Advanced wide-field interferometric microscopy for nanoparticle sensing and characterization

Nanoparticles have a key role in today's biotechnological research owing to the rapid advancement of nanotechnology. While metallic, polymer, and semiconductor based artificial nanoparticles are widely used as labels or targeted drug delivery agents, labeled and label-free detection of natural nanoparticles promise new ways for viral diagnostics and therapeutic applications. The increasing impact of nanoparticles in bio- and nano-technology necessitates the development of advanced tools for their accurate detection and characterization. Optical microscopy techniques have been an essential part of research for visualizing micron-scale particles. However, when it comes to the visualization of individual nano-scale particles, they have shown inadequate success due to the resolution and visibility limitations. Interferometric microscopy techniques have gained significant attention for providing means to overcome the nanoparticle visibility issue that is often the limiting factor in the imaging techniques based solely on the scattered light. In this dissertation, we develop a rigorous physical model to simulate the single nanoparticle optical response in a common-path wide-field interferometric microscopy (WIM) system. While the fundamental elements of the model can be used to analyze nanoparticle response in any generic wide-field imaging systems, we focus on imaging with a layered substrate (common-path interferometer) where specular reflection of illumination provides the reference light for interferometry. A robust physical model is quintessential in realizing the full potential of an optical system, and throughout this dissertation, we make use of it to benchmark our experimental findings, investigate the utility of various optical configurations, reconstruct weakly scattering nanoparticle images, as well as to characterize and discriminate interferometric nanoparticle responses. This study investigates the integration of advanced optical schemes in WIM with two main goals in mind: (i) increasing the visibility of low-index nanoscale particles via pupil function engineering, pushing the limit of sensitivity; (ii) improving the resolution of sub-diffraction-limited, low-index particle images in WIM via reconstruction strategies for shape and orientation information. We successfully demonstrate an overall ten-fold improvement in the visibility of the low-index sub-wavelength nanoparticles as well as up to two-fold extended spatial resolution of the interference-enhanced nanoparticle images. We also systematically examine the key factors that determine the signal in WIM. These factors include the particle type, size, layered substrate design, defocus and nanoparticle polarizability. We use the physical model to demonstrate how these factors determine the signal levels, and demonstrate how the layered substrate can be designed to optimize the overall signal, while defocus scan can be used to maximize it, as well as its signature can be utilized for particle discrimination purposes for both dielectric particles and resonant metallic particles. We introduce a machine learning based particle characterization algorithm that relies on supervised learning from model. The particle characterization is limited to discrimination based on nanosphere size and type in the scope of this dissertation

Ultrafast quantum coherent control apparatus

Department Head: Anthony A. Maciejewski.2007 Fall.Includes bibliographical references (pages 90-92).In recent years, the availability of ultrafast laser sources has opened up a number of opportunities for exploring molecular dynamics that take place on femtosecond time scales. Coherent control experiments involve creating, manipulating, and measuring these ultrafast phenomena. Such controllable processes include second harmonic generation (SHG), creation of vibrational wavepackets, high-harmonic generation, photodissociation, and more.The foundation to all these experiments is an ultrafast pulse shaper and a high-dimensional search algorithm. Here we present the design and construction of a spectral phase-only pulse shaper, including details on alignment and calibration. We also demonstrate the functionality of the device by producing several pulse profiles that could be potentially useful in coherent control experiments. A covariance matrix analysis evolutionary strategy (CMAES) is also implemented, and demonstrated to optimize SHG in a nonlinear crystal. Finally, recognizing that phase-only shapers cannot produce the full range of temporal shapes available to a given input pulse, we show the design and construction of a pulse shaper which uses only a single linear phase mask to gain control over both spectral phase and amplitude by use of phase gratings

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Photonics in Nature: From Order to Disorder

The most vibrant and striking colours in living organisms are often caused by a combination of pigments and nano-scale transparent architectures, which interact with light to produce so-called structural colours. These colours are the result of light interfering with the nanoscale structures that are present in the materials. Such colour-producing structures are not perfect, and irregularities in the arrangements (disorder) are present in many organisms. However, disorder in natural structures is not detrimental but functional, as it allows a broader range of optical effects. This chapter reviews and attempts to classify structurally coloured organisms, highlighting the influence that disorder has on their visual appearance. It also showcases how photonic systems, such as the blue Morpho butterfly and the white Cyphochilus beetle, are capable of obtaining optical properties (long-distance visibility and whiteness, respectively) where disorder seems to be highly optimized, indicating that disorder is important for obtaining complex visual effects in natural systems. The chapter first introduces the mathematical concepts required for analysing disordered systems, such as the Fourier transform and the structure factor. Then, ordered and disordered natural photonic systems are reviewed. This is followed by examples of completely disordered structures responsible of white appearances. Finally, we review the possibilities of hierarchical organisation and pixelated surfaces to widen the range of optical appearances

An Observational Perspective of Transitional Disks

Transitional disks are objects whose inner disk regions have undergone substantial clearing. The Spitzer Space Telescope produced detailed spectral energy distributions (SEDs) of transitional disks that allowed us to infer their radial dust disk structure in some detail, revealing the diversity of this class of disks. The growing sample of transitional disks also opened up the possibility of demographic studies, which provided unique insights. There now exist (sub)millimeter and infrared images that confirm the presence of large clearings of dust in transitional disks. In addition, protoplanet candidates have been detected within some of these clearings. Transitional disks are thought to be a strong link to planet formation around young stars and are a key area to study if further progress is to be made on understanding the initial stages of planet formation. Here we provide a review and synthesis of transitional disk observations to date with the aim of providing timely direction to the field, which is about to undergo its next burst of growth as ALMA reaches its full potential. We discuss what we have learned about transitional disks from SEDs, color-color diagrams, and imaging in the (sub)mm and infrared. We then distill the observations into constraints for the main disk clearing mechanisms proposed to date (i.e., photoevaporation, grain growth, and companions) and explore how the expected observational signatures from these mechanisms, particularly planet-induced disk clearing, compare to actual observations. Lastly, we discuss future avenues of inquiry to be pursued with ALMA, JWST, and next generation of ground-based telescopes.Comment: 24 pages, 13 figures, Accepted for publication as a chapter in Protostars and Planets VI, University of Arizona Press (2014), eds. H. Beuther, R. Klessen, C. Dullemond, Th. Hennin

Investigating the Swimming of Microbial Pathogens Using Digital Holography.

To understand much of the behaviour of microbial pathogens, it is necessary to image living cells, their interactions with each other and with host cells. Species such as Escherichia coli are difficult subjects to image: they are typically microscopic, colourless and transparent. Traditional cell visualisation techniques such as fluorescent tagging or phase-contrast microscopy give excellent information on cell behaviour in two dimensions, but no information about cells moving in three dimensions. We review the use of digital holographic microscopy for three-dimensional imaging at high speeds, and demonstrate its use for capturing the shape and swimming behaviour of three important model pathogens: E. coli, Plasmodium spp. and Leishmania spp

From Nonlinear Optimization to Convex Optimization through Firefly Algorithm and Indirect Approach with Applications to CAD/CAM

ABSTRACT. Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail.This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor’s method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently