Collaborative Research: Ideas Lab: Light in the Dark: Fiber Optic Sensing of Climate-Critical Carbon Cycle Components at Water/Ice-Air Interfaces

合作研究：创意实验室：黑暗中的光：水/冰-空气界面气候关键碳循环成分的光纤传感

• 批准号: 2322284
• 负责人: Ruo-Qian Wang
• 金额: $ 33.18万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322284&HistoricalAwards=false
• 关键词: Collaborative; Research; Ideas; Lab; Light

Lakes play an important role in regulating the greenhouse gases that are important to Earth’s climate, but lakes are under an increasing amount of human-induced stress and disturbance, exacerbated by a changing climate. Monitoring of lakes, especially those that are ice covered in winter months, is critical to understand how lakes are changing. However, it is difficult to make such measurements because of the high cost to install and maintain instruments in the lake year-round. This Ideas Lab: Engineering Technologies to Advance Underwater Sciences (ETAUS) project will advance the field of water quality monitoring by developing a sensor that can monitor multiple water quality parameters throughout the year and fill this knowledge gap. The goal for the sensor development is to simultaneously measure parameters that are significant components of measuring a lake’s influence on climate change (carbon dioxide, methane), the health of the lake ecosystem (temperature, pH, salinity, dissolved oxygen), and the impacts of human influence (salinity, temperature). An easy-to-deploy, cost-effective sensor will provide an improved understanding of the carbon footprint of all lake systems that will better inform lake management decisions. The education programs supported by this project will also promote learning and discovery of water quality issues, science, and solutions for children and adults through the partnership with the Museum of Science (MOS) in Boston. The MOS has a focus on working with women and girls from the Boston community in engineering and a field-leading emphasis on universal design. The overall aim of this project is to increase our quantitative understanding of greenhouse gas cycling within lakes through the development of a novel, miniature, fiber-optic multiparameter sensor (FOMS) capable of long-term, under-ice deployment. A fundamental understanding of the wave-material/structure interaction in cascaded high-Q ring resonators will be achieved to develop miniature photonic sensors for simultaneous monitoring of multiple parameters with high analyte specificity and fast response. The FOMS will be developed to measure seven parameters simultaneously, including CO2 and CH4, and deployed on a stationary mooring and mobile underwater robotic platforms for high temporal and spatial resolution data collection. The development and calibration of the FOMS will be guided by a novel machine learning-based sensor calibration model that will help transform the FOMS into an intelligent sensing system, leading to high-fidelity “fingerprint” sensing that can address hardware variations, noise in the monitoring environment, nonlinearities and uncertainties in the sensor response, and cross-talk between the multiple sensor inputs. Data will be collected year-round using the FOMS across stationary and mobile platforms, which will produce four-dimensional data. Data assimilation methods will be compared with the goal of producing a modeling framework that can inform measurement optimization in future deployments. Collectively, the development, testing, and use of the FOMS will produce a measurement tool and framework for a quantitative understanding of GHG production, consumption, and transport in ice-covered lakes.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.

湖泊在调查对地球气候重要的温室气体中起着重要作用，但由于气候变化而加剧了人类引起的压力和灾难越来越多。对湖泊的监测，尤其是冬季覆盖的湖泊，对于了解湖泊的变化至关重要。但是，由于全年安装和维护仪器的成本很高，因此很难进行此类测量。这个想法实验室：推进水下科学（ETAUS）项目的工程技术将通过开发一个可以监视全年多个水质参数并填补此知识差距的传感器来推动水质监测领域。传感器开发的目的是同时测量参数，这些参数是测量湖泊对气候变化影响（二氧化碳，甲烷），湖泊生态系统的健康（温度，pH，盐分，盐度，溶解氧）的重要组成部分，以及人类影响的影响（盐度，温度）。一个易于独E的，具有成本效益的传感器将对所有湖泊系统的碳足迹有了改进的了解，这将更好地告知湖泊管理决策。该项目支持的教育计划还将通过与波士顿科学博物馆（MOS）的合作伙伴关系来促进和发现儿童和成人水质问题，科学和解决方案。 MOS专注于与波士顿社区的工程领域的妇女和女孩合作，并着重于普遍设计。该项目的总体目的是通过开发能够长期，触发不足的部署的新颖，微型，光纤多参数传感器（FOM）来提高我们对湖中温室气体循环的定量理解。将获得对级联高Q环谐振器中波材料/结构相互作用的基本理解，以开发微型光子传感器，以简单地监测具有高分析物特异性和快速响应的多个参数。 FOM将开发以简单地测量包括CO2和CH4在内的七个参数，并部署在固定系泊和移动的水下机器人平台上，以进行高临时和空间分辨率数据收集。 FOM的开发和校准将由一种新型的基于机器学习的传感器校准模型来指导，该模型将有助于将FOM转变为智能传感器系统，从而导致高保真的“指纹”传感器，该传感器可以解决监测环境中的硬件变化，在传感器响应中的非线性和不确定性中的噪声，以及在传感器响应中的不确定性以及多个传感器之间的交叉。数据将全年使用固定和移动平台的FOM全年收集，这将产生四维数据。将数据同化方法与生产建模框架的目标进行比较，该框架可以为未来部署的测量优化提供信息。总体而言，FOM的开发，测试和使用将产生一个测量工具和框架，以定量了解冰覆盖的湖泊中的温室气体生产，消费和运输。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查审查的标准来通过评估来通过评估来获得支持的。