SBIR Phase I: Cost-effective, portable and fast platform for automated characterization of aquatic microorganisms and other particles for aquaculture, public health and marine res
SBIR Phase I: Cost-effective, portable and fast platform for automated characterization of aquatic microorganisms and other particles for aquaculture, public health and marine res
批准号:
1843093
负责人:
Maxim Batalin
金额:
$22.49万
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
项目状态:
已结题
起止时间:
2019-02-01 至 2020-02-29
中文摘要
小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是开发一个具有成本效益的高性能平台,以监测和表征水中的浮游植物和其他微生物。其中一些水生微生物可能是有毒的,甚至是致命的,导致重大的公共卫生问题(例如最近在美国佛罗里达州发生的“赤潮”事件)和严重的经济后果(即有害的藻华对水产养殖业的致命影响)。目前最先进的监测技术包括使用台式显微镜或光学数字成像系统进行费力且昂贵的人工样本采集和评估。相比之下,拟议的平台将具有变革性,能够实现对水生微生物的低成本、便携和快速监测和自动化表征。因此,这一平台将使水生微生物监测发生革命性变化,并且由于成本较低,它将使该技术在其他市场得到更广泛的应用。其中一些未来的应用包括更有效的生物燃料研究和开发(通过藻类监测)、海洋生物科学和教育、水处理设施中颗粒物和病原体的一般监测、生产藻类监测。因此,拟议技术的潜在社会影响和商业潜力是革命性的。拟议项目旨在开发和评估一种便携式、快速、耐用和环境稳定的成像流式细胞仪技术,该技术将自动监测微生物,如浮游植物和流水中存在的病原体,并将能够进行特定的分类。现有的基于光学的流式细胞仪解决方案价格昂贵,而且不适用于现场使用。与基于实验室的流式细胞仪或手持化验不同,拟议的系统将不依赖试剂或标签,因此不需要专家/专业人员,并将保持评估的水不变。因此,它可以安装为连续的无人值守操作,并且由于省去了专家的时间、昂贵的试剂和荧光团,每次测试将更具成本效益。此外,拟议的系统还将与能够自动识别微生物和其他感兴趣的微观物体的机器学习引擎相结合。最后,将对预先收集的本地沿海海水样本进行评估,以确定是否存在三种典型的浮游植物微生物,并提供所有检测到的微型物体的统计分布。因此,拟议的系统将具有变革性,并提供创新和独特的能力,方便地测量水样和自动识别识别的微对象。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。
英文摘要
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a cost-effective high-performance platform to monitor and characterize phytoplankton and other microorganisms in water. Some of these aquatic microorganisms may be toxic or even fatal resulting in significant public health concerns (such as recent "red tide" events in Florida, USA) and drastic economic consequences (i.e. deadly effects of harmful algal blooms on the aquaculture industry). The current state of the art in monitoring technology includes laborious and expensive manual sample collection and evaluation using a benchtop microscope or optical digital imaging system. In contrast, the proposed platform will be transformative by enabling low-cost, portable and fast monitoring and automated characterization of aquatic microorganisms. Therefore, this platform will revolutionize monitoring of aquatic microorganisms and, being low cost, it will enable a much wider application of the technology to other markets. Some of these future applications include more efficient biofuels research and development (via algae monitoring), marine biology science and education, general monitoring of particles and pathogens at the water treatment facilities, production algae monitoring. Therefore, the potential societal impact and commercial potential of the proposed technology is transformative.The proposed project aims to develop and evaluate a portable, rapid, durable and environmentally-stable imaging flow-cytometry technology that will automatically monitor microorganisms, such as phytoplankton and pathogens present in the flowing water, and will be capable of specific classification. Existing optics-based flow-cytometry solutions are expensive and not durable for field use. Unlike lab-based flow cytometers or hand-held assays, the proposed system will not rely on reagents or labeling, and therefore will not need an expert/professional, and will keep the evaluated water unchanged. Thus, it can be installed for continual unattended operation and will be much more cost-effective per test due to elimination of an expert's time, costly reagents and fluorophores. Furthermore, the proposed system will also be integrated with a machine learning engine capable of automated identification of microorganisms as well as other micro-objects of interest. Finally, the proposed system will be evaluated on pre-collected samples of local coastal ocean water to determine the presence of three example types of phytoplankton microorganisms and to provide statistical distribution of all of the detected micro-objects. Therefore, the proposed system will be transformative and provide an innovative and unique capability to expediently survey samples of water and automatically characterize identified micro objects.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.
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SBIR Phase II: AI-based automated, portable, and high-throughput platform for early identification and characterization of potentially harmful microorganisms in aquaculture
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批准号:2052393
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项目类别:Cooperative Agreement
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资助金额:$100.0万
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财政年份:2021
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负责人:Maxim Batalin
-
依托单位:
国内基金
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