Collaborative Research: Developing Automated Nutrient and Trace Metal Methodology using Programmable Flow Injection
合作研究:使用可编程流动注射开发自动化营养物和微量金属方法
基本信息
- 批准号:1924690
- 负责人:
- 金额:$ 52.38万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-01 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Progress and discovery in the understanding of the chemical processes that regulate the growth of phytoplankton in the marine environment is limited by the ability of oceanographers to measure nutrients and trace metals at relevant spatial and temporal scales. This project aims to develop a new generation of autonomous and highly sensitive nutrient and trace metal analyzers for shipboard use and deployment at coastal monitoring stations, with potential for incorporation into existing autonomous observing platforms (e.g., gliders, autonomous underwater vehicles). Such a development will generate new insights into nutrient dynamics at a range of spatial and temporal scales and provide a new capability to obtain high resolution data sets for nutrients and trace metals in remote areas and environments where sample volumes are limited (e.g., brines, pore waters). This project will develop novel automated methods for nutrients (phosphate and silicate) and trace metals (aluminium, manganese) using a technology called programmable Flow Injection (pFI). pFI is a microfluidic technique, which allows to automate conventional wet chemical analysis using microliter volumes of sample and reagents while significantly reducing the generation waste. In order to bring low levels of trace metals and nutrients that characterize remote ocean regions into the analytical window of pFI, concentration techniques will also be developed and coupled to spectrophotometric and fluorescence detection. The developed methodologies will be intercalibrated with standard oceanographic methods by participating in a GO-SHIP research cruise and a Hawaii Ocean Time-series cruise of opportunity. The long-term, unattended operation of the pFI analyzers will be evaluated at the Central California Ocean Observing System (CenCOOS) Moss Landing Shore station, where autonomous pFI analyzers will be deployed to undertake a year-long, hourly time series of phosphate and silicate. The data obtained from the shore station will be made publicly available and will complement existing monitoring data from this location, illuminating the connection between deep ocean nutrient dynamics and biological activity in coastal upwelling systems. The newly developed pFI analyzers will be assembled using commercially available components and open source software to facilitate uptake of this new methodology by the chemical oceanography community at large. This project will also support the training and exchange of at least two graduate students at the University of Hawaii at Mānoa and the Moss Landing Marine Laboratories, who will represent the next generation of specialists in the development and application of pFI methodologies to chemical oceanography.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
在了解调节海洋环境中浮游植物生长的化学过程方面的进展和发现,受到海洋学家在相关空间和时间尺度上测量营养物和微量金属的能力的限制。该项目旨在开发新一代自主和高灵敏度的养分和微量金属分析仪,供船上使用和部署在沿海监测站,并有可能纳入现有的自主观测平台(例如,滑翔机、自主水下航行器)。 这种发展将在一系列空间和时间尺度上对营养物动态产生新的见解,并提供新的能力,以获得样本量有限的偏远地区和环境中营养物和痕量金属的高分辨率数据集(例如,盐水、孔隙沃茨)。该项目将使用一种称为可编程流动注射(pFI)的技术开发用于营养物质(磷酸盐和硅酸盐)和微量金属(铝,锰)的新型自动化方法。pFI是一种微流体技术,它允许使用微升体积的样品和试剂自动进行常规的湿化学分析,同时显著减少产生的浪费。 为了将偏远海洋区域特有的低水平微量金属和营养物纳入pFI的分析窗口,还将开发浓缩技术,并将其与分光光度和荧光检测相结合。将通过参加GO-SHIP研究航行和夏威夷海洋时间序列机会航行,用标准海洋学方法相互校准所制定的方法。将在中央加州海洋观测系统(CenCOOS)莫斯登陆海岸站对pFI分析仪的长期无人值守运行进行评估,在那里将部署自主pFI分析仪,以进行为期一年的磷酸盐和硅酸盐的小时时间序列。从海岸站获得的数据将向公众提供,并将补充从该地点获得的现有监测数据,阐明深海营养物动态与沿海涌升系统生物活动之间的联系。新开发的pFI分析仪将使用商用组件和开源软件进行组装,以促进整个化学海洋学界采用这种新方法。该项目还将支持在M #257的夏威夷大学培训和交流至少两名研究生;&该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Automated calibration by a single standard solution prepared in deionized water by flow programming eliminates the schlieren and salinity effects and is applied to the determination of phosphate in sea water of different salinities
通过流程编程在去离子水中配制单一标准溶液进行自动校准,消除纹影和盐度影响,应用于不同盐度海水中磷酸盐的测定
- DOI:10.1016/j.talanta.2022.124041
- 发表时间:2023
- 期刊:
- 影响因子:6.1
- 作者:Hatta, M.;Ruzicka, J.;Measures, C.;Davis, M.
- 通讯作者:Davis, M.
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Mariko Hatta其他文献
海底地形上の流れの安定性:二層準地衡系における一般的条件と線形解析
海底地形的流动稳定性:两层准地转系统的一般条件和线性分析
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Madeline Davis;Mariko Hatta;Jaromir Ruzicka;Chris Measures;田中 祐希 - 通讯作者:
田中 祐希
GPS位置情報をもちいた屋外交通広告の推定
利用GPS位置信息估算户外交通广告
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Efrem d'A vila Ferreira;Mariko Hatta;Yasunaga Takeda;Chika Horikawa;Mizuki Takeuchi;Shigemi Tashiro;Noriko Kato;Hiroki Yokoyama;Yoshio Kurihara;Koichi Iwasaki;Kazuya Fujihara;Hiroshi Maegawa;Hirohito Sone .;村田裕人・星野崇宏 - 通讯作者:
村田裕人・星野崇宏
Using programmable flow injection in batch mode to eliminate the Schlieren effect and enabling the use distilled water standards in seawater nutrient determinations
在批量模式下使用可编程流量注射来消除纹影效应,并在海水营养物测定中使用蒸馏水标准品
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Madeline Davis;Mariko Hatta;Jaromir Ruzicka;Chris Measures - 通讯作者:
Chris Measures
Diabetes Reflect Study: Redefining Fundamental Learning of SMBG through Educational Color Tools
糖尿病反映研究:通过教育色彩工具重新定义 SMBG 的基础学习
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Katsunori Suzuki;Takaaki Sato;Mariko Hatta - 通讯作者:
Mariko Hatta
Characteristics of late summer Arctic brash sea ice and its melting effect on the surface-water biogeochemistry of the Chukchi Shelf and Canada Basin
夏末北极急流海冰特征及其融化对楚科奇陆架和加拿大盆地地表水生物地球化学的影响
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Ryota Akino;Daiki Nomura;Reishi Sahashi;Manami Tozawa;Mariko Hatta;Kohei Matsuno;Wakana Endo;T. Shiozaki;Tatsuya Kawakami;Masato Ito;Akihiko Murata;A. Fujiwara - 通讯作者:
A. Fujiwara
Mariko Hatta的其他文献
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{{ truncateString('Mariko Hatta', 18)}}的其他基金
US GEOTRACES PMT: Shipboard determination of key dissolved trace elements
US GEOTRACES PMT:关键溶解微量元素的船上测定
- 批准号:
1736906 - 财政年份:2017
- 资助金额:
$ 52.38万 - 项目类别:
Standard Grant
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