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.

Towards an understanding of Pakistani undergraduates’ current attitudes towards learning and speaking English

The English language has an important place in Pakistan and in its education system, not least because of the global status of English and its role in employment. Realising the need to enhance language learning outcomes, especially at the tertiary level, the Higher Education Commission (HEC) of Pakistan has put in place some important measures to improve the quality of English language teaching practice through its English Language Teaching Reforms (ELTR) project. However, there is a complex linguistic, educational and ethnic diversity in Pakistan and that diversity, alongside the historical and current role of English in the country, makes any language teaching reform particularly challenging. I argue, in this thesis, that reform to date has largely ignored the issues of learner readiness to learn and learner perceptions of the use of English. I argue that studying learner attitudes is important if we are to understand how learners perceive the practice of learning and the use of English in their lives. This study focuses on the attitudes of undergraduate learners of English as a foreign language at two universities in the provinces of Sindh and Balochistan in Pakistan. These provinces have experienced long struggles and movements related to linguistic and ethnic rights and both educate students from all of the districts of their respective provinces. Drawing on debates around linguistic imperialism, economic necessity, and linguistic and educational diversity, I focus on learners’ perceptions about learning and speaking English, asking what their attitudes are towards learning and speaking English with particular reference to socio-psychological factors at a given time and context, including perceived threats to their culture, religion, and mother tongue. I ask how they make choices about learning and speaking English in different domains of language use and question their motivation to learn and speak English. Additionally, I explore issues of anxiety with reference to their use of English. Following a predominantly qualitative mixed methods research approach, the study employs two research tools: an adapted Likert Scale questionnaire completed by 300 students and semi-structured interviews with 20 participants from the two universities. The data were analysed through descriptive statistics and qualitative content analysis, with each set of data synthesised for interpretation. The findings suggest that, compared with the past, the majority of participants hold positive attitudes towards learning and speaking English regardless of their ethnic or linguistic backgrounds. Most of these undergraduate students do not perceive the use of English as a threat to their culture, mother tongue or religious values but, instead, they have a pragmatic and, at the same time, aspirational attitude to the learning and use of English. I present these results and conclude this thesis with reference to ways in which this small-scale study contributes to a better understanding of learner attitudes and perceptions. Acknowledging the limitations of this study, I suggest ways in which the study, enhanced and extended by further research, might have implications for practice, theory and policy in English language teaching and learning in Pakistan.