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RamaCam - In situ holographic imaging and chemical spectroscopy for long term scalable analysis of marine particles in deep-sea environments

RamaCam - In situ holographic imaging and chemical spectroscopy for long term scalable analysis of marine particles in deep-sea environments
RamaCam - 原位全息成像和化学光谱,用于深海环境中海洋颗粒的长期可扩展分析
批准号:
NE/R01227X/1
负责人:
Blair Thornton
金额:
$10.26万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

项目摘要

项目成果

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中文摘要
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英文摘要
While modern day ocean sensors are capable of measuring the concentration of chemicals dissolved in seawater to such high sensitivities that we rarely need to sample them, many chemicals form tiny particles in seawater, often with diameters smaller than the width of a human hair, and these act as a blind spot for most of today's sensors. The only way to study these particles in detail, is to recover samples and analyse them in a laboratory. Marine particles include plankton, dead skin shed from whales and fish, faecal pellets as well as micro-plastics and other types of human litter. If you took a bottle of seawater from the surface of the ocean and compared it to seawater from the deep-sea, the number of large particles would be much higher in the surface water, because light from the sun provides energy that can be used by plankton, which form a large proportion of the particles where sunlight can reach within 200 m of the ocean surface. At the same time, we also know that most particles sink, and so it is important for us to understand why there are so few particles in the deep-sea, how much material is sinking to the seafloor, what it is made out of, how fast it sinks, and what proportion of it makes it back up to the sea surface or gets washed on-shore. The reason this is important, is that particles that sink to the seafloor are thought to play an important role in removing carbon from our atmosphere. At the same time, scientists are worried that litter and plastics may accumulate on the seafloor and damage the fragile seafloor ecosystems that exist at an average depth of more than 3800 m below the ocean's surface. The aim of this project, is to demonstrate new ways in which we can improve our ability to study the distribution of different types of particles in the deep-sea. The sensor that will be developed will analyse large volumes of seawater, almost 2/3 of a drinks can a second, in order to gather data in the deep-sea where the relative number of particles is small. The sensor will count the number of particles that pass through it, study their appearance and also perform laser based chemical analysis to identify what these particles are made out of. An important aspect of this work is to achieve this in a compact, low power way. The last point is important to allow large numbers of this new type of sensor to be used to study vast regions of the ocean for several years at a time. This innovative work will be carried out by researchers based in the UK and in Japan, both island nations with a long history of marine research, who will combine their expertise to overcome the difficult challenges that are involved in achieving our goal. By helping researchers in the future achieve a better understanding how particles in the ocean behave, and this can in turn help our governments decide what kinds of policies need to be put in place to preserve our ocean and our atmosphere.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
Methods for surveying deep-sea organisms
调查深海生物的方法
DOI: --
发表时间: 2018
期刊: Journal of the Japan Society of Mechanical Engineers
影响因子: --
作者: [Lindsay D]
通讯作者: Lindsay D
Comparison of region proposal methods for marine holograms
海洋全息图区域提议方法比较
DOI: --
发表时间: 2020
期刊:
影响因子: --
作者: [Liu Z]
通讯作者: Liu Z
RamaCam: autonomous in-situ monitoring system of marine particles by combining holography and Raman spectroscopy
RamaCam:结合全息术和拉曼光谱的海洋颗粒自主原位监测系统
DOI: 10.1109/ut49729.2023.10103388
发表时间: 2023
期刊:
影响因子: --
作者: [Takahashi T]
通讯作者: Takahashi T
Technology for surveying gelatinous zooplankton: 3D imaging and machine learning
胶状浮游动物测量技术:3D 成像和机器学习
DOI: --
发表时间: 2019
期刊: Aquabiology
影响因子: --
作者: [Lindsay D]
通讯作者: Lindsay D
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