Previous research has determined that nanoparticle systems require a wide parameter space to effectively conduct synthesis and characterization. As a result, the development of high-throughput techniques is essential for efficiently analyzing the large datasets produced in colloidal particle experiments. These methods enable the rapid assessment of particle properties, such as size, shape, and charge, which are critical for modifying nanoparticles for specific applications. In order to do this, advancements in automated synthesis platforms, such as the Jubilee automated multi-tool system, offer the potential to streamline the fabrication of magic sized clusters. This approach has the potential to accelerate the discovery of novel nanoparticles but also allows for real-time adjustment of synthesis parameters to achieve desired properties with high precision, throughput, and reproducibility. As a result of the optimized synthesis process, characterization using techniques such as small angle X-ray scattering (SAXS) and UV-vis spectroscopy can be done at an accelerated rate. Efforts to enhance the durability and performance of the Jubilee automated multi-tool platform are focused on integrating advanced materials to improve system lifespan. This work will incorporate glass syringes and resin-printed components which offer improved chemical resistance and precision compared to traditional plastic components, extending the utility of the platform to be able to work with solvents and chemicals that are corrosive, volatile, or strong solvating agents for typical plastics. These improvements aim to reduce wear and tear, extend the lifespan of critical components, and ultimately ensure the platform's reliability for long-term use in high-throughput nanoparticle synthesis.

Silica nanoparticles have diverse applications in catalysis, imaging, and drug delivery. Tailoring these nanoparticles for specific applications requires precise control over their size, surface chemistry, porosity, and polydispersity. These properties are controlled by a wide range of factors such as reactant type and concentration, pH, reaction temperature, and other synthesis parameters. Due to the large parameter space, determining the optimal reaction conditions for synthesizing silica nanoparticles with the desired size and morphology is time-consuming and challenging. An accelerated experimentation platform integrating automation and artificial intelligence can streamline the selection of reaction parameters for synthesizing silica nanoparticles with targeted size and morphology using machine learning-based iterative design of experiments to optimize material properties. This system uses the Science Jubilee flexible laboratory automation platform to carry out sol-gel synthesis. Small-angle X-ray scattering is used to characterize the sample. The data collected is used to optimize the reaction condition for synthesizing the targeted nanoparticle. We have successfully carried out sol-gel processes and synthesized silica nanoparticles with various sizes and polydispersity using the platform. Currently, we are working on optimizing the selection of sample synthesis conditions.

Gold nanoparticles (AuNPs) have unique optical and physical properties that have a range of applications in photovoltaics and medicine. The properties of AuNPs can be adjusted depending on their intended use, which is accomplished by synthesizing AuNPs of a specific size, shape, and surface chemistry. Optimizing AuNP structure is currently performed through a time-consuming approach. In experimental synthesis a multitude of parameters can affect the AuNP structure, including temperature, reagent concentrations, time delays of component addition, and the use of selective passivation molecules during synthesis. In order to achieve robotic control over the large design space, a computational method called phase-mapping can be utilized. These algorithms correlate the different synthesis design variables to the AuNP structure measured using characterization, and from that information the algorithm can provide synthesis parameters to create a desired AuNP structure. In this poster, an experimental case study of creating phasemaps of peptide-based AuNP synthesis by varying temperatures and the ratio of peptides in the growth solution will be presented. To produce enough experimental data to create an accurate phase-mapping algorithm, the synthesis process will be automated using an Opentrons OT-2 liquid handling robot, with an attached thermal module to control the synthesis temperature. After synthesizing the AuNPs, their structure will be characterized using UV-Vis spectroscopy. The structure, alongside the design parameters, will be used to update the phase-mapping algorithm, from which new design parameters will be obtained and synthesized in order to validate if the produced structure matches the algorithm’s prediction. The phasemaps generated will be used to understand the design rules for controlling the colloidal AuNP growth and further guide the bio-inspired synthesis of colloidal nanoparticles.

Future technological developments in fields including alternative energy and medicine require next-generation materials. Synthesizing each new material requires exploring a multi-dimensional parameter space. Developing laboratory automation tools for automating lab procedures and data analysis will be key to efficient discovery of optimal, novel materials. Some automation tools utilized in this work include automated sample loading and analysis for both Small Angle X-ray Scattering (SAXS) and Dynamic Light Scattering (DLS), and a custom sonication robot. The goals of this project are to apply these lab automation tools to construct and characterize crystalline structures of nanoparticles encapsulated in lipid membranes and connected with DNA linkers. With high throughput methods, the impact of design parameters on the crystal structure can also be determined. Parameters of interest in the self-assembly of particles include the molar ratio of lipid membrane components and the nanoparticle surface area to membrane surface area ratio. The first assembly step is embedding the nanoparticles in a lipid membrane of optimal composition. Next, the cholesterol end of synthesized DNA-cholesterol fragments embeds in the membrane and complementary DNA fragments are added to connect the nanoparticles when combined with a complementary DNA bridge. The aggregates formed are analyzed with Zeta potential, SAXS, and DLS to determine if crystals are formed. Preliminary results from this project are presented here.

Linear electrochemical impedance spectroscopy (EIS) is widely used in the characterization of electrochemical systems, such as batteries, although the results of EIS are only as good as the scientist's model of their data, as it’s possible to fit multiple models to the same data. Nonlinear EIS (NLEIS) can also be helpful when characterizing batteries - as they are nonlinear devices - and reveal additional information, such as the asymmetry of the charge transfer between charge and discharge. Combining EIS and NLEIS results in multiple, interrelated data sets, which when fit together drastically reduces the set of models that fit the same data, providing a better understanding of battery physics. However, NLEIS is not as widely developed or used as traditional EIS methods. The goal of this research project is to further develop the use of NLEIS for battery characterization in order to combine EIS and NLEIS to ultimately provide a more accurate picture of battery health. To reach this goal, I plan to test fresh and aged lithium nickel manganese cobalt (NMC) pouch cell batteries with my group’s EIS/NLEIS model. Using materials and equipment from the Washington Clean Energy Testbeds, I will then deconstruct these batteries and fabricate coin cell batteries from the harvested electrode materials and run EIS/NLEIS experiments on these coin cells, comparing the results of the coin cells to the results of their parent pouch cells to assess the accuracy and usefulness of the NLEIS model. Advancing battery health testing is critical for the future development and use of batteries, as understanding battery health allows consumers and scientists to make sustainable decisions regarding battery use, recycling, and disposal.