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Melting gels are a class of hybrid organic-inorganic silica based gels prepared via the sol-gel process that are solid below their glass transition temperatures, near room temperature, but show thermoplastic behavior when heated. While this phase change can be repeated multiple times, heating the gel past its consolidation temperatures, typically above 130 oC initiates an irreversible reaction that produces highly crosslinked glassy organic-inorganic materials via hydrolysis and poly-condensation. This ability makes melting gels uniquely compatible with processing techniques inaccessible to other sol-gels. By properly tuning their properties, it should be possible to create protective coatings for electronics and anti-corrosive coatings for metals that are highly hydrophobic and insulating. However, melting gel consolidation reactions are highly dependent on charge interactions, raising the question of how these materials will respond to a processing technique, like electrospray deposition (ESD), which is dependent on charge delivery. In this study, we focus on the role that substrate temperature and charge polarity play on film morphology, consolidation chemistry, and surface properties. Optical images, film thickness measurements, nanoindentation, and FTIR were used to characterize the sprayed melting gel with the goal of developing a robust processing space for producing highly cross linked, hydrophobic, dielectric coatings. 

Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that covariate overlap is more difficult to satisfy in this setting. In this paper, we explore the implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument, suggesting that these assumptions are stronger than investigators likely realize. Our key innovation is to explore how strict overlap restricts global discrepancies between the covariate distributions in the treated and control populations. Exploiting results from information theory, we derive explicit bounds on the average imbalance in covariate means under strict overlap and show that these bounds become more restrictive as the dimension grows large. We discuss how these implications interact with assumptions and procedures commonly deployed in observational causal inference, including sparsity and trimming.