UNDERSTANDING AND EVALUATING CRYSTAL POLYMORPHISM BY SECOND HARMONIC GENERATION MICROSCOPY

The crystalline form of a solid can profoundly affect its physical and chemical properties, with both potentially stable and metastable crystal polymorphs are accessible during crystal formation. Conventional methods limit the detection of rare nucleation and rapid phase transitioning events due to their lack of selectivity and sensitivity. Inkjet printing of a solution confines the nucleation event in a few micrometer volumes within the droplet, and furthermore rapid desolvation favors the kinetic factor to trap the rare metastable polymorphs. Second harmonic generation microscopy (SHG) possesses enough sensitivity to detect sub-micrometer size chiral crystals selectively and has the potential for use in crystal nucleation studies

Regularized Newton Methods for X-ray Phase Contrast and General Imaging Problems

Like many other advanced imaging methods, x-ray phase contrast imaging and tomography require mathematical inversion of the observed data to obtain real-space information. While an accurate forward model describing the generally nonlinear image formation from a given object to the observations is often available, explicit inversion formulas are typically not known. Moreover, the measured data might be insufficient for stable image reconstruction, in which case it has to be complemented by suitable a priori information. In this work, regularized Newton methods are presented as a general framework for the solution of such ill-posed nonlinear imaging problems. For a proof of principle, the approach is applied to x-ray phase contrast imaging in the near-field propagation regime. Simultaneous recovery of the phase- and amplitude from a single near-field diffraction pattern without homogeneity constraints is demonstrated for the first time. The presented methods further permit all-at-once phase contrast tomography, i.e. simultaneous phase retrieval and tomographic inversion. We demonstrate the potential of this approach by three-dimensional imaging of a colloidal crystal at 95 nm isotropic resolution.Comment: (C)2016 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibite

Filter-Based Probabilistic Markov Random Field Image Priors: Learning, Evaluation, and Image Analysis

Markov random fields (MRF) based on linear filter responses are one of the most popular forms for modeling image priors due to their rigorous probabilistic interpretations and versatility in various applications. In this dissertation, we propose an application-independent method to quantitatively evaluate MRF image priors using model samples. To this end, we developed an efficient auxiliary-variable Gibbs samplers for a general class of MRFs with flexible potentials. We found that the popular pairwise and high-order MRF priors capture image statistics quite roughly and exhibit poor generative properties. We further developed new learning strategies and obtained high-order MRFs that well capture the statistics of the inbuilt features, thus being real maximum-entropy models, and other important statistical properties of natural images, outlining the capabilities of MRFs. We suggest a multi-modal extension of MRF potentials which not only allows to train more expressive priors, but also helps to reveal more insights of MRF variants, based on which we are able to train compact, fully-convolutional restricted Boltzmann machines (RBM) that can model visual repetitive textures even better than more complex and deep models. The learned high-order MRFs allow us to develop new methods for various real-world image analysis problems. For denoising of natural images and deconvolution of microscopy images, the MRF priors are employed in a pure generative setting. We propose efficient sampling-based methods to infer Bayesian minimum mean squared error (MMSE) estimates, which substantially outperform maximum a-posteriori (MAP) estimates and can compete with state-of-the-art discriminative methods. For non-rigid registration of live cell nuclei in time-lapse microscopy images, we propose a global optical flow-based method. The statistics of noise in fluorescence microscopy images are studied to derive an adaptive weighting scheme for increasing model robustness. High-order MRFs are also employed to train image filters for extracting important features of cell nuclei and the deformation of nuclei are then estimated in the learned feature spaces. The developed method outperforms previous approaches in terms of both registration accuracy and computational efficiency

Doctor of Philosophy

dissertationThe importance of lipid bilayers to the structure and function of cellular membranes coupled with their inherent complexity has driven the development of analytical techniques capable of high-throughput investigation of these surfaces. This work describes a continuous flow microspotter (CFM) that was modified to create micropatterned lipid bilayer arrays (MLBAs). This dissertation is divided into four main parts, with the first chapter focusing on the characterization of MLBAs using fluorescence microscopy to ensure bilayer formation and integrity. The individually addressable nature of the CFM was also demonstrated using a multi-ligand array containing ganglioside GM1, dinitrophenyl (DNP) and biotin. A multiple protein-ligand assay was performed using the ligand array to detect three different fluorescently labeled proteins (cholera toxin b (CTb), anti-DNP antibody and NeutrAvidin) from solution simultaneously. The second part of this dissertation concentrates on creating stable MLBAs using a polymerizable lipid, poly(bis-SorbPC) in order to generate a more robust biosensing platform. The poly(lipid) arrays were compared directly to the MLBAs prepared without the polymerizable lipids using fluorescence microscopy to demonstrate their superior stability. A multiple protein-ligand assay was also performed to demonstrate the utility of these arrays and their potential application as a sensor substrate. iv Next, the MLBAs were used to investigate the impact of fifteen different lipid components on small molecule-membrane binding. The lipophilic dye merocyanine 540 (MC540) was used as a model small molecule and its binding was monitored by fluorescence microscopy. These studies demonstrate the potential of using MLBAs to investigate drug membrane interactions while preserving time and cost-effectiveness. Finally, sum-frequency vibrational imaging (SFVI) was developed to provide a surface specific noninvasive, analytical technique capable of monitoring lipid structure and dynamics in a high-throughput manner. The vibrational sensitivity of SFVI was investigated with an asymmetric lipid bilayer patterned by ultraviolet (UV) radiation lithographically. The phase behavior of three different binary mixtures in a MLBA was successfully investigated using SFVI. The SFVI setup had the sensitivity, resolution and field of view required for exploring lipid bilayer properties in an array format. This dissertation presents a new approach for assembling lipid bilayer arrays in combination with a powerful analytical technique to allow exploration of the physical properties of lipid membranes in a high-throughput and noninvasive manner

Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data

Identifying parameters of computational models from experimental data, or model calibration, is fundamental for assessing and improving the predictability and reliability of computer simulations. In this work, we propose a method for Bayesian calibration of models that predict morphological patterns of diblock copolymer (Di-BCP) thin film self-assembly while accounting for various sources of uncertainties in pattern formation and data acquisition. This method extracts the azimuthally-averaged power spectrum (AAPS) of the top-down microscopy characterization of Di-BCP thin film patterns as summary statistics for Bayesian inference of model parameters via the pseudo-marginal method. We derive the analytical and approximate form of a conditional likelihood for the AAPS of image data. We demonstrate that AAPS-based image data reduction retains the mutual information, particularly on important length scales, between image data and model parameters while being relatively agnostic to the aleatoric uncertainties associated with the random long-range disorder of Di-BCP patterns. Additionally, we propose a phase-informed prior distribution for Bayesian model calibration. Furthermore, reducing image data to AAPS enables us to efficiently build surrogate models to accelerate the proposed Bayesian model calibration procedure. We present the formulation and training of two multi-layer perceptrons for approximating the parameter-to-spectrum map, which enables fast integrated likelihood evaluations. We validate the proposed Bayesian model calibration method through numerical examples, for which the neural network surrogate delivers a fivefold reduction of the number of model simulations performed for a single calibration task

Engineering active sites on reduced graphene oxide by hydrogen plasma irradiation: mimicking bifunctional metal/supported catalysts in hydrogenation reactions

[EN] H2 plasma has been used to generate carbon vacancies on reduced graphene oxide to increase its catalytic activity as a hydrogenation catalyst. A relationship between the power of the plasma treatment and the exposure time with the activity of the material was observed for CvC double bond hydrogenation. The activity data in the case of 1-octene, showing skeletal isomerization besides hydrogenation, indicate that H2 plasma treatment can introduce hydrogenating and acid sites rendering a bifunctional catalyst that is reminiscent of the activity of noble metals supported on acid supports.Financial support from the Spanish Ministry of Economy and Competitiveness (Severo Ochoa, CTQ2015-69563-CO2-R1 and Grapas) is gratefully acknowledged. AP thanks the Ministry for a Ramon y Cajal research associate contract. AFG thanks the Center of Supercomputing of Galicia (CESGA) for the computational facilities. MM acknowledges financial support from the PN 16 47 01 04 project. 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Anisotropic conjugated polymer chain conformation tailors the energy migration in nanofibers

Conjugated polymers are complex multi-chromophore systems, with emission properties strongly dependent on the electronic energy transfer through active sub-units. Although the packing of the conjugated chains in the solid state is known to be a key factor to tailor the electronic energy transfer and the resulting optical properties, most of the current solution-based processing methods do not allow for effectively controlling the molecular order, thus making the full unveiling of energy transfer mechanisms very complex. Here we report on conjugated polymer fibers with tailored internal molecular order, leading to a significant enhancement of the emission quantum yield. Steady state and femtosecond time-resolved polarized spectroscopies evidence that excitation is directed toward those chromophores oriented along the fiber axis, on a typical timescale of picoseconds. These aligned and more extended chromophores, resulting from the high stretching rate and electric field applied during the fiber spinning process, lead to improved emission properties. Conjugated polymer fibers are relevant to develop optoelectronic plastic devices with enhanced and anisotropic properties.Comment: 43 pages, 15 figures, 1 table in Journal of the American Chemical Society, (2016