U.S.-RoI-NI R&D Partnership: Ultrasensitive Nitrogen Sensor using Imprinted Polymer Assisted-Bacteria for Real-Time Monitoring of Water Quality

伦斯勒理工学院获奖，以整合来自三个国家(美国、爱尔兰共和国和北爱尔兰、英国)的四个研究小组，共同设计、建造、验证和现场测试传感器系统，以实时检测三种最常见的氮：硝酸盐、亚硝酸盐和氨/铵。在全球范围内，淡水和海洋生态系统受到多种环境压力的影响，包括污染物、入侵物种、气候变化、酸化和营养过剩。过量的营养物质具有特别重要的意义，因为它们是有害的藻类水华和“死亡区域”的驱动力，这些现象在世界各地越来越多地发生，并可能使生态系统走向重大的、可能是灾难性的生态事件。虽然科学家们努力监测、理解过量营养的影响并建立模型，但目前的主要挑战是以合理的成本使用实时传感器频繁监测氮[N]和磷[P]的动态。拥有高频、实时的营养数据将使世界各地的基础和应用研究人员能够将营养数据与来自现有传感器网络和遥感的其他数据相结合，以应对有害藻类大量繁殖和死亡区域的全球挑战。这项研究还将整合四个研究小组之间令人兴奋的K-12外联工作。利用来自不同学科的团队专家，这些小组将共同创建教育模块，将知识从基础信息螺旋式上升到高级信息，以进行广泛的教育和参与。这些小组将为高中生和本科生创建为期一周的暑期项目，整合面对面和虚拟的教育体验，了解分析化学、工程学和湖沼学对生产新一代水生传感器的重要性。学生们将参观并参与皇后大学、都柏林城市大学和伦斯勒的最先进设施的研究，以及RPI在纽约州乔治湖部署的传感器网络(与IBM Research和George Lake Association合作)。如今，研究人员对营养物质的监测大多依赖于不频繁地采集水样并将其带回实验室进行台式测试。测量水生生态系统营养物质的较新技术是一次性使用的，或者需要经常更换试剂。此外，少数可现场部署实时数据的营养传感器--如磷传感器--成本较高(3万美元)，这严重限制了可以部署的营养传感器的数量。水生研究人员需要实时、高频、低成本、低检测下限的营养传感器，这些传感器可以嵌入现有的传感器网络中，监测其他水变量的套件。该研究团队建议建造一种单一传感器，该传感器使用1)聚合物分离和浓缩各种形态的氮，2)细菌将各种形态的氮转化为单一形态(即亚硝酸盐)，3)先进的3D打印微流体，以确保在一个完整的分析仪平台中通往主动传感器的路线，以便现场部署，以及4)使用新型超灵敏检测器的更灵敏的检测系统。监测复杂自然环境中水生营养物质的实时动态有可能对这些营养物质如何影响水生生态系统带来变革性的新见解，重点是有害藻华的全球问题。这种洞察力对于提高理解、预测和缓解这些影响的能力至关重要。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

An award is made to Rensselaer Polytechnic Institute to integrate four research groups from the three nations (the United States, Republic of Ireland and Northern Ireland, UK) to collaboratively design, build, validate, and field test a sensor system for the real-time detection of the three most commonly monitored forms of nitrogen: nitrate, nitrite, and ammonia/ammonium. Across the globe, freshwater and marine ecosystems are threatened by the effects of multiple environmental stressors including pollutants, invasive species, climate change, acidification, and excess nutrients. Excess nutrients are of particular importance since they are a driver of harmful algal blooms and “dead zones,” which are increasingly occurring around the world and can tip ecosystems toward significant and potentially catastrophic ecological events. While scientists strive to monitor, understand, and model the effects of excess nutrients, the current major challenge is to frequently monitor nitrogen [N] and phosphorus [P]) dynamics using real-time sensors at a reasonable cost. Having high-frequency, real-time nutrient data would allow basic and applied researchers around the world to integrate nutrient data with other data from existing sensor networks and remote sensing to address the global challenge of harmful algal blooms and dead zones. The research will also integration exciting K-12 outreach efforts among the four research groups. Using team experts from different disciplines, the groups will work together to create educational modules that spiral knowledge from basic to advanced information to educate and engage broadly. These groups will create week-long summer programs that integrate in-person and virtual educational experiences for high school students and college undergraduates to learn about the importance of analytical chemistry, engineering, and limnology to produce new generations of aquatic sensors. Students will visit and participate in the research of state-of-the-art facilities at Queen’s University, Dublin City University, and Rensselaer, as well as the sensor network that has been deployed by RPI on Lake George, NY (in collaboration with IBM Research and The Lake George Association).Most nutrient monitoring today by researchers depends on collecting water samples infrequently and bringing them back to the lab for benchtop testing. Newer technologies for measuring nutrients in aquatic ecosystems are single-use or require the frequent replacement of reagents. Moreover, the few nutrient sensors that are field-deployable with real-time data—such as phosphorus sensors—have a high cost ($30k) that strongly limits the number of nutrient sensors that can be deployed. Aquatic researchers need real-time, high-frequency, low-cost nutrient sensors with low detection limits that can be embedded in existing sensor networks that monitor suites of other water variables. This research team proposes to build a single sensor that uses 1) polymers to separate and concentrate each form of nitrogen, 2) bacteria to convert each form of nitrogen into a single form (i.e., nitrite), 3) advanced 3D printed microfluidics to ensure routes toward the active sensor in a complete analyzer platform for field deployment, and 4) a more sensitive detection system using novel ultrasensitive detectors. Monitoring the real-time dynamics of aquatic nutrients in complex natural environments has the potential to bring transformative new insights into how these nutrients impact aquatic ecosystems, with a focus on the global issue of harmful algal blooms. Such insights are crucial for improving capabilities to understand, predict, and mitigate these impacts.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.