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.

Chiroptical spectroscopy of biomolecules using chiral plasmonic nanostructures

This thesis explores the potential of chiral plasmonic nanostructures for the ultrasensitive detection of protein structure. These nanostructures support the generation of fields with enhanced chirality relative to circularly polarised light and are an extremely incisive probe of protein structure. In chapter 4 we introduce a nanopatterned Au film (Templated Plasmonic Substrate, TPS) fabricated using a high through-put injection moulding technique which is a viable alternative to expensive lithographically fabricated nanostructures. The optical and chiroptical properties of TPS nanostructures are found to be highly dependent on the coupling between the electric and magnetic modes of the constituent solid and inverse structures. Significantly, refractive index based measurements of strongly coupled TPSs display a similar sensitivity to protein structure as previous lithographic nanostructures. We subsequently endeavour to improve the sensing properties of TPS nanostructures by developing a high through-put nanoscale chemical functionalisation technique. This process involves a chemical protection/deprotection strategy. The protection step generates a self-assembled monolayer (SAM) of a thermally responsive polymer on the TPS surface which inhibits protein binding. The deprotection step exploits the presence of nanolocalised thermal gradients in the water surrounding the TPS upon irradiation with an 8ns pulsed laser to modify the SAM conformation on surfaces with high net chirality. This allows binding of biomaterial in these regions and subsequently enhances the TPS sensitivity levels. In chapter 6 an alternative method for the detection of protein structure using TPS nanostructures is introduced. This technique relies on mediation of the electric/magnetic coupling in the TPS by the adsorbed protein. This phenomenon is probed through both linear reflectance and nonlinear second harmonic generation (SHG) measurements. Detection of protein structure using this method does not require the presence of fields of enhanced chirality whilst it is also sensitive to a larger array of secondary structure motifs than the measurements in chapters 4 and 5. Finally, a preliminary investigation into the detection of mesoscale biological structure is presented. Sensitivity to the mesoscale helical pitch of insulin amyloid fibrils is displayed through the asymmetry in the circular dichroism (CD) of lithographic gammadions of varying thickness upon adsorption of insulin amyloid fibril spherulites and fragmented fibrils. The proposed model for this sensitivity to the helical pitch relies on the vertical height of the nanostructures relative to this structural property as well as the binding orientation of the fibrils.