Addressing a desired surface wettability has been a significant challenge for a variety of innovative thermofluidic applications from anti-icing coating to two-phase heat exchangers. Surface wettability can be controlled by fabricating nanostructures on the surfaces and modifying their chemistry. While numerous nanofabrication efforts have been investigated for this purpose, a solution immersion method is suggested in this research since it can effectively install nanostructures on two-dimensional or three-dimensional surfaces over a large area. Therefore, we employ the solution immersion method in order to investigate the wettability of copper oxide nanostructures related to fabrication parameters. The examples of copper oxide nanostructures include nanoneedles and nanoflowers/nanograsses on polished copper substrates and open-cell porous copper structures (i.e. copper inverse opals). Detailed analysis is followed to quantify surface structural details, chemical composition, and surface wettability. The understanding of structure- and chemistry-dependent wettability will pave the way for the implementation of copper nanostructures with well-controlled wetting properties into future copper-based thermofluidic applications such as heat exchangers and heat pipes.

The Transporter Classification Database (TCDB) provides literature-curated, functional and evolutionary information on transporters, as well as descriptions of their families and superfamilies across all domains of life. This project aims to test the hypothesis that the Ligand-gated Ion Channel (LIC) family is a member of the Voltage-gated Ion Channels (VIC) superfamily. The topological similarities between the Glutamate-gated Ion Channel (GIC) family, an established member of the VIC superfamily, and the LIC family provided the initial clue of the relationship. To test our hypothesis, multiple bioinformatic approaches were applied to explore the similarities between the LIC family and existing members of the VIC superfamily. These included (1) sequence similarity, (2) compatibility of topology and hydropathy profiles, (3) shared domains, (4) conserved motifs, and (5) similarity of sequence profiles at the family level. Our results showed that two families from the VIC superfamily, the Glutamate-gated Ion Channel (GIC) Family of Neurotransmitter Receptors and the Voltage-gated Proton Channel (VPC) Family, generated reliable signals and satisfied at least five of our six criteria, thus supporting the conclusion that the LIC family is a distant member of the VIC superfamily.

This thesis discusses Data Brushes, an interactive web application to explore neural style transfer using models trained on artistic data visualizations. The application invites casual creators to engage with deep convolutional neural networks to co-create custom artworks with a focus on style transfer networks created from canonical and contemporary works of data visualization and data art to demonstrate the versatility and flexibility of the algorithm. In addition to enabling a novel creative workflow, the process of interactively modifying an image via multiple style transfer networks reveals meaningful features encoded within the networks, and provides insight into the effects particular networks have on different images, or different regions within a single image. To evaluate Data Brushes, we gathered expert feedback from participants of a data science symposium and ran an observational study, finding that our application facilitates the creative exploration of neural style transfer for data art and enhances user intuition regarding the expressive range of style transfer features. This thesis explores both the practical uses of such tools for artists as Data Brushes and the interpretive uses of creating such venues for accessibility to computational art, remixing the purpose of data visualizations to be more than just graphical representations of information.