Fundamentals and applications of ion tracks and nanopores in solid state membranes
Creators: name: Dutt, Shankar
Publisher: name: The Australian National University
Year: 2024

description: Solid-state nanopore membranes have emerged as pivotal tools in a wide array of technological domains, including the analysis of biomolecules, advanced filtration, osmotic energy generation, and environmental monitoring. This thesis reports on the fabrication and characterisation of solid-state nanopore membranes, as well as their application for single-molecule sensing. The focus is on two classes of membrane materials: polymer foils and silicon-based membranes. The nanopore formation in these foils/windows was achieved through the ion-track etching and the controlled breakdown (CBD) techniques. In the ion-track etching method, membranes were irradiated with swift heavy ions. At these energies, the ions generate long, narrow columnar defect regions along their trajectory, called 'ion tracks'. These ion tracks are often highly susceptible to chemical etching, which can be used to fabricate nanopores with a high aspect ratio. The CBD method fabricates nanopores by applying an electric field across a thin dielectric membrane, which causes localised breakdown and formation of nanopores with precise control over their size. A detailed investigation of ion tracks in polypropylene foils was conducted, particularly how their structure changes with varying antioxidant concentrations. Notably, foils with high antioxidant content show a cylindrical track structure with a highly damaged core with significant mass loss and a gradual transition to the undamaged material while a core-shell structure and increase in foil mass over time was observed in polypropylene foils with lower antioxidant content. The increased mass, located in the shell region is attributed to an oxidation chain reaction, which occurs as primary radicals capture oxygen in the absence of hindered phenols, which typically act as antioxidants. Knowledge of the track structure is important for controlled nanopore fabrication. This thesis then delves into elucidating the detailed structure of nanopores in polycarbonate membranes, a challenge that has persisted for over four decades. Using small-angle X-ray scattering with a new form factor model, the shape of the nanopores was found to be tapered towards the surface of the foils while the center of the nanopores is cylindrical in nature. It was found that the size distribution of the pores increases with increasing ion fluence, and the pore dimensions slightly decrease when the fluence exceeds a certain value. The latter effect is attributed to a halo around the track core which exhibits crosslinking of polymer chains. The research then transitions to silicon-based materials, studying the annealing of ion tracks in amorphous silicon dioxide and their impact on the etching of the tracks. The annealing kinetics of the ion tracks were explored, examining the effects of the ambient atmosphere, annealing temperature, and duration. It was discovered that the ion tracks in amorphous silicon dioxide anneal preferentially near the surface. A combination of stress relaxation and oxygen diffusion was suggested as the driving mechanism for this behavior. Furthermore, an etching model was developed that can accurately predict the shape and size of the resulting nanopores. Subsequently, the thesis delves into the development of ultra-thin nanopore membranes, employing controlled dielectric breakdown to create single nanopores. The study further ventured into single biomolecule sensing using these nanopores. Membranes with different stoichiometry of silicon nitride were studied and found to have different surface charges, which is important for biomolecule translocation. Finally, the thesis reports on the integration of solid-state nanopore sensing with machine learning for the label-free identification of proteins. High F-values and specificity were achieved for combinations of four proteins similar in size and weight through the combined use of high bandwidth instruments, advanced clustering and machine learning methods.