Date of Award


Degree Name

Master of Science



First Advisor

Kohli, Punit


In recent years, microlenses (ML), which are micro-scale spheres, have been used to overcome physical diffraction limit of optical microscopy (~200 nm). Although the use of such ML has provided highly resolved images of objects beyond the Abbe optical diffraction limit, the process needs to be refined before it can be applied widespread in materials, biological and clinical research. In this research work, we have implemented experiments on super-resolution imaging utilizing MLs of different refractive indices (n) and diameters to provide the scientific and engineering communities with practical guidelines for obtaining high resolution images with ease. With the support from experimental imaging data as well as FDTD simulations, we have shown that optimal super-resolution imaging with microspheres was accomplished under specific parameter range. We have identified ML with n=1.51 as a preferable choice over those MLs with n=1.4, 1.93, and 2.2, because of high reliability and high magnification for ML with n=1.51. With n=1.51 in mind, we have identified a diameter range from 15 μm to 50 μm provides high resolution and magnification for practical purposes. We show that other ML diameters provided high resolution as well; we believe that ML diameters between 15 μm and 50 μm are practically preferred. We were able to achieve <150 nm resolution and further refinement of this tool can potentially yield higher quality imaging results. Ideally, MLs will eventually be directly incorporated as a modular device in an optical microscope providing the researchers an effective, noninvasive, and economical alternative to complex super resolution microscopy techniques. To improve scanning efficiency, we also proposed microtubule (MT) based imaging. With the demonstration of theoretical optics, we conclude, at present time, that there are some practical concerns for MT-based imaging technique that may limit its application as super-resolution imaging technique. For example, MT-based imaging appears to possess a lower contrast than ML-based technique. Thus, although the concept of MT-based imaging is theoretically possible, we think that more work is needed to utilization of this tool for practical applications.




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