Decoding Nitrogen Dynamics in Soil through Novel Integration of in-situ Wireless Soil Sensors with Numerical Modeling
通过原位无线土壤传感器与数值建模的新颖集成来解码土壤中的氮动态
基本信息
- 批准号:NE/T010584/1
- 负责人:
- 金额:$ 84.93万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2020
- 资助国家:英国
- 起止时间:2020 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Excess nitrogen leaching from agricultural and horticultural lands into waterways is a long-standingchallenge for agricultural sustainability and environmental protection. An effective approach to improvingthe water/fertilizer use efficiency is through precision farming practices guided by real-time monitoringand near-term forecast of crop irrigation and fertilization needs. However, this initiative is hampered bythe lack of reliable sensing technologies and modeling tools for elucidating the spatiotemporal variabilityof soil moisture and nitrogen concentration. The current state of the art is to collect soil samples at fieldsites and bring to the laboratory for analysis, which is prohibitively expensive, labor-intensive, and isunable to track the transient variation of soil nitrogen content in a timely manner.This interdisciplinary collaborative US-UK SitS project aims to tackle the grand challenge of decodingnitrogen dynamics in the soil through seamless integration of four innovative solutions: hydrogel-coatingsolid-state ion-selective membrane (HS-ISM) based wireless nitrogen sensing technology, droplet-flowmicrofluidic-based sensors (DFMS) as in situ calibration, high-resolution profiling of nitrogen speciesand moisture level in different ecosystems, and data-driven modeling of rhizosphere nitrogen dynamics.Together, this project will transform existing labor-intensive and inefficient soil analysis practice toautomated and highly efficient soil nitrogen dynamics decoding and field modeling strategy, and thusadvancing the underlying science and engineering of soil nitrogen dynamics and providing guidance forsustainable agricultural and ecosystem management.
过量的氮从农业和园艺用地渗入水道是农业可持续性和环境保护的长期挑战。提高水/肥料利用效率的有效方法是通过实时监测和作物灌溉和施肥需求的近期预测指导下的精准农业实践。然而,由于缺乏可靠的传感技术和建模工具来阐明土壤水分和氮浓度的时空变化,这一举措受到了阻碍。目前的技术水平是在现场采集土壤样品并带到实验室进行分析,这种方法成本高昂,劳动强度大,并且无法及时跟踪土壤氮含量的瞬态变化。这个跨学科合作的美英sit项目旨在通过四种创新解决方案的无缝整合,解决土壤中氮动态解码的巨大挑战:基于水凝胶涂层的固态离子选择膜(HS-ISM)无线氮传感技术,基于液滴流微流体的传感器(DFMS)作为原位校准,不同生态系统中氮种类和水分水平的高分辨率剖面,以及根际氮动力学的数据驱动建模。该项目将把现有的劳动密集型和低效的土壤分析实践转变为自动化、高效的土壤氮动力学解码和田间建模策略,从而推进土壤氮动力学的基础科学和工程,为可持续农业和生态系统管理提供指导。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Sensitive absorbance measurement in droplet microfluidics via multipass flow cells
通过多通道流通池对液滴微流体进行灵敏的吸光度测量
- DOI:10.29363/nanoge.eimc.2021.021
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Lu B
- 通讯作者:Lu B
3D printed filtration and separation devices with integrated membranes and no post-printing assembly
3D打印过滤和分离装置,带有集成膜,无需打印后组装
- DOI:10.1039/d3re00245d
- 发表时间:2024
- 期刊:
- 影响因子:3.9
- 作者:Clark M
- 通讯作者:Clark M
Generation of "droplet trains" for continuous and multiple sample or multiple step assays in droplet microfluidics
生成“液滴序列”,用于液滴微流体中的连续和多个样品或多步骤测定
- DOI:
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Evans G.W.H.
- 通讯作者:Evans G.W.H.
Easily-fabricated fluoropolymer chips for sensitive long-term absorbance measurement in droplet microfluidics
易于制造的含氟聚合物芯片,用于液滴微流体中灵敏的长期吸光度测量
- DOI:
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Nightingale A.M.
- 通讯作者:Nightingale A.M.
AN IN SITU DROPLET MICROFLUIDICS BASED AMMONIUM SENSOR AND ITS APPLICATION TO A SEQUENTIAL BATCH BIOREACTOR
基于原位液滴微流控的铵传感器及其在序批式生物反应器中的应用
- DOI:
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Bhuiyan W.T.
- 通讯作者:Bhuiyan W.T.
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{{ truncateString('Xize Niu', 18)}}的其他基金
UK-Further development of droplet microfluidic based chemical sensors for rapid measurement of nutrients in water
英国-进一步开发基于液滴微流体的化学传感器,用于快速测量水中的营养物质
- 批准号:
NE/S013458/1 - 财政年份:2019
- 资助金额:
$ 84.93万 - 项目类别:
Research Grant
Droplet microfluidic based chemical sensors for rapid measurement of nutrients in water
基于液滴微流体的化学传感器,用于快速测量水中的营养物质
- 批准号:
NE/P004016/1 - 财政年份:2016
- 资助金额:
$ 84.93万 - 项目类别:
Research Grant
Lab-on-an-Organ: A droplet based portable continuous chemical sensor
器官实验室:基于液滴的便携式连续化学传感器
- 批准号:
EP/M012425/1 - 财政年份:2015
- 资助金额:
$ 84.93万 - 项目类别:
Research Grant
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