Polymer coatings on periodic nanostructures have facilitated advanced applications in various fields. The performance of these structures is intimately linked to their nanoscale characteristics. Smart polymer coatings responsive to environmental stimuli such as temperature, pH level, and ionic strength have found important uses in these applications. Therefore, to optimize their performance and improve their design, precise characterization techniques are essential for understanding the nanoscale properties of polymer coating, especially in response to stimuli and interactions with the surrounding medium. Due to their layered compositions, applying non-destructive measurement methods by X-ray/neutron scattering is optimal. These approaches offer unique insights into the structure, dynamics, and kinetics of polymeric coatings and interfaces. The caveat is that scattering methods require non-trivial data modeling, particularly in the case of periodic structures, which result in strong correlations between scattered beams. The dynamical theory (DT) model offers an exact model for interpreting off-specular signals from periodically structured surfaces and has been validated on substrates measured by neutron scattering. In this dissertation, we improved the model using a computational optimization approach that simultaneously fits specular and off-specular scattering signals and efficiently retrieves the three-dimensional sample profile with high precision. In addition, we extended this to the case of X-ray scattering. We applied this approach to characterize polymer brushes for nanofluidic applications and protein binding to modulated lipid membranes. This approach opens new possibilities in developing soft matter nanostructured substrates with desired properties for various applications. / Doctor of Philosophy / Polymer coatings on nanopatterned surfaces have recently facilitated advanced applications in various fields, particularly biotechnology. For example, multichannel surfaces coated with polymer can serve as nanofluidic devices for precise control of fluid flow in drug screening and detection of specific biomolecules. Moreover, polymer-coated nanopatterned surfaces, which possess similar properties to the extracellular matrix, provide excellent substrates for biological studies. The performance of these systems is closely tied to their nanoscale features, such as the thickness and conformation of the polymer layers. Therefore, high-resolution non-invasive nanoscale characterization techniques are essential for investigating these coatings to optimize their performance and enhance their design. X-ray/neutron scattering offers a non-destructive measurement method with unique capabilities in the nanoscale characterization of polymer coatings. However, scattering methods require non-trivial data modeling, particularly in the case of layered coatings on patterned surfaces. To tackle this challenge, we improved a dynamical theory (DT) model that allows for precise modeling of neutron and X-ray scattering signals from such systems. Using a computational optimization approach, the model enables efficient retrieval of the three-dimensional sample profile with high accuracy. We applied this approach to characterize polymer brushes for nanofluidic applications and protein binding to modulated lipid membranes. This methodology opens up new avenues for developing customizable, nanostructured substrates made from soft materials that possess tailored properties for a wide range of uses.

<p>With the extreme increases in power density of electronic devices, the contact thermal resistance imposed at interfaces between mating solids becomes a major challenge in thermal management. This contact thermal resistance is mainly caused by micro-scale surface asperities (roughness) and wavy profile of surface (nonflatness) which severely reduce the contact area available for heat conduction. High contact pressures (1~100 MPa) can be used to deform the surface asperities to increase contact area. Besides, a variety of conventional thermal interface materials (TIM), such as greases and pastes, are used to improve the contact thermal conductance by filling the remaining air gaps. However, there are still some applications where such TIMs are disallowed for reworkability concerns. For example, heat must be transferred across dry interfaces to a heat sink in pluggable opto-electronic transceivers which needs to repeatedly slide into / out of contact with the heat sink. Dry contact and low contact pressures are required for this sliding application.</p> <p>This dissertation presents a metallized micro-spring array as a surface coating to enhance dry contact thermal conductance under ultra-low interfacial contact pressure. The shape of the micro-springs is designed to be mechanically compliant to achieve conformal contact between nonflat surfaces. The polymer scaffolds of the micro-structured TIMs are fabricated by using a custom projection micro-stereolithography (μSL) system. By applying the projection scheme, this method is more cost-effective and high-throughput than other 3D micro-fabrication methods using a scanning scheme. The thermal conductance of polymer micro-springs is further enhanced by metallization using plating and surface polishing on their top surfaces. The measured mechanical compliance of TIMs indicates that they can deform ~10s μm under ~10s kPa contact pressures over their footprint area, which is large enough to accommodate most of surface nonflatness of electronic packages. The measured thermal resistances of the TIM at different fabrication stages confirms the enhanced thermal conductance by applying metallization and surface polishing. Thermal resistances of the TIMs are compared to direct metal-to-metal contact thermal resistance for flat and nonflat mating surfaces, which confirms that the TIM outperforms direct contact. A thin layer of soft polymer is coated on the top surfaces of the TIMs to accommodate surface roughness that has a smaller spatial period than the micro-springs. For rough surfaces, the polymer-coated TIM has reduced thermal resistance which is comparable to a benchmark case where the top surfaces of the TIM are glued to the mating surface. A polymer base is designed under the micro-spring array which can provide the advantages for handling as a standalone material or integration convenience, at the toll of an increased insertion resistance. Through-holes are designed in the base layer and coated with thermally conductive metal after metallization to enhance thermal conductance of the base layer; a thin layer of epoxy is applied between the base layer and the working surface to reduce contact thermal resistance exposed on the base layer. Cycling tests are conducted on the TIMs; the results show good early-stage reliability of the TIM under normal pressure, sliding contact, and temperature cycles. The TIM is thermally demonstrated on a pluggable application, namely, a CFP4 module, which shows enhanced thermal conductance by applying the TIM. </p> To further enhance the potential mechanical compliance of microstructured surfaces, a stable double curved beam structure with near-zero stiffness composed of intrinsic negative and positive stiffness elastic elements is designed and fabricated by introducing residual stresses. Stiffness measurements shows that the positive-stiffness single curved beam, which is the same as the top beam in the double curved beam, is stiffer than the double curved beam, which confirms the negative stiffness of the bottom beam in the double curved beam. Layered near zero-stiffness materials made of these structures are built to demonstrate the scalability of the zero-stiffness zone.

This study aims to develop coatings which show wear resistant behaviour along with multiple functions such as improved ice adhesion, better freezing delay etc which help in improving the effectiveness of the wind turbine efficiency. The significance of anti-icing/de-icing solutions for wind turbines is emphasized since ice accretion can cause serious issues in generation of power and might lead to damage of blades. The use of active and passive anti-icing/de-icing technologies in wind turbine blade applications is reviewed. The discrepancy between passive anti-icing, which depends on surface treatment, coatings, de-icing fluids and active anti-icing, which uses heating devices, sensors such as actuators, transducers, is explored along with the current challenges in industry. In this study we’ve developed interesting methods for improving the anti-icing/de-icing capabilities of wind turbine blades by using gelcoat coatings in which are filler particles (boron nitride and graphene) and oils (vegetable and paraffin oil) are incorporated. Evaluating the impacts of type of fillers, oils, their concentrations on anti-icing efficacy, as well as the prospects for this technique to enhance wind energy production's reliability and productivity will be explored. In summary, this study aims to develop multi-functional polymer coatings for anti-icing/de-icing application in wind turbine blades. The coatings with boron-nitride and graphene showed an increase in the surface roughness and contact angles, while there’s no change in the chemical composition in comparison with pure gelcoat. The thermal conductivity of the coatings was increased with addition of fillers. For the wear test, the operating parameters chosen are a load of 5N and 1Hz frequency of slider, which is run for a duration of 10 min. The COF for both the coatings is lesser than baseline coatings whereas graphene provided better wear resistance. The hardness was increased for boron-nitride coatings and it remained almost same for graphene coatings. The ice adhesion strength, freezing delay and thermal analysis (TGA) for these coatings showed better performance than pure gelcoat. Whereas for coatings with vegetable and paraffin oils, the contact angles were increased and surface roughness was increased in case of paraffin oil coatings whereas it reduced for vegetable oil coatings. Both the coatings offered better wear resistance and reduced COF, whereas the hardness was reduced. The ice adhesion strength and freezing delay improved drastically and are much better than both pure gelcoat as well as coatings with boron-nitride and graphene. There is slight increase in the glass transition temperature than pure gelcoat coating.

Unravelling details of charge, structure and molecular interactions of functional polymer coatings defines an important analytical challenge that requires the extension of current methodologies. In this article we demonstrate how streaming current measurements interpreted with combined self consistent field (SCF) and soft surface electrokinetic theories allow the evaluation of the segment distribution within poly(ethylene oxide) (PEO) brushes beyond the resolution limits of neutron reflectivity technique.

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities. Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this project focuses on finding machine-readable feature representation techniques most suitable for encoding formulation ingredients. Utilizing two polymer-informatics datasets, one encompassing a large set of 700,000 common homopolymers including epoxies and polyurethanes as coating base materials while the other a relatively small set of 1000 data points of epoxy-diluent formulations, four featurization schemes to represent polymer coating molecules were benchmarked. They include the molecular access system, the extended connectivity fingerprint, molecular graph-based chemical graph network, and graph convolutional network (MG-GCN) embeddings. These representation schemes were used with ensemble models to predict molecular properties including topological surface area and viscosity. The results show that the combination of MG-GCN and ensemble models such as the extreme boosting machine and random forest models achieved the best overall performance, with coefficient of determination (r2) values of 0.74 in topological surface area and 0.84 in viscosity, which compare favorably with existing techniques. These results lay the foundation for using ML with physical modeling to expedite the development of polymer coating formulations.