCO2, and aragonite saturation state estimations inside the kelp canopy (36° 37.297’ N, 121° 54.102’ W.) at Hopkins Marine Station, recorded between June and October 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/823008
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1737096, NSF Division of Ocean Sciences (NSF OCE) OCE-1737176
    Keywords: Ocean acidification ; Kelp forest ; Biogeochemistry ; Spatiotemporal variability ; Upwelling ; DIC ; Alkalinity ; Omega (aragonite) ; PCO2
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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