Stimulated Raman Scattering (SRS) microspectroscopy is a powerful imaging technique that uses the inherent chemical composition of specimens to produce highly detailed images. In recent years, SRS microscopy has gained popularity and has been used in various biomedical, material, and environmental science applications. One of the reasons of its success is that, unlike the coherent anti-Stokes Raman scattering (CARS), the SRS process is not affected by the so-called non-resonant background, providing precise and easy-to-interpret hyperspectral measurements that are directly comparable with spontaneous Raman. However, SRS microscopy is not an entirely background-free technique. In fact, there are other competing optical phenomena whose signals spectrally overlap with SRS, potentially reducing the contrast and sensitivity achievable with this technique.
Although recent technical implementations capable of effectively reduce the background in SRS microspectroscopy have been presented, the widespread adoption of this technique is still limited by the complexity and lack of automation of existing systems, which require manual operation and technical expertise to be operated. This thesis introduces a novel scheme for a flexible and user-friendly system capable to perform automated background-free SRS acquisitions across the entire fingerprint to CH-stretch vibrational interval, broadening the range of applications in which SRS microspectroscopy can be applied.
I received my M.Sc. in Medical Physics from the University of Pisa in 2018. I then joined the Vibrational Imaging Lab at KAUST, where I am currently doing a Ph.D. in Bioengineering under the mentorship of Prof. Carlo Liberale. Throughout my Ph.D. I have developed stimulated Raman scattering microspectroscopy systems and used them for different biological and environmental science applications