SBIR Phase II: Total Holographic Characterization of Colloids Through Holographic Video Microscopy

SBIR 第二阶段：通过全息视频显微镜对胶体进行全息表征

• 批准号: 1631815
• 负责人: Laura Philips
• 金额: $ 74.8万
• 依托单位: Spheryx, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631815&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Total; Holographic

This Small Business Innovation Research (SBIR) Phase II project will enable a commercial implementation of holographic video microscopy, a fast, precise and flexible technology for measuring the properties of individual colloidal particles suspended in fluid media. This disruptive technology solves critical manufacturing problems across industries that work with colloidal dispersions. Demonstrated applications include: 1) monitoring the growth of nanoparticle agglomerates in precision slurries used to polish semiconductor wafers where scratches due to slurry agglomerates are responsible for waste valued at $1 billion annually; 2) tracking concentrations of dangerous contaminants in wastewater streams; and 3) measuring the concentration of protein aggregates in biopharmaceuticals, a safety concern noted by the Food and Drug Administration (FDA) in this $250 billion industry. Holographic video microscopy is unique among particle-characterization technologies in providing comprehensive information about the size, shape and composition of individual particles in real time and in situ. Having access to this wealth of data facilitates product development, creates new opportunities for process control and provides a new tool for quality assurance across a broad spectrum of industries enabling safer, less expensive products for consumers while providing cost savings to manufacturers.The technical objectives of this project are: 1) to optimize the design of the underlying holographic microscopy system without compromising the quality of results; 2) to enable quantitative concentration determination including corrections for perturbations introduced by flow dynamics; 3) to expand the domain of operation to characterize non-spherical particles and 4) to apply machine-learning algorithms for automated robust operation. Using holographic video microscopy for commercial applications requires adaptation and innovation in the design of the prototype instrument that was used to demonstrate feasibility. Streamlining the optical train will require advanced modeling and the creation of new methods of correcting optical aberrations to enable ease of manufacture. Additional improvements in design will include advances in improving microfluidic flow control to generate accurate concentration determination, to adapt holographic analysis algorithms for characterizing the structure of aspheric particles, and to extend analytical capabilities for turbid fluids. Finally, innovative machine-learning using neural network algorithms demonstrated significant improvements for analytical robustness in Phase I and will be extended to a wider range of applications. The Phase II effort will enable holographic video microscopy of real-world samples with typical measurement times of a few minutes.

这个小企业创新研究 (SBIR) 第二阶段项目将实现全息视频显微镜的商业实施，这是一种快速、精确和灵活的技术，用于测量悬浮在流体介质中的单个胶体颗粒的特性。这项颠覆性技术解决了胶体分散体各行业的关键制造问题。 演示的应用包括：1) 监测用于抛光半导体晶圆的精密浆料中纳米颗粒团聚体的生长，其中浆料团聚体造成的划痕每年造成价值 10 亿美元的浪费； 2) 跟踪废水流中危险污染物的浓度； 3) 测量生物制药中蛋白质聚集体的浓度，这是美国食品和药物管理局 (FDA) 在这个价值 2500 亿美元的行业中注意到的一个安全问题。 全息视频显微镜在颗粒表征技术中是独一无二的，它可以实时、原位地提供有关单个颗粒的尺寸、形状和成分的全面信息。获取这些丰富的数据可以促进产品开发，为过程控制创造新的机会，并为各行业的质量保证提供新工具，为消费者提供更安全、更便宜的产品，同时为制造商节省成本。该项目的技术目标是：1）在不影响结果质量的情况下优化底层全息显微镜系统的设计； 2) 实现定量浓度测定，包括对流动动力学引入的扰动进行校正； 3) 扩展操作领域以表征非球形颗粒，4) 应用机器学习算法进行自动化鲁棒操作。 将全息视频显微镜用于商业应用需要对用于证明可行性的原型仪器的设计进行调整和创新。简化光学系统需要先进的建模和创建校正光学像差的新方法，以方便制造。设计的其他改进将包括改进微流体流量控制以产生精确的浓度测定、采用全息分析算法来表征非球面颗粒的结构以及扩展混浊流体的分析能力。最后，使用神经网络算法的创新机器学习在第一阶段展示了分析鲁棒性的显着改进，并将扩展到更广泛的应用。第二阶段的工作将使真实世界样品的全息视频显微镜成为可能，典型的测量时间为几分钟。

项目成果

Holographic characterization of colloidal particles in turbid media

• DOI: 10.1063/1.4999101
• 发表时间: 2017-10-09
• 期刊: APPLIED PHYSICS LETTERS
• 影响因子: 4
• 作者: Cheong, Fook Chiong;Kasimbeg, Priya;Grier, David G.
• 通讯作者: Grier, David G.

Holographic characterization of contaminants in water: Differentiation of suspended particles in heterogeneous dispersions

• DOI: 10.1016/j.watres.2017.06.006
• 发表时间: 2017-10-01
• 期刊: WATER RESEARCH
• 影响因子: 12.8
• 作者: Philips, Laura A.;Ruffner, David B.;Grier, David G.
• 通讯作者: Grier, David G.

The role of the medium in the effective-sphere interpretation of holographic particle characterization data

• DOI: 10.1039/c9sm01916b
• 发表时间: 2020-01-28
• 期刊: SOFT MATTER
• 影响因子: 3.4
• 作者: Odete, Mary Ann;Cheong, Fook Chiong;Grier, David G.
• 通讯作者: Grier, David G.

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

• DOI: 10.1364/oe.26.015221
• 发表时间: 2018-06-11
• 期刊: OPTICS EXPRESS
• 影响因子: 3.8
• 作者: Hannel, Mark D.;Abdulali, Aidan;Grier, David G.
• 通讯作者: Grier, David G.

Lifting degeneracy in holographic characterization of colloidal particles using multi-color imaging

使用多色成像消除胶体颗粒全息表征的简并性

• DOI: 10.1364/oe.26.013239
• 发表时间: 2018
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Ruffner, David B.;Cheong, Fook Chiong;Blusewicz, Jaroslaw M.;Philips, Laura A.
• 通讯作者: Philips, Laura A.