PFI:BIC Human-Centered Smart-Integration of Mobile Imaging and Sensing Tools with Machine Learning for Ubiquitous Quantification of Waterborne and Airborne Nanoparticles

PFI：BIC 以人为中心的移动成像和传感工具与机器学习的智能集成，可实现水性和空气性纳米粒子的普遍定量

• 批准号: 1533983
• 负责人: Aydogan Ozcan
• 金额: $ 100万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533983&HistoricalAwards=false
• 关键词: PFI; BIC; Human; Centered; Smart

This Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) project focuses on the creation of a human-centered smart toolset and service system for on-site and ubiquitous quantification and automated charaterization/classification of nanosize objects. Nanoparticles are being used in more and more commercial and industrial products while their health and environmental implications are still under debate. The toxicity of nanomaterials not only varies among different materials, but is also highly dependent on the dose of exposure. Developing a sensitive method to detect the release and spatio-temporal distribution of nanoparticles in the environment as well as in daily lives is a high priority before their toxicity effects are fully understood via long-term toxicological studies. Despite this urgent need for widespread detection and quantification of nanoparticle distributions, current technologies are lacking appropriate features for ubiquitous and cost-effective mapping and quantification of nanoparticle contamination. This project aims to create a transformative and human-centered toolset for on-site and ubiquitous quantification and automated characterization of nanomaterials found in houses, workplaces and the environment based on the cost-effective integration of computational imaging and mobile sensing techniques with big data based dynamic machine learning algorithms. The central challenge in this project is to translate the bulky and expensive laboratory equipment currently used for nanoparticle quantification and characterization to field-portable, easy-to-use, cost-effective, and rapid analysis devices and smart service systems aiming to be massively used by consumers in their daily routines. To solve this challenge, highly sensitive optical imaging systems will be developed based on mass-produced Complementary Metal-Oxide Semiconductor (CMOS) sensor chips embedded in mobile phones with extraordinary signal to noise ratios (SNR) and large fields-of-view for high-throughput machine learning based automated nanoparticle analysis and classification. One approach this will take is to combine computational microscopy with self-assembled nanolenses around nanoparticles that significantly enhance imaging SNR and contrast. The aim of this approach is to enable automated detection and sizing of individual nanoparticles, mono-dispersed samples, and complex poly-dispersed mixtures, where the sample concentrations can span ~5 orders-of-magnitude and particle sizes can range from 40 nm to millimeter-scale, which provide unmatched performance metrics compared to existing nanoparticle sizing approaches. Another approach that will be implemented is the development of highly sensitive multi-modal (e.g. fluorescence plus dark-field) mobile phone based microscopy platforms for distributed nanoparticle imaging and sensing. Furthermore, in terms of big data analysis and machine learning tools, the techniques in this project can adaptively learn "semantic" similarities that can be used for more accurate data classification. These techniques are unlike existing techniques developed so far in the literature. The extant technologies are based only on signal similarities, which do not work well on multi-modality data. The smart and adaptive methods of this project are the first in the literature that come with confidence bounds, that is, they not only have the capability to accurately classify the information, but they also provide guarantees about the accuracy of this classification, which is quite important for self-learning smart service systems. Through these field-portable devices that are integrated with adaptive big data based decision analytics and quantification algorithms, spatio-temporal maps of nanoparticle concentrations and size distributions in various consumer samples will be created for public or personal monitoring (e.g., measurements of waterborne/airborne particles at home, workplace, or airborne particles along a freeway, etc.).The broader impacts of this transformative research include (1) The development of these nanoparticle sensing and quantification platforms and smart service systems will extend the boundaries of current optical metrology science, resulting in new advances in the fields of nanophotonics and optical microscopy (2) These devices will also be easy to translate into various biomedical, chemical and material science applications, significantly impacting the use and regulations of nanotechnologies in consumer market and related products. (3) This project would deliver a paradigm-shift by ubiquitous quantification and spatiotemporal mapping/monitoring of nanoparticle contamination and exposure even in non-laboratory settings, assisting in the revelation and better understanding of various cause-effect relationships at the consumer level that have remained unidentified so far due to the limitations of existing nano-imaging, detection and quantification technologies, also providing maps of potential health risks. (4) This project will also establish a complementary educational outreach program based in California.The lead institution and primary partners included in this cross-organizational interdisciplinary project are: Lead Academic Institution: University of California, Los Angeles, CA, School of Engineering, Electrical and Bioengineering Departments; Primary Industrial Partner: Holomic LLC (Small Business located in Los Angeles, CA); Other Industrial Partner: Google Inc. (Large Business located in Mountain View, CA).

该创新伙伴关系：建设创新能力 (PFI:BIC) 项目侧重于创建以人为本的智能工具集和服务系统，用于纳米尺寸物体的现场和无处不在的量化和自动表征/分类。纳米粒子正被用于越来越多的商业和工业产品，但其对健康和环境的影响仍存在争议。纳米材料的毒性不仅因不同材料而异，而且高度依赖于暴露剂量。在通过长期毒理学研究充分了解纳米颗粒的毒性作用之前，开发一种灵敏的方法来检测纳米颗粒在环境和日常生活中的释放和时空分布是当务之急。尽管迫切需要广泛检测和量化纳米颗粒分布，但当前技术缺乏适当的功能来普遍且经济高效地对纳米颗粒污染进行绘图和量化。该项目旨在创建一个变革性的、以人为本的工具集，基于计算成像和移动传感技术与基于大数据的经济高效的集成，对房屋、工作场所和环境中发现的纳米材料进行现场和无处不在的量化和自动表征。动态机器学习算法。该项目的核心挑战是将目前用于纳米颗粒定量和表征的笨重且昂贵的实验室设备转化为现场便携式、易于使用、经济高效、快速的分析设备和智能服务系统，旨在大规模使用消费者在日常生活中。为了解决这一挑战，将开发基于批量生产的嵌入手机中的互补金属氧化物半导体（CMOS）传感器芯片的高灵敏度光学成像系统，该芯片具有非凡的信噪比（SNR）和大视场，可实现高-基于吞吐量机器学习的自动化纳米粒子分析和分类。采取的一种方法是将计算显微镜与纳米颗粒周围的自组装纳米透镜结合起来，从而显着增强成像信噪比和对比度。这种方法的目的是实现单个纳米颗粒、单分散样品和复杂多分散混合物的自动检测和尺寸测定，其中样品浓度可以跨越约 5 个数量级，颗粒尺寸可以从 40 nm 到 40 nm 不等。毫米级，与现有的纳米颗粒尺寸测量方法相比，它提供了无与伦比的性能指标。将实施的另一种方法是开发基于手机的高灵敏度多模式（例如荧光加暗场）显微镜平台，用于分布式纳米颗粒成像和传感。此外，在大数据分析和机器学习工具方面，该项目中的技术可以自适应地学习“语义”相似性，从而可以用于更准确的数据分类。这些技术与文献中迄今为止开发的现有技术不同。 现有技术仅基于信号相似性，在多模态数据上效果不佳。该项目的智能自适应方法是文献中第一个带有置信界限的方法，也就是说，它们不仅具有对信息进行准确分类的能力，而且还为这种分类的准确性提供了保证，这是相当不错的。对于自学习智能服务系统非常重要。通过这些与基于自适应大数据的决策分析和量化算法相集成的现场便携式设备，将创建各种消费者样本中纳米颗粒浓度和尺寸分布的时空图，以供公共或个人监测（例如，水性/空气性的测量）这项变革性研究的更广泛影响包括（1）这些纳米颗粒传感和量化平台以及智能服务系统的发展将扩展当前光学技术的边界计量科学，从而在纳米光子学和光学显微镜领域取得新进展 (2) 这些设备也将很容易转化为各种生物医学、化学和材料科学应用，显着影响消费市场和相关产品中纳米技术的使用和监管。 (3) 该项目将通过对纳米颗粒污染和暴露进行无处不在的量化和时空测绘/监测（甚至在非实验室环境中）来实现范式转变，从而帮助揭示和更好地理解消费者层面的各种因果关系由于现有纳米成像、检测和量化技术的局限性，迄今为止仍无法识别，也提供了潜在健康风险的地图。 (4) 该项目还将在加州建立一个补充教育推广计划。该跨组织跨学科项目的牵头机构和主要合作伙伴包括： 牵头学术机构：加州大学洛杉矶分校工程学院，电气和生物工程系；主要工业合作伙伴：Holomic LLC（位于加利福尼亚州洛杉矶的小型企业）；其他工业合作伙伴：Google Inc.（位于加利福尼亚州山景城的大型企业）。