Broadband Spectral Interferometric Polarized Coherent anti-Stokes Raman Scattering - a non-linear approach to fast all-optical chemical fingerprinting

宽带光谱干涉偏振相干反斯托克斯拉曼散射 - 一种快速全光学化学指纹识别的非线性方法

基本信息

批准号：
2188482
负责人：
金额：
--
依托单位：
King's College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2188482
关键词：
Broadband Spectral Interferometric Polarized Coherent

项目摘要

Development in coherent anti-Stokes Raman spectroscopy (CARS) techniques has expanded greatly in the past two decades with research geared towards faster and more sensitive data acquisition, simplified experimental requirements and robust computational/optical methods to remove the associated non-resonant background (NRB) with CARS. Following on from research conducted by David Richards' group at KCL, this project aims to further expand the capabilities of the broadband CARS technique developed and patented by the group, spectral interferometric polarized CARS (SIPCARS), as well as work on broadband CARS in general. There are two main strands as to which this will be achieved; experimental studies and data analysis of hyperspectral CARS data.The experimental strand will encompass both developments to the SIPCARS system and the usage of complex biological samples to characterise and demonstrate the validity and usefulness of SIPCARS as a means for obtaining rapid and accurate spectral data of biological specimen. The data analysis strand will primarily focus on analysing hyperspectral data obtained from SIPCARS measurements. Typically, analysis of hyperspectral data is carried out using clustering techniques. Essentially, the purpose of this strand in the research programme will be to provide tools to extract information from biological hyperspectral data, thus creating a fully comprehensive system capable of measuring and analysing hyperspectral Raman data of complex biological samples with rapid acquisition rates and a high level of accuracy.

相干反斯托克斯拉曼光谱 (CARS) 技术的发展在过去二十年中得到了极大的发展，研究致力于更快、更灵敏的数据采集、简化的实验要求和强大的计算/光学方法，以消除相关的非共振背景 (NRB) ）与汽车。继 KCL David Richards 团队进行的研究之后，该项目旨在进一步扩展该团队开发并获得专利的宽带 CARS 技术、光谱干涉偏振 CARS (SIPCARS) 的功能，以及宽带 CARS 的总体工作。实现这一目标有两个主要方面：高光谱 CARS 数据的实验研究和数据分析。实验链将包括 SIPCARS 系统的开发以及复杂生物样品的使用，以表征和证明 SIPCARS 作为获取快速、准确的生物光谱数据的手段的有效性和实用性。标本。数据分析部分将主要集中于分析从 SIPCARS 测量获得的高光谱数据。通常，高光谱数据的分析是使用聚类技术进行的。本质上，该研究计划的目的是提供从生物高光谱数据中提取信息的工具，从而创建一个全面的系统，能够以快速采集率和高水平测量和分析复杂生物样品的高光谱拉曼数据。的准确性。