Respirable Fibre Measurement from Light Scattering Patterns

目前关于雾化纤维的检测方法可以很容易地实现，但是关于形态学的细节水平目前缺乏。该项目旨在使用混合计算模型来模拟空气中、可呼吸纤维的光散射模式，以及来自探测器的经验数据，试图开发一种纤维检测和分类系统。对潜在有害纤维散射光的建模预测将通过利用材料的物理和光学特性来计算，从而更好地理解光与纤维相互作用的行为。建模数据将与从赫特福德大学可用仪器收集的已知形态数据的分类纤维的经验结果进行比较，例如相位粒子鉴别器（PPD）或气溶胶冰界面透射光谱仪（AIITS），随后通过光学或电子显微镜确认材料的形态信息。然后，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材料的纵横比有关，因此最初的重点将是形成散射光与纤维尺寸（最重要的纵横比）之间的关系，从而考虑更复杂的形态。

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.