Transmission electron tomography: quality assessment and enhancement for three-dimensional imaging of nanostructures

Nanotechnology has revolutionised humanity's capability in building microscopic systems by manipulating materials on a molecular and atomic scale. Nan-osystems are becoming increasingly smaller and more complex from the chemical perspective which increases the demand for microscopic characterisation techniques. Among others, transmission electron microscopy (TEM) is an indispensable tool that is increasingly used to study the structures of nanosystems down to the molecular and atomic scale. However, despite the effectivity of this tool, it can only provide 2-dimensional projection (shadow) images of the 3D structure, leaving the 3-dimensional information hidden which can lead to incomplete or erroneous characterization. One very promising inspection method is Electron Tomography (ET), which is rapidly becoming an important tool to explore the 3D nano-world. ET provides (sub-)nanometer resolution in all three dimensions of the sample under investigation. However, the fidelity of the ET tomogram that is achieved by current ET reconstruction procedures remains a major challenge. This thesis addresses the assessment and advancement of electron tomographic methods to enable high-fidelity three-dimensional investigations. A quality assessment investigation was conducted to provide a quality quantitative analysis of the main established ET reconstruction algorithms and to study the influence of the experimental conditions on the quality of the reconstructed ET tomogram. Regular shaped nanoparticles were used as a ground-truth for this study. It is concluded that the fidelity of the post-reconstruction quantitative analysis and segmentation is limited, mainly by the fidelity of the reconstructed ET tomogram. This motivates the development of an improved tomographic reconstruction process. In this thesis, a novel ET method was proposed, named dictionary learning electron tomography (DLET). DLET is based on the recent mathematical theorem of compressed sensing (CS) which employs the sparsity of ET tomograms to enable accurate reconstruction from undersampled (S)TEM tilt series. DLET learns the sparsifying transform (dictionary) in an adaptive way and reconstructs the tomogram simultaneously from highly undersampled tilt series. In this method, the sparsity is applied on overlapping image patches favouring local structures. Furthermore, the dictionary is adapted to the specific tomogram instance, thereby favouring better sparsity and consequently higher quality reconstructions. The reconstruction algorithm is based on an alternating procedure that learns the sparsifying dictionary and employs it to remove artifacts and noise in one step, and then restores the tomogram data in the other step. Simulation and real ET experiments of several morphologies are performed with a variety of setups. Reconstruction results validate its efficiency in both noiseless and noisy cases and show that it yields an improved reconstruction quality with fast convergence. The proposed method enables the recovery of high-fidelity information without the need to worry about what sparsifying transform to select or whether the images used strictly follow the pre-conditions of a certain transform (e.g. strictly piecewise constant for Total Variation minimisation). This can also avoid artifacts that can be introduced by specific sparsifying transforms (e.g. the staircase artifacts the may result when using Total Variation minimisation). Moreover, this thesis shows how reliable elementally sensitive tomography using EELS is possible with the aid of both appropriate use of Dual electron energy loss spectroscopy (DualEELS) and the DLET compressed sensing algorithm to make the best use of the limited data volume and signal to noise inherent in core-loss electron energy loss spectroscopy (EELS) from nanoparticles of an industrially important material. Taken together, the results presented in this thesis demonstrates how high-fidelity ET reconstructions can be achieved using a compressed sensing approach.

Corporate governance and cost of capital: evidence from Pakistani listed firms

This thesis investigates the effectiveness of Corporate Governance (CG) reforms in Pakistan. Using a sample of 160 Pakistani firms from 2003 to 2013 and governance data collected manually from the annual reports, this thesis investigates seven closely related and important corporate issues that are related to the compliance of governance rules. Specifically, it aims to : (i) investigate the degree of CG compliance with 2002 Pakistani Code of CG (PCCG); (ii) determine whether the introduction of 2002 PCCG has improved Pakistani CG practices; (iii) investigate the determinants of CG compliance and disclosure for Pakistani listed firms; (iv) test the nexus between CG compliance with the 2002 PCCG and firms’ cost of capital (COC); (v) investigate the impact of different individual CG mechanisms on listed firms COC; (vi) examine how different ownership structures impact on firms’ COC; and (vii) analyse relationship between CG structures and Cost of Equity (COE) as well as Cost of Debt (COD) for Pakistani listed firms. These empirical investigations report some important results. First, the reported findings suggest that Pakistani firms have responded positively to governance disclosure requirements over the eleven year period from 2003 to 2013. The results also show that the introduction of the PCCG in 2002 has improved CG standards by Pakistani listed firms. Second, the reported results related to the determinants of CG compliance demonstrate that significant and positive association between institutional, government and foreign ownership with CG compliance. However, findings relating to the determinants of CG compliance show a negative and significant association between board size and block ownership with CG compliance and disclosure. The study finds no significant relationship between director ownership, audit firm size and the presence of female board members with the constructed Pakistan Corporate Governance Index (PCGI). Third, the investigation on the relationship between CG and COC report a significantly negative nexus between PCGI and firms’ COC. The investigation on the association between ownership structures and COC report a negative and significant nexus between block ownership with firms’ COC. Further, a number of robustness analyses performed in this study suggest that the empirical results reported in this study are generally robust to the alternative CG variables, alternative COC variables and potential endogeneity problems.