Laser-Based Diagnostics for Aerosolized Nanoparticles

基于激光的雾化纳米颗粒诊断

• 批准号: RGPIN-2018-03756
• 负责人: Daun, Kyle
• 金额: $ 4.66万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712543
• 关键词: Laser; Based; Diagnostics; Aerosolized; Nanoparticles

The unique properties of nanoparticles place them prominently at the frontiers of material science. Metal nanoparticles improve the performance solar photovoltaics, for example, while graphitic nanoparticles enable smaller, lighter, and longer-lasting batteries. Since nanoparticle functionality depends strongly on size and shape, new tools that measure these attributes are needed to control bulk nanoparticle production, and to understand their nucleation and growth. At the same time, growing attention focuses on how nanoparticles adversely affect human health and the environment, also in ways that depend strongly on size and morphology. Scientists and engineers increasingly turn to laser-induced incandescence (LII) and multiangle elastic light scattering (MAELS) to characterize synthetic nanoparticles. Nevertheless, significant issues remain with both techniques. LII researchers have derived increasingly-elaborate measurement models for interpreting data without considering how model complexity impacts the reliability of LII-derived estimates, while several commonly-observed spectral features in LII data elude physical interpretation altogether. In the case of MAELS, small errors in the light scattering model are amplified by data inversion into large biases in recovered parameters. These deficiencies limit the reliability of nanoparticle-related quantities derived from these diagnostics. The proposed research program will address these shortcomings by improving the LII and MAELS measurement models, with the long-term goal of developing reliable laser-based nanoparticle diagnostics. Research will commence with a theoretical and experimental investigation of the LII spectroscopic submodel, focusing on a microplasma that may form around the nanoparticle during laser excitation. In parallel, the applicant's team will use Bayesian techniques to derive robust LII and MAELS measurement models for metal nanoparticles, which will then be extended to the more complex case of soot. Finally, these algorithms will be incorporated into a combined LII-MAELS instrument that simultaneously measures primary particle diameter, volume fraction, radius-of-gyration, and fractal dimension of aerosolized nanoaggregates. The metrology techniques developed through this research will be used by Canada's emerging nanotechnology industry to develop new products and materials, and help safeguard Canada's environment and the health of Canadians. More fundamentally, this research will improve our understanding of light-nanoparticle interactions, and provide a way to estimate thermophysical properties under extreme temperatures inaccessible to other measurement modalities. Finally, this research will equip nine HQP with skills in experimentation, theoretical analysis, and numerical simulation, preparing them for careers in both industry and academia.

纳米颗粒的独特性质使其处于材料科学的前沿。例如，金属纳米粒子可以提高太阳能光伏发电的性能，而石墨纳米粒子可以实现更小、更轻、更耐用的电池。由于纳米颗粒的功能在很大程度上取决于尺寸和形状，因此需要测量这些属性的新工具来控制批量纳米颗粒的生产，并了解它们的成核和生长。与此同时，人们越来越关注纳米粒子如何对人类健康和环境产生不利影响，其影响方式也很大程度上取决于尺寸和形态。 科学家和工程师越来越多地利用激光诱导白炽光 (LII) 和多角度弹性光散射 (MAELS) 来表征合成纳米粒子。然而，这两种技术仍然存在重大问题。 LII 研究人员推导出了越来越复杂的测量模型来解释数据，但没有考虑模型复杂性如何影响 LII 得出的估计的可靠性，而 LII 数据中一些常见的光谱特征完全无法解释物理解释。在 MAELS 的情况下，光散射模型中的小误差会通过数据反演放大为恢复参数中的大偏差。这些缺陷限制了从这些诊断中得出的纳米颗粒相关量的可靠性。 拟议的研究计划将通过改进 LII 和 MAELS 测量模型来解决这些缺点，长期目标是开发可靠的基于激光的纳米颗粒诊断。研究将从 LII 光谱子模型的理论和实验研究开始，重点关注激光激发期间纳米颗粒周围可能形成的微等离子体。与此同时，申请人的团队将使用贝叶斯技术导出金属纳米颗粒的稳健 LII 和 MAELS 测量模型，然后将其扩展到更复杂的烟灰情况。最后，这些算法将被纳入组合的 LII-MAELS 仪器中，该仪器可同时测量雾化纳米聚集体的初级粒径、体积分数、回转半径和分形维数。 通过这项研究开发的计量技术将被加拿大新兴的纳米技术行业用来开发新产品和材料，并帮助保护加拿大的环境和加拿大人的健康。 更根本的是，这项研究将增进我们对光-纳米颗粒相互作用的理解，并提供一种估计其他测量方式无法达到的极端温度下的热物理性质的方法。最后，这项研究将为九名高级工程师提供实验、理论分析和数值模拟方面的技能，为他们在工业界和学术界的职业生涯做好准备。