Industrial chemical separation processes, such as distillation, drying, and evaporation, consume 10-15% of US annual energy production. Membranes, which act as a selective barrier to separate compounds, are substantially more energy efficient than traditional chemical separation methods that require heat and could help reduce this consumption. Inorganic membranes are inherently suitable for many separation processes because they are chemically and thermally stable; however, ceramic membranes are mechanically fragile and costly to produce. Commercial polymeric membranes are comparably more economical but degrade in harsh organic solvents and high-temperature environments. One approach to achieve the necessary membrane properties at low cost is vapor phase infiltration (VPI), a gas-phase synthesis technique consisting of sorption, diffusion, and entrapment of vapor-phase reactants within organic polymers. The infiltration of inorganic oxides through VPI has been shown to enhance the properties of polymeric membranes by producing cost-effective, chemically stable, and temperature-tolerant organic-inorganic hybrid materials. However, the mechanical properties of these hybrid membranes, which are crucial for maximizing lifetime and durability, are generally less well understood. In this study, polyethersulfone (PES) membranes are subjected to trimethylaluminum and water under various VPI process conditions in a custom-built reactor. Thermogravimetric analysis is utilized to quantify the extent of inorganic infiltration by measuring the aluminum oxide loading within PES membranes. Mechanical properties of these membranes are characterized by tensile stress, modulus, and maximum pressure through dynamic mechanical analysis and burst pressure testing. Enhancement in chemical stability is determined by measuring the degradation of VPI-treated PES samples after exposure to organic solvents. These results provide insight into the relationship between infiltration structure, membrane stability, and mechanical properties, which may allow for improved membrane design and more sustainable industrial chemical operations.

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.

Per- and polyfluoroalkyl substances (PFAS) are highly toxic contaminants shed from man-made chemicals which are still being used in consumer and industrial applications. Unfortunately, strong carbon-fluorine bonds present within PFAS prevents their natural degradation in the environment, leading to PFAS accumulation. Membranes, particularly those used for desalination, have been shown to be effective at removing many types of PFAS from water and are less expensive and energy intensive when compared to other removal approaches. However, new membrane materials are needed that can remove even the smallest PFAS molecules. In this project, we are developing new membrane materials aimed at being more effective than commercial nanofiltration and reverse osmosis membranes using molecular layer deposition (MLD), a technique that can deposit and precisely control membrane chemistry. First, commercial membranes from DuPont (NF245, NF270, and Seamaxx) were tested for their pure water permeability as well as rejection of salts and PFAS of varying carbon chain lengths, the results of which were used as an experimental control. Next, polymer membranes were made using MLD. These MLD-based membranes were synthesized and tested, and their results were compared to the commercial membranes for efficacy. This work hopes to develop new membrane chemistries that are more effective at removing PFAS than existing commercial materials.

3D perovskites have enormous potential for optoelectronic applications such as light-emitting devices, photodetectors and lasers, due to tunable optical properties. Achieving precise control over their characteristics, specifically color purity, can be costly to discover because of their highly nonlinear behavior.  In this work, machine learning (ML) will be employed to explore the synthesis parameter space and target perovskite films with desired RGB values. By varying the annealing time and composition of the MAPbIBr₂ perovskite while fixing other synthesis parameters the film’s optical response can be adjusted. Using Bayesian Optimization, a data-driven approach will be established based on experimental feedback for precisely tuning the perovskite. This synthesis framework is designed for easy adaptation to other synthetic spaces requiring precise material control. This research aims to accelerate ML-driven design of perovskites while enhancing our understanding of their nonlinear synthesis space.

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.

Laboratory automation has demonstrated great potential in accelerating the discovery and optimization of new materials. However, the lack of low cost high-throughput characterization has been a limiting factor in the development of autonomous self-driving labs. To address this, we developed an open-source 3D-printable robotic framework that can be integrated with an ocean optics spectrometer probe designed to measure materials properties in a high-throughput fashion. The device is low-cost, easy to construct and fully compatible with the Opentron (OT-2) automated liquid handler. The system operates on a printer-gantry system that moves the spectrometer probe across a laboratory plate as scanning progresses. We aim to achieve scanning speeds of 1 second per well, allowing a standard 48 well laboratory plate to be completed in under 1 minute – a significant improvement over current times achieved with human testing. Additionally, we outline potential applications for the system through the characterization of perovskite semiconductors for energy-efficient lighting and discuss the challenges of fully integrating this device into a completely autonomous workflow. Despite its current limitations, by facilitating high throughput characterization through affordable, open-source technologies, this device enables materials researchers in underserved regions to accelerate progress in key areas such as green technology development.