Molecular tectonics : supramolecular 2D nanopatterning of surfaces by self-assembly

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Contrôle de l'organisation moléculaire en 2D et 3D par l’utilisation de liaisons hydrogène, de coordination métallique et d'autres interactions

La stratégie de la tectonique moléculaire a montré durant ces dernières années son utilité dans la construction de nouveaux matériaux. Elle repose sur l’auto-assemblage spontané de molécule dite intelligente appelée tecton. Ces molécules possèdent l’habilité de se reconnaitre entre elles en utilisant diverses interactions intermoléculaires. L'assemblage résultant peut donner lieu à des matériaux moléculaires avec une organisation prévisible. Cette stratégie exige la création de nouveaux tectons, qui sont parfois difficiles à synthétiser et nécessitent dans la plupart des cas de nombreuses étapes de synthèse, ce qui empêche ou limite leur mise en application pratique. De plus, une fois formées, les liaisons unissant le corps central du tecton avec ces groupements de reconnaissance moléculaire ne peuvent plus être rompues, ce qui ne permet pas de remodeler le tecton par une procédure synthétique simple. Afin de contourner ces obstacles, nous proposons d’utiliser une stratégie hybride qui se sert de la coordination métallique pour construire le corps central du tecton, combinée avec l'utilisation des interactions plus faibles pour contrôler l'association. Nous appelons une telle entité métallotecton du fait de la présence du métal. Pour explorer cette stratégie, nous avons construit une série de ligands ditopiques comportant soit une pyridine, une bipyridine ou une phénantroline pour favoriser la coordination métallique, substitués avec des groupements diaminotriazinyles (DAT) pour permettre aux complexes de s'associer par la formation de ponts hydrogène. En plus de la possibilité de créer des métallotectons par coordination, ces ligands ditopiques ont un intérêt intrinsèque en chimie supramoléculaire en tant qu'entités pouvant s'associer en 3D et en 2D. En parallèle à notre étude de la chimie de coordination, nous avons ii examiné l'association des ligands, ainsi que celle des analogues, par la diffraction des rayons-X (XRD) et par la microscopie de balayage à effet tunnel (STM). L'adsorption de ces molécules sur la surface de graphite à l’interface liquide-solide donne lieu à la formation de différents réseaux 2D par un phénomène de nanopatterning. Pour comprendre les détails de l'adsorption moléculaire, nous avons systématiquement comparé l’organisation observée en 2D par STM avec celle favorisée dans les structures 3D déterminées par XRD. Nous avons également simulé l'adsorption par des calculs théoriques. Cette approche intégrée est indispensable pour bien caractériser l’organisation moléculaire en 2D et pour bien comprendre l'origine des préférences observées. Ces études des ligands eux-mêmes pourront donc servir de référence lorsque nous étudierons l'association des métallotectons dérivés des ligands par coordination. Notre travail a démontré que la stratégie combinant la chimie de coordination et la reconnaissance moléculaire est une méthode de construction rapide et efficace pour créer des réseaux supramoléculaires. Nous avons vérifié que la stratégie de la tectonique moléculaire est également efficace pour diriger l'organisation en 3D et en 2D, qui montre souvent une homologie importante. Nous avons trouvé que nos ligands hétérocycliques ont une aptitude inattendue à s’adsorber fortement sur la surface de graphite, créant ainsi des réseaux organisés à l'échelle du nanomètre. L’ensemble de ces résultats promet d’offrir des applications dans plusieurs domaines, dont la catalyse hétérogène et la nanotechnologie. Mots clés : tectonique moléculaire, interactions intermoléculaires, stratégie hybride, coordination métallique, diffraction des rayons-X, microscopie de balayage à effet tunnel, graphite, phénomène de nanopatterning, calculs théoriques, ponts hydrogène, chimie supramoléculaire, ligands hétérocycliques, groupements DAT, catalyse hétérogène, nanotechnologie.

Nanopatterning and electrical tuning of MoS2 layers with a subnanometer helium ion beam

We report subnanometer modification enabled by an ultrafine helium ion beam. By adjusting ion dose and the beam profile, structural defects were controllably introduced in a few-layer molybdenum disulfide (MoS2) sample and its stoichiometry was modified by preferential sputtering of sulfur at a few-nanometer scale. Localized tuning of the resistivity of MoS2 was demonstrated and semiconducting, metallic-like, or insulating material was obtained by irradiation with different doses of He(+). Amorphous MoSx with metallic behavior has been demonstrated for the first time. Fabrication of MoS2 nanostructures with 7 nm dimensions and pristine crystal structure was also achieved. The damage at the edges of these nanostructures was typically confined to within 1 nm. Nanoribbons with widths as small as 1 nm were reproducibly fabricated. This nanoscale modification technique is a generalized approach that can be applied to various two-dimensional (2D) materials to produce a new range of 2D metamaterials.

2D-3D Face Recognition Based on PCA and Feature Modelling

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.

A bridging transition technique for the combination of meshfree method with finite element method in 2D solids and structures

For certain continuum problems, it is desirable and beneficial to combine two different methods together in order to exploit their advantages while evading their disadvantages. In this paper, a bridging transition algorithm is developed for the combination of the meshfree method (MM) with the finite element method (FEM). In this coupled method, the meshfree method is used in the sub-domain where the MM is required to obtain high accuracy, and the finite element method is employed in other sub-domains where FEM is required to improve the computational efficiency. The MM domain and the FEM domain are connected by a transition (bridging) region. A modified variational formulation and the Lagrange multiplier method are used to ensure the compatibility of displacements and their gradients. To improve the computational efficiency and reduce the meshing cost in the transition region, regularly distributed transition particles, which are independent of either the meshfree nodes or the FE nodes, can be inserted into the transition region. The newly developed coupled method is applied to the stress analysis of 2D solids and structures in order to investigate its’ performance and study parameters. Numerical results show that the present coupled method is convergent, accurate and stable. The coupled method has a promising potential for practical applications, because it can take advantages of both the meshfree method and FEM when overcome their shortcomings.

Generation of a 3D proximal femur shape from a single projection 2D radiographic image.

Summary Generalized Procrustes analysis and thin plate splines were employed to create an average 3D shape template of the proximal femur that was warped to the size and shape of a single 2D radiographic image of a subject. Mean absolute depth errors are comparable with previous approaches utilising multiple 2D input projections. Introduction Several approaches have been adopted to derive volumetric density (g cm-3) from a conventional 2D representation of areal bone mineral density (BMD, g cm-2). Such approaches have generally aimed at deriving an average depth across the areal projection rather than creating a formal 3D shape of the bone. Methods Generalized Procrustes analysis and thin plate splines were employed to create an average 3D shape template of the proximal femur that was subsequently warped to suit the size and shape of a single 2D radiographic image of a subject. CT scans of excised human femora, 18 and 24 scanned at pixel resolutions of 1.08 mm and 0.674 mm, respectively, were equally split into training (created 3D shape template) and test cohorts. Results The mean absolute depth errors of 3.4 mm and 1.73 mm, respectively, for the two CT pixel sizes are comparable with previous approaches based upon multiple 2D input projections. Conclusions This technique has the potential to derive volumetric density from BMD and to facilitate 3D finite element analysis for prediction of the mechanical integrity of the proximal femur. It may further be applied to other anatomical bone sites such as the distal radius and lumbar spine.

Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour

This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.

Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images

In the analysis of medical images for computer-aided diagnosis and therapy, segmentation is often required as a preliminary step. Medical image segmentation is a complex and challenging task due to the complex nature of the images. The brain has a particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues in order to prescribe appropriate therapy. Magnetic Resonance Imaging is an important diagnostic imaging technique utilized for early detection of abnormal changes in tissues and organs. It possesses good contrast resolution for different tissues and is, thus, preferred over Computerized Tomography for brain study. Therefore, the majority of research in medical image segmentation concerns MR images. As the core juncture of this research a set of MR images have been segmented using standard image segmentation techniques to isolate a brain tumor from the other regions of the brain. Subsequently the resultant images from the different segmentation techniques were compared with each other and analyzed by professional radiologists to find the segmentation technique which is the most accurate. Experimental results show that the Otsu’s thresholding method is the most suitable image segmentation method to segment a brain tumor from a Magnetic Resonance Image.