Lab-on-Chip Measurement of Nitrate and Nitrite for In Situ Analysis of Natural Waters

Lab-on-Chip Measurement of Nitrate and Nitrite for In Situ Analysis of Natural Waters
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DOI:
10.1021/es300419u
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发表时间:
2012-09-04
影响因子:
11.4
通讯作者:
Morgan, Hywel
Morgan, Hywel
中科院分区:
环境科学与生态学1区
文献类型:
--
作者:
Beaton, Alexander D.;Cardwell, Christopher L.;Morgan, Hywel

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微流控技术允许传统上在实验室环境中使用台式设备进行的化学分析方法的小型化。当应用于环境监测时,这些“芯片实验室”系统可以在具有大量测量节点的分布式传感器网络上原位执行高性能化学分析方法。在这里,我们介绍了第一个新一代微流体化学分析系统,该系统具有足够的分析性能和鲁棒性,可用于自然沃茨水域。该系统检测硝酸盐和亚硝酸盐(高达350 μ M,21.7 mg/L的NO3-)的检测限(LOD)为0.025 μ M的硝酸盐(0.0016 mg/L的NO3-)和0.02 μ M的亚硝酸盐(0.00092 mg/L的NO2-)。这种性能适用于几乎所有的自然沃茨(除了贫营养的开放海洋),该设备被部署在河口环境(南安普敦水),以监测不同盐度的沃茨中的硝酸盐+亚硝酸盐浓度。该系统能够跟踪由于一段时间的高降雨后河流流量增加而导致的河口沃茨水域硝酸盐-盐度关系的变化。实验室表征和部署数据,展示了该系统的能力,以获得高时间分辨率的数据。
Microfluidic technology permits the miniaturization of chemical analytical methods that are traditionally undertaken using benchtop equipment in the laboratory environment. When applied to environmental monitoring, these "lab-on-chip" systems could allow high-performance chemical analysis methods to be performed in situ over distributed sensor networks with large numbers of measurement nodes. Here we present the first of a new generation of microfluidic chemical analysis systems with sufficient analytical performance and robustness for deployment in natural waters. The system detects nitrate and nitrite (up to 350 mu M, 21.7 mg/L as NO3-) with a limit of detection (LOD) of 0.025 mu M for nitrate (0.0016 mg/L as NO3-) and 0.02 mu M for nitrite (0.00092 mg/L as NO2-). This performance is suitable for almost all natural waters (apart from the oligotrophic open ocean), and the device was deployed in an estuarine environment (Southampton Water) to monitor nitrate+nitrite concentrations in waters of varying salinity. The system was able to track changes in the nitrate-salinity relationship of estuarine waters due to increased river flow after a period of high rainfall. Laboratory characterization and deployment data are presented, demonstrating the ability of the system to acquire data with high temporal resolution.