1,720,976 research outputs found
Data-Driven Analysis of Experimental Design Spaces for Colloidal Synthesis and Assembly
No full textThesis (Ph.D.)--University of Washington, 2025Colloidal nanomaterials offer diverse functionalities driven by their structural properties, requiring precise control over synthesis and assembly. Due to the complexity of the experimental design space, a self-driving lab approach, which combines artificial intelligence (AI) with autonomous experimentation, is a powerful method used to navigate it. In this approach, an AI agent selects and evaluates experiments in a closed loop, progressively improving its understanding of the design space as data is collected. Some ways to improve self-driving labs include improving the distance metric to enhance the system's ability to guide synthesis. In this work, the amplitude-phase distance metric is introduced, which captures shape differences in functional datasets (e.g., UV-Vis Spectroscopy, SAXS). Its performance is compared to the Euclidean distance metric, and key differences are observed in nanoparticle structural differentiation (e.g., between nanospheres and nanorods) and the balance between the exploitation and exploration of the design space. Further advancements in self-driving labs include using multiple characterization methods (UV-Vis, SAXS, TEM), minimizing reliance on literature for design space definition, and employing interpretable AI to extract experimental insights. These improvements are demonstrated with another self-driving lab tested with silver nanoplate synthesis, where the derived design rules align with established knowledge. In addition to inorganic nanoparticle synthesis, this work also explores engineering stimuli responsive protein assemblies, which was done by modifying the RhuA protein with light and chemically responsive molecules. Structural analysis via a Monte Carlo-based SAXS fitting method reveals light-controlled assembly into tubes or sheets, influenced by solution ionic strength. This efficient modeling approach supports future exploration of metastable structures using in situ SAXS combined with AI-guided light sequencing. Finally, we explore DNA-mediated assembly of lipid encapsulated nanoparticles where high-throughput experimentation is used to identify the effect of design variables on the final assembly
Engineering the Multi-Length Scale Structure of Self-Assembled Conjugated Polymer Networks
No full textThesis (Ph.D.)--University of Washington, 2014Conjugated polymers are a Nobel Prize winning class of materials known for their intrinsic semi-conducting properties and have been used in applications as diverse as organic-based solar cells, transistors, light emitting diodes, sensors and thermoelectric devices. These applications have varying, but optimized, structure and property requirements which often include an interconnected network of conjugated polymers to transport charge. In this work, self-assembly and gelation are explored as a platform to engineer the multi-length scale structure of conjugated polymer networks. A detailed understanding is developed through structural characterization of each stage of the self-assembly process: dissolved polymer, semi-crystalline fiber, fibrillar branching and percolated network formation. Variations in self-assembly conditions were utilized to develop multi-length scale structure-property relationships. Furthermore, a new method to directly incorporate these fibrillar network structures into thin films for organic electronics is discussed. This dissertation will demonstrate our work towards understanding the mechanisms behind conjugated polymer self-assembly in order to provide robust design parameters that can be tuned to generate specific structures, occurring on multiple length scales, and relevant properties for a diversity of applications
Automation and Autonomous Experimentation for Sol-Gel Nanomaterial Synthesis
No full textThesis (Ph.D.)--University of Washington, 2025The nexus of laboratory automation and machine learning enables a new paradigm of autonomousexperimental materials research. Autonomous experimentation integrates highly automated
materials synthesis and characterization experiments with machine-learning guided experimental
design strategies to adaptively execute experiments that advance specific research goals. These
systems have the potential to accelerate materials development timelines compared to traditional
manual processes by efficiently targeting experimental efforts. Sol-gel processes are used to
synthesize a diverse array of metal oxide nanomaterials for many applications. In particular,
mesoporous colloidal silicas are promising materials for use as catalysis support matrices,
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chromatographic separation media, and drug delivery systems. Achieving retrosynthetic control
over particle morphologies is critical for advancing use in these applications. In this work,
automated and autonomous systems for the synthesis and optimization of colloidal silicas are
developed. An open-source platform for flexible laboratory automation is developed to enable
democratized access to autonomous experimentation. A system for the fully automated synthesis
and characterization of colloidal mesoporous silicas is developed which integrates the open-
hardware automation platform to perform automated sol-gel synthesis with synchrotron and
laboratory X-ray scattering instruments for characterization. Synthesis campaigns executed with
this system have produced colloidal silicas with a range of particle morphologies and mesopore
phase structures. Finally, progress towards integrating a Bayesian optimization based experimental
design strategy to optimize the morphology of silica nanoparticles is discussed. This work
represents an important step towards accelerating the development of sol-gel materials with
autonomous experimentation
Self-Assembly of Nanoparticle Surfactants
No full textThesis (Master's)--University of Washington, 2014Self-assembly utilizes non-covalent forces to organize smaller building blocks into larger, organized structures. Nanoparticles are one type of building block and have gained interest recently due to their unique optical and electrical properties which have proved useful in fields such as energy, catalysis, and advanced materials. There are several techniques currently used to self-assemble nanoparticles, each with its own set of benefits and drawbacks. Here, we address the limited number of techniques in non-polar solvents by introducing a method utilizing amphiphilic gold nanoparticles. Grafted polymer chains provide steric stabilization while small hydrophilic molecules induce assembly through short range attractive forces. The properties of these self-assembled structures are found to be dependent on the polymer and small molecules surface concentrations and chemistries. These particles act as nanoparticle surfactants and can effectively stabilize oil-water interfaces, such as in an emulsion. In addition to the work in organic solvent, similar amphiphilic particles in aqueous media are shown to effectively stabilize oil-in-water emulsions that show promise as photoacoustic/ultrasound theranostic agents
Bottom-Up Synthesis of Colloidal Systems Using Sequence Defined Molecules
No full textThesis (Ph.D.)--University of Washington, 2023Self-assembled colloidal nanoparticles can be used to deliver vaccines, sense pathogenic materials, and mark tumors. In response to light, they catalyze the formation of clean fuels or help transform it directly into usable energy.Quantum dots are used in commercial displays and will be a key step in new forms of computing and information storage.
The performance of nanoparticles is dictated by their structure and composition and can be controlled using sequence defined molecules.
The latter include any polymeric or oligomeric materials in which the exact sequence is precisely controlled using enzymatic processes or synthetic chemistry. Their physicochemical diversity and modularity are used to intervene in chemical processes occurring during synthesis, stabilize specific crystal facets, or form templates that guide nanomaterial growth.
Yet the multivariate relationship between experimental parameters and intermolecular reactions that govern nanomaterial self-assembly is difficult to study using traditional experimental methods. In this work, gold nanoparticle synthesis in the presence of peptides is used as a model system for developing and integrating experimental automation with computational approaches to extract information for guiding sequence design.Several peptide variants were selected through systematic variations of a gold binding peptide, and nanoparticles were synthesized using a liquid handling robot in a large design space of reagent concentrations.
The plasmonic response of nanoparticles was used as a fast proxy for changes in structures and was analyzed using functional data analysis methods.
The analysis resulted in a metric for quantifying how changes in peptide design affect nanoparticle synthesis outcomes, and the conclusions were corroborated with small-angle X-ray scattering and electron microscopy.
Next, the relationship between substitution of methionine in a peptide sequence and an increase in particle anisotropy was assessed.
A programmed liquid handling robot was used to dynamically intervene in nanoparticle synthesis to control the resulting structure and stability of anisotropic nanoparticles.
Finally, highlights of how small-angle X-ray scattering can work in parallel with computational methods to study colloidal self-assembly mechanisms are presented
Open-Science Materials Acceleration Platforms for Clean Energy Material Design Spaces
No full textThesis (Ph.D.)--University of Washington, 2024Conventional materials synthesis schemes can be labor and time-intensive, which significantly impedes the pace of new materials discovery and their applications. To achieve innovative solutions to global challenges, such as climate change and an ever-growing sustainable energy demand, a new paradigm of science movement arose in the past five years to adapt traditional materials science workflows to ones with the potential to accelerate the pace of materials discovery. This novel approach to science has been called “Materials Acceleration Platforms” (MAPs) or “Self-driving Laboratories” (SDLs) and it relies on autonomous robotic systems designed to conduct scientific experiments and research with minimal human intervention. However, new initiatives of MAPs are still too costly, and their rigid design limits their implementation. It is also essential to acknowledge and address the diverse needs and challenges of different scientific fields. In this context, open-hardware principles have allowed the use of laboratory automation to be more accessible and more easily implemented for a variety of applications. In this work, workflows with various levels of automation of experimental and computational tasks are presented implementing a combination of off-the-shelf open-source robotic platforms, high-throughput, small scale characterization tools, and custom open-hardware solutions. A semi-automated protocol was designed for the synthesis, physical and electrochemical characterization of novel electrolytes based on deep eutectic solvents and organic redox active molecules for use in electrochemical storage systems. Next, a human-in-the-loop SDL was developed to explore a large design space for the investigation of CdSe nanoparticles’ optical properties at a fraction of the time compared to traditional techniques. This work implements a repurposed multi-tool, open-hardware 3D printer platform, Jubilee, reconfigured for sonochemical processing. Furthermore, data-driven methods were also used to obtain a holistic view of the space and learn trends in the data based on all variables tested. Finally, a new python-based control library for Jubilee is presented, along with the broadening of tool library with new synthesis, processing, and characterization tools. These efforts enabled the creation of closed-loop workflows, such as the benchmarking protocol for SDLs, an autonomous color-mixing problem. These tools hold immense scientific and educational value for both new materials discovery and more interdisciplinary skill development. The increased access through low-costs solution will broaden the application space of this new science paradigm, empowering a larger number of (materials) scientists to take advantage of the precision of automation, and enabling automation tools to be included in hands-on educational curricula
Structural Characterization of Cross-linked Pluronic Hydrogels via Small Angle X-ray Scattering
Get PDFThesis (Master's)--University of Washington, 2022Engineered living materials (ELMs) are a new technology within Synthetic Biology (SynBio) that incorporates living microbial organisms within hydrogel matrices. Unlike traditional liquid cultures of living cells, both components in engineered living hydrogels can be programmed to meet specific goals. The living cells can be engineered to fulfill a multitude of functions, ranging from sensing to chemical production, and even generation of electricity. Hydrogels are a popular construct for these materials for a number of reasons. Most hydrogels offer a high degree of biocompatibility and chemical permeability that make it ideal for sustaining living microbes. There are a number of design criteria that must be met to determine if the hydrogel platform is a good candidate for manufacturing chemical intermediates. They must be able to provide compartmentalization of living cells, prevent cell leakage, maintain mechanical integrity, allow substrates and products to flow in and out of the hydrogel matrix, and sustain long shelf life. The primary motivation behind the research presented in this thesis is to contribute to the collaborative effort amongst an interdisciplinary team of researchers funded by the National Science Foundation (NSF). This group is interested in expanding the potential of SynBio and ELMS by developing next-generation bioreactors capable of converting cheap raw materials (such as sugars and amino acids) into high-value products such as pharmaceuticals and chemical intermediates using yeast and bacteria as the biocatalysts. Current manufacturing processes for these chemicals of interest require sourcing materials that rely on unsustainable farming practices. The development of these microbe-laden hydrogels could introduce a more sustainable approach to manufacturing these products. Pluronic F127 was selected as the polymer for this hydrogel due to its relatively high solubility in water and its ability to self-assemble in aqueous media without additives. Pluronic F127 as it exists off the shelf does not fulfill the required design criteria. Although it is capable of gelling, it cannot maintain this mechanical integrity when exposed to solution. Therefore, a new functionalized form of F127, known was F127-BUM, was developed by the Nelson Research Group. The work presented in this thesis focuses on using small angle X-ray scattering to characterize the progression of F127 through functionalization with bisurethane methacrylate to form F127-BUM, polymerization into cured hydrogels to prevent dissolution in water, and lyophilization/rehydration for extended shelf life
Structure-Property Relationships of Self-Assembled Conjugated Polymers
No full textThesis (Ph.D.)--University of Washington, 2015Conjugated polymers, due to their unique optical and electronic properties, can be used for applications such as photovoltaic devices, light-emitting diodes, transistors, and organic sensors. Although, conjugated polymers have several promising properties, uses for these materials are often limited by inefficient charge transport. Inducing polymer aggregation improves the efficiency of charge propagation due to an increase on the conjugation length of the polymer and the crystallization of the polymer domains. The scope of this project is to study the self-assembly of conjugated polymers and its effect on the properties of the materials. More specifically, this project aims to control the kinetics of aggregation in order to affect the structural conformation of the polymer and have an impact on the properties of the material. Self-assembly is controlled by changing parameters such as solvent quality, temperature and dopant chemistry. Both structure and properties change in response to the conditions in which the polymer is placed. This means that the properties of the material could be controlled and optimized. Additionally, a protocol to synthesized organic solvent dispersible Poly(3,4-ethylenedioxythiphehe) is developed and studied. This synthesis uses the controlled aggregation of the polymer strands to prevent the polymer from going out of dispersion. This procedure enables the use of one of the most conductive conjugated polymers in applications previously prohibited due to its poor processability
Structural Characterization of Composite Thin-Film and Dispersed Phase Conjugated Polymer/Fullerene Composites
Get PDFThesis (Ph.D.)--University of Washington, 2014Controlling the structural morphology of conjugated polymer/fullerene composites is an important aspect in improving the performance of polymer solar cells. The efficiency of exciton dissociation and carrier transport to the working electrodes are both a function of the size and distribution of polymer and fullerene domains within the active layer. In the current solution processing paradigm, these characteristics are intrinsically linked to the detailed process history of the film. This fact compromises the promise of polymer solar cells as an inexpensive renewable energy technology because performance gains made in the laboratory may not translate well to scaled-up manufacturing processes. Aqueous dispersions of conjugated polymer/fullerene nanoparticles have the potential to address this challenge. Similar to their thin-film counterparts, the performance of photovoltaic devices derived from conjugated polymer/fullerene composite nanoparticles (CNPs) is a function of the structural morphology within each nanoparticle. However, whereas the structural morphology of an active layer produced using solution phase deposition is a function of the detailed history of the coating process, the structure of CNPs is fixed during their formulation. Because of this fact, the structure can be identified in the dispersed phase and tied, using single particle characterization, to its photovoltaic performance before it is deposited into a device. Therefore, the optimization of devices derived from CNPs can be broken into two discrete problems: improving the intrinsic properties of a nanoparticle as a function of how it is produced and optimizing the extrinsic properties of devices through the deposition of optimized CNPs
Making Ceramic Membranes Used in Redox Flow Batteries with Sol-gel Process
No full textThesis (Master's)--University of Washington, 2018To increase the amount of renewable energy utilized in electrical grids, large- scale batteries are required. Redox flow batteries (RFBs) are promising batteries. However, the high cost of electrolytes and membranes impede the application of RFBs. It is worth exploring a new membrane with inexpensive material and simple process. The goals of this project are to explore a new anion exchange membrane using sol-gel process and to find the ceramic materials can be used as the membrane materials of alkaline-based organic RFBs. The membranes were prepared by coating silica paper with titania. After sintering, titania membranes survived in 3 M HCl but showed worse permeability performance than Nafion membranes. Thus, titania might not be the proper membrane material of anion exchange membranes. The cellulose paper coated with Ludox® CL was stable in 1 KOH. Therefore, alumina might be the proper membrane material utilized in alkaline-based organic RFBs
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