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Respirable Fibre Measurement from Light Scattering Patterns

根据光散射模式测量可呼吸纤维

基本信息

批准号：
2441053
负责人：
金额：
--
依托单位：
University of Hertfordshire
依托单位国家：
英国
项目类别：
Studentship
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：
https://gtr.ukri.org/projects?ref=studentship-2441053
关键词：
Respirable Fibre Measurement Light Scattering

项目摘要

Current detection methods regarding aerosolised fibres can be readily achieved however the level of detail concerning morphology is currently lacking. This project aims to use a blend of computational modelling to emulate the light scattering patterns of airborne, respirable fibres, and empirical data from detectors in an attempt to develop a system of fibre detection and categorisation. Modelled predictions of the scattered light from potentially harmful fibres will be computed through the exploitation of the materials' physical and optical properties for a greater comprehension of the behaviour of light interacting with fibres. The modelled data will be compared with empirical results of categorised fibres with known morphological data collected from the available instrumentation at the University of Hertfordshire, such as the Phase Particle Discriminator (PPD) or the Aerosol Ice Interface Transmission Spectrometer (AIITS) with the morphological information of the material later confirmed via optical or electron microscopy. Comparisons of the predicted and empirical data will then be used to further improve any models for more accurate predictions. As pathogenicity is often related to aspect ratio when regarding fibrous material within the lungs, the initial focus will be to form a relationship between the scattered light and the dimensions of the fibre (most importantly aspect ratio), leading to considerations of more complex morphologies.

关于雾化纤维的当前检测方法可以容易地实现，但是目前缺乏关于形态的细节水平。该项目旨在使用混合的计算模型来模拟空气中的可呼吸纤维的光散射模式，以及来自检测器的经验数据，以尝试开发一个纤维检测和分类系统。将通过利用材料的物理和光学特性来计算来自潜在有害纤维的散射光的模型预测，以便更好地理解光与纤维相互作用的行为。模型化的数据将与经验的分类纤维的结果与已知的形态数据收集从现有的仪器在赫特福德大学，如相粒子鉴别器（PPD）或气溶胶冰界面透射光谱仪（AIITS）与材料的形态信息，后来通过光学或电子显微镜确认。预测数据和经验数据的比较将用于进一步改进任何模型，以获得更准确的预测。由于致病性通常与肺内纤维物质的纵横比有关，因此最初的重点将是形成散射光与纤维尺寸（最重要的是纵横比）之间的关系，从而考虑更复杂的形态。