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Chitosan-based nanoparticles are promising materials for potential biomedical applications. We used Flash NanoPrecipitation as a rapid, scalable, single-step method to achieve self-assembly of crosslinked chitosan nanoparticles. Self-assembly was driven by electrostatic interactions, hydrogen bonding, and hydrophobic interactions; tannic acid served to precipitate chitosan to seed nanoparticle formation and crosslink the chitosan to stabilize the resulting particles. The size of the nanoparticles can be tuned by varying formulation parameters including the total solids concentration and block copolymer to core mass ratio. We demonstrated that hydrophobic moieties can be incorporated into the nanoparticle using a lipophilic fluorescent dye as a model system.

Flash NanoPrecipitation (FNP) is a rapid method for encapsulating hydrophobic materials in polymer nanoparticles with high loading capacity. Encapsulating biologics such as proteins remains a challenge due to their low hydrophobicity (logP < 6) and current methods require multiple processing steps. In this work, we report rapid, single-step protein encapsulation via FNP using bovine serum albumin (BSA) as a model protein. Nanoparticle formation involves complexation and precipitation of protein with tannic acid and stabilization with a cationic polyelectrolyte. Nanoparticle self-assembly is driven by hydrogen bonding and electrostatic interactions. Using this approach, high encapsulation efficiency (up to ~80%) of protein can be achieved. The resulting nanoparticles are stable at physiological pH and ionic strength. Overall, FNP is a rapid, efficient platform for encapsulating proteins for various applications.

Silica aerogels are mesoporous high surface area materials with extensive synthetic and processing conditions. To effectively synthesize aerogels, the impact of synthetic pathways on the resulting aerogel properties must be understood prior to experimental investigation. We develop an information architecture, the silica aerogel graph database (10 3 ), and a supervised machine learning neural network regression model to examine these relationships. The property graph database enables rapid queries and visualization of the impact of the synthesis and processing conditions on the final aerogel properties. The model maps from silica aerogel synthetic and processing conditions to predict the aerogel BET surface area with an average error of 109 ± 84 m 2 /g. Following a validation experiment, the model was shown to predict the aerogel surface area from new synthetic and processing conditions with an error of less than 5%. The experiment demonstrates the usefulness of the model in surface area prediction through the compatibility between computational and experimental results. Both in its current form and with further expansion, the developed graph database could reduce experimental dimensionality, time, and resources, enabling the successful synthesis of high surface area silica aerogels, which are advantageous for applications including thermal insulation, sorption media, and catalysis.

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