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<p>The largest obstacle in nanoscale microscopy is the diffraction limit. Although
several means of achieving sub-diffraction resolution exist, they all have shortcomings
such as cost, complexity, and processing time, which make them impractical for widespread
use. Additionally, these technologies struggle to find a balance between a high resolution
and a large field of view. In this introduction of dissertation, we provide an overview
of various microsphere based super resolution techniques that address the shortcomings
of existing platforms and consistently achieve sub-diffraction resolutions. Initially,
the theoretical basis of photonic nanojets, which make microsphere based super resolution
imaging possible, are discussed. In the following sections, different type of acoustofluidic
scanning techniques and intelligent nanoscope are explored. The introduction concludes
with an emphasis on the limitless potential of this technology, and the wide range
of possible biomedical applications.First, we have documented the development of an
acoutofluidic scanning nanoscope that can achieve both high resolution and large field
of view at the same time, which alleviates a long-existing shortcoming of conventional
microscopes. The acoutofluidic scanning nanoscope developed here can serve as either
an add-on component to expand the capability of a conventional microscope, or could
be paired with low-cost imaging platforms to develop a stand-alone microscope for
portable imaging. The acoutofluidic scanning nanoscope achieves high-resolution imaging
without the need for conventional high-cost and bulky objectives with high numerical
apertures. The field of view of the acoutofluidic scanning nanoscope is much larger
than that from a conventional high numerical aperture objective lens, and it is able
to achieve the same resolving power. The acoutofluidic scanning nanoscope automatically
focuses and maintains a constant working distance during the scanning process thanks
to the interaction of the microparticles with the liquid domain. The resolving power
of the acoutofluidic scanning nanoscope can easily be adjusted by using microparticles
of different sizes and refractive indices. Additionally, it may be possible to further
improve the performance of this platform by exploring additional microparticle sizes
and materials, in combination with various objectives. Altogether, we believe this
acoutofluidic scanning nanoscope has potential to be integrated into a wide range
of applications from portable nano-detection to biomedicine and microfluidics.
Next, we developed a dual-camera acoustofluidic nanoscope with a seamless image merging
algorithm (alpha blending process). This design allows us to precisely image both
the sample and the microspheres simultaneously and accurately track the particle path
and location. Therefore, the number of images required to capture the entire field
of view (200 × 200 μm) by using our acoustofluidic scanning nanoscope is reduced by
55-fold compared with previous designs. Moreover, the image quality is also greatly
improved by applying an alpha blending imaging technique, which is critical for accurately
depicting and identifying nanoscale objects or processes. This dual-camera acoustofluidic
nanoscope paves the way for enhanced nanoimaging with high resolution and a large
field of view.
Next, we developed an acoustofluidic scanning nanoscope via fluorescence amplification
technique. Nanoscale fluorescence signal amplification is a significant feature for
many biomedical and cell biology research area. Different types of fluorescence amplification
techniques were studied; however, those technologies still need a complex process
and rely on an elaborate optical system. To conquer these limitations, we developed
an acoustofluidic scanning nanoscope via fluorescence amplification with hard PDMS
membrane technique. The microsphere magnification by photonic nanojets effect with
the hard PDMS could deliver certain focal distance to maximize the amplification.
Moreover, a bidirectional acoustofluidic scanning device with an image processing
also developed to perform 2D scanning of large field of view area. In the image processing
procedure, we applied a correction of lens distortion to provide a restored distortion
image. This fluorescence amplification via acoustofluidic nanoscope allow us to afford
a nanoscale fluorescence imaging.
Next, we developed an intelligent nanoscope that combines machine learning and microsphere
array-based imaging to: (1) surpass the diffraction limit of the microscope objective
with microsphere imaging to provide high-resolution images; (2) provide large field-of-view
imaging without the sacrifice of resolution by utilizing a microsphere array; and
(3) rapidly classify nanomaterials using a deep convolution neural network. The intelligent
nanoscope delivers more than 46 magnified images from a single image frame so that
we collected more than 1,000 images within 2 seconds. Moreover, the intelligent nanoscope
achieves a 95% nanomaterial classification accuracy using 1,000 images of training
sets, which is 45% more accurate than without the microsphere array. The intelligent
nanoscope also achieves a 92% bacteria classification accuracy using 50,000 images
of training sets, which is 35% more accurate than without the microsphere array. This
platform accomplished rapid, accurate detection and classification of nanomaterials
with miniscule size differences. The capabilities of this device wield the potential
to further detect and classify smaller biological nanomaterial, such as viruses or
extracellular vesicles.
Lastly, this chapter serves a conclusion. Here, I discuss current issues regarding
the acoustofluidic scanning nanoscope across review the current limitations of the
technology and give suggestions for different direction of microsphere imaging. Moreover,
I provide my perspective on the future development of acoustofluidic scanning nanoscope
and potential new applications. I discuss how the technologies developed in this dissertation
can be improved and applied to new applications in nanoimaging.
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