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Malekzadsani Nobar, Hediyeh UL

Doctoral thesis (2022)

Controlling biomolecule-surface interactions with nano- and micro-engineered surfaces is of great interest in biomedical applications such as tissue regeneration and biosensing platforms. Developing high ... [more ▼]

Controlling biomolecule-surface interactions with nano- and micro-engineered surfaces is of great interest in biomedical applications such as tissue regeneration and biosensing platforms. Developing high-performance functional bio-interfaces for cell-surface or protein-surface interactions necessitates optimizing the interface by modifying material surface variables. Surface gradients are a category of combinatorial technique that enables monitoring and high-throughput optimization of biomolecule-surface interactions by providing a gradually varying surface parameter(s) on a small scale and across an extended area length. It is elaborated that a surface gradient not only greatly reduces the required time and labour of conducting numerous separate experiments for producing several distinct samples but also minimises the inter-batch errors associated with. In this context, multigradients are particularly promising for advanced bio-interface optimisation since they incorporate two or more separate gradients that evolve independently across different directions. While gradients have been vastly studied in past two decades, reporting different surface gradients of chemistry, topography, or mechanical nature in either nano or larger scales, there have been few studies on multigradients, due to the limited operational flexibility required for generating more than one gradient on the surface. First, plasma technologies were explored for establishing a suitable fabricating method for generating spatial variation of surface chemistry along a direction. Both the mask-assisted static and maskless dynamic deposition were examined via two different plasma technologies, namely atmospheric pressure plasma and low-pressure plasma. Depending on the electrical conductivity of the chosen substrates and the nature of the coatings, different surface characterisations were performed on the generated samples. Surface chemistry, surface morphology and wettability properties of the treated surfaces were mainly investigated. As a result, two chemistry gradients were reported; first, an oxygen-functional chemistry gradient deposited with a single-step approach via a programmed corona discharge based on the polymerisation of HMDSO with varying flow rates of oxygen. The chemistry gradient consisted of 7 deposition conditions spanning between mostly organic and inorganic coating also exhibiting the surface energy gradient along a polyethylene foil with length of 10 cm. The surface morphology was also altered as oxygen level was increasing, leading to mild gradual surface roughening. Second, a nitrogen-functional chemistry gradient with the specific feature of enhanced water stability was reported via polymerisation of ethylene with gradually varying ammonia flow rates using a mask-assisted static deposition approach with low pressure capacitively coupled radio frequency plasmas. A smooth coating exhibiting a chemistry gradient consisted of four deposition conditions, and a subsequent surface energy gradient was achieved along 1 cm width of a 2x1cm Si chip. Following that, a versatile experimental setup was presented for developing the next class of surface gradients, the structural or topography gradients, which benefited from a rational design and soft lithography. As a result, a total of 4 topography gradients were reported, two of which were stochastic density gradients and the other two being periodical nanocluster density and periodical size gradients. The gradient was formed based on time-dependent incubation of the functionalised material surface with the chosen precursor and electrostatic interactions between the two. The main experimental inputs were the precursor flow rate, dimension of the experiment chamber and dimension of the substrate. For material surface functionalisation, various classes of chemistries were employed, including aminosilane monolayers, cross-linked plasma polymer, and copolymer templates for developing either stochastic or periodic arrangements of the surface features. The kinetics of incubation of each functional surface was monitored with real-time QCM before gradient formation allowing a prediction of surface coverage and all the generated gradients were investigated for their surface morphology. The obtained micrographs and the respective experimental plots and theoretical fittings confirmed the successful formation of stochastic and periodical topography gradients. Surface-enhanced Raman spectroscopy (SERS) studies revealed the high potential of gold nanocluster density gradients for SERS-based biosensing applications. However, despite exceptionally strong SERS signals recorded on the nanoparticle density gradient (generated on the plasma polymer template), the SERS response diminished at some spots along the surface, revealing a noncontinuous SERS variation. Meanwhile, gold domes did not demonstrate any enhancement as a function of size variation. Wettability analyses were performed selectively on the stochastic gold nanoparticle density gradient utilizing both the experimental sessile drop method and theoretical modelling to investigate the probable wetting regime. The theoretical modelling indicated good agreement with the experimental WCAs and indicated Wenzel, full wetting regime.As the ultimate objective, an orthogonal surface gradient was presented. The approach was based on depositing the previously reported nitrogen-functional chemistry gradient in a perpendicular direction over the unidirectional stochastic gold nanoparticle density gradient. As confirmed by XPS and ToF-SIMS, the surface chemical composition was retained after coating and did not change due to the presence of the underlying conductive gold nanoparticle layer. The surface morphology was significantly altered after being coated with the top plasma layer, demonstrating an overall decreased roughness variation compared to the unidirectional nanoparticle density gradient. Furthermore, the surface wettability variation was significantly lower when compared to the wettability variation scale of the integrated unidirectional gradients. [less ▲]

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