Atomic Force Microscopy (AFM) images of peptide self-assembly on two dimensional atomically-thin inorganic solid substrates have features that are difficult to extract because of the diverse conformations these peptides have on the surfaces from nanoislands, no nanowires to confluent films and disorganized nanostructures. Analysis of these images can produce parameters such as thickness, aspect ratio, order/disorder ratio, and orientation distributions quickly and accurately. The first step is to input data in a table and convert it into an array. The images are then converted into a grayscale version, its background noise subtracted and subsequently renormalized. User determined edge detection techniques are then used to delineate the edges in the images. Appropriate segmentation methods are then used to separate different types of nanostructural textures and phases, and coupled with the edge information. Several further parameters such as percent area and volume fraction of each phase (surface coverage), percent-ordering in the long-range ordered phases, their shape and orientation distributions, as well as relative sizes are identified and quantified from the images. The sets of unique parameter are then used as the bases for assigning specific labels for the degree of molecular recognition of the substrate by the peptides and eventual orientation relationships between the peptide nanostructures and the crystalline lattices of the 2D atomically thin solid substrates. These labels are then used to train a machine learning algorithm to cluster these images and relate them to processing parameters associated with them via a relational database. The eventual goal here is to parameterize the images so that we can predict the ordering characteristics of any peptide-substrate pair to accelerate design of future technologies, e.g., bionanosensors, emanating from these hybrid material systems. 

 Polyethylenimine (PEI) is considered as the most promising alternative gene carrier to viral vectors. PEI-based carriers minimize unwanted immunogenicity and promote loading capacity. However, PEIs’ nondegradable nature determines their high cytotoxicity. To minimize the toxicity and improve gene transfection efficacy, biodegradable cross-linking agents have been screened to synthesize biocompatible PEIs. Various cross-linkers have been linked to low molecular weight PEI (MW = 800) and tested across multiple cell lines (xPEI). N,N'-Methylenebis(acrylamide) (NDA) showed the lowest toxicity and highest transfection rate among all cross-linkers. To further optimize gene transfection efficacy, xPEIs were modified with increasing amount of fluorocarbon (xPEI-FC). DNA that expresses red fluorescence protein are bound to xPEI and xPEI-FC and then incubated with cells for 48 hours. Green florescence light is later used to examine the presence of RFP. xPEI-FC has demonstrated higher biocompatibility as well as higher transfection rate in vitro with higher level of RFP. The best-performing candidates for xPEI, PEI-FC and xPEI-FC based on toxicity and transfection efficiency will be selected for nanoparticle (NP) modification. Selected candidates will be conjugated with Chitosan-poly(ethylene glycol) (PEG)-catechol copolymer (CCP) and then grafted onto iron oxide nanoparticle (IOCCP-PEI). The resulting nano-vector delivery system will be tested in vitro. Ultimately, in vivo test will be performed to evaluate its transfection efficacy in living organisms.

This research aims to examine the effects of nanoparticle-mediated inhibition of the phospholipid glutathione peroxidase (GPX4) pathway in mesenchymal state cells on radio-resistance in glioblastoma (GBM) therapy. GBM is a particularly deadly cancer with poor survival rates and relatively low treatment success, despite aggressive surgery and radiotherapy. Recent research has shown that biocompatible, tumor-targeted iron oxide nanoparticles (NPs) can serve to enhance radiotherapy through production of secondary electrons and subsequent reactive oxygen species (ROS) within the tumor volume. The presence of these NPs in the tumor during radiotherapy has shown to decrease damage to the healthy surrounding cells and prolong survival in mice with GBM tumors. This approach however, has not demonstrated the ability to eliminate the cancer completely, in part due to the presence of mesenchymal state cancer stem cells. The GPX4 pathway has shown to effectively kill mesenchymal state cells in sarcomas by inducing ferroptosis, an iron-dependent form of cell death. Here we plan to evaluate the efficacy of a range of GPX4 inhibitors on silencing of the GPX4 pathway and the ability to induce ferroptosis in primary human GBM cells. Inhibitors identified as effective silencers of the GPX4 pathway will be combined with a NP delivery vector to provide targeted delivery to GBM. The radioenhancement capabilities of iron oxide NPs in conjunction with targeted therapy against mesenchymal state cancer stem cells may provide a means to overcome radioresistance in GBM therapy.

Solar energy has unmatched potential as the energy source of the future and semiconducting polymers offer a unique set of properties that can address many of the current barriers that hold solar technology back. Little work has been done investigating bulk film microstructure formation, but the ability to control and engineer defects opens the door to new applications that employ tunable energy levels and adjustable open circuit voltage and short circuit density. Semiconducting polymers are exciting because they have untapped potential for improvements in efficiency, they offer a cheap, energy efficient alternative to silicon, and can be easily mass produced via roll-to-roll printing. Solution processing via roll-to-roll printing is transformative as it allows for low energy, high throughput manufacturing of flexible devices. This research focuses on generalizing structure-property relationships for semiconducting polymers and investigating the locations and causes of crystalline defects. The goal is to extrapolate measurements made at a nanometer scale on self-assembled poly(3-hexylthiophene) (P3HT) nanowires to larger production of bulk films. Current understanding of polymer crystallization is built on two competing models, both of which were developed for non-conjugated, therefore non-semiconducting polymers. The Luscombe group has previously demonstrated that the crystalline defects, such as unfavorable monomer arrangement (regioregularity), bulky end groups, the range of polymer lengths (dispersity) and the specific polymer length (degree of polymerization (DP)) do not affect nanowire width, meaning defects are not excluded to the perimeter of the nanowires. This research isolates the variable of polymer length and controls other defects. Using X-ray diffraction and differential scanning calorimetry, nanowires synthesized from polymer lengths ranging 50 to 150 DP have been tested for melting temperature, level of crystallinity and dimensions to determine where the defects reside. Once this is understood, we can confirm which model for conjugated polymer defects is correct.

Organic electronics have generated wide interest and excitement because they are relatively inexpensive to produce and are created from abundant resources, unlike their inorganic counterparts. Many organic materials also possess the unique potential of being stretchable in electronic applications including solar cells, OLEDs, and transistors. These materials can be made into wearable electronic devices and have the potential to power other advanced technology. Organic electronics are often made using semiconducting polymers. However, these polymers are semi-crystalline and brittle in solid state. To enhance their stretchability, our approach is to incorporate stretchable rubber into the semiconducting material by chemically linking the two polymers to combine their properties. This research investigates the crosslinking the well-known semiconducting polymer P3HT, poly(3-hexylthiophene), with polybutadiene (PB), a common rubber, to create a stretchable semiconducting material. To make crosslinking possible, we synthesize P3HT that includes a functional bromine to produce P3HBrT, poly(3-(6-bromohexyl)thiophene). Once the P3HBrT is synthesized, it can then be crosslinked with PB at different ratios to optimize for conductivity and stretchability. The crosslinked P3HBrT/PB can then be made into a thin film transistor and characterized for the desired properties. Ultimately, future research in this area will lead to a new generation of electronic devices with improved material properties.

Ultrasonic transducers are key components in the probes of ultrasound devices used for medical imaging. The probe consists of 4 different materials; a piezoelectric element, backing material, an acoustic matching layer and an acoustic lens. The properties of these materials will directly impact the performance of the transducer and the quality of the images produced, making them a high priority for future development. Currently, researchers at Siemens Healthineers are looking to develop new materials for our ultrasound probes to help increase device performance and make better ultrasound devices. This senior capstone project looks to develop the Acoustical Properties Measurement System (APMS), a characterization testing system, to aid in the production of the new transducer materials. Development of new MATLAB and LabView code has been carried out to provide a smoother operating experience and enhanced design simulation. Data storage has been revised to provide a single location for users to access all collected data and a new naming system has been implemented for easier sample tracking. A standard operating procedure has also been established to help aid new users in operation of the system and a new method for measuring high attenuation materials has been developed. These improvements to the APMS system will further benefit the research and development of Siemens Healthineers ultrasound devices, providing customers and their patients with provide accurate diagnoses and help them to make the best decisions for their care.

Periodontal disease (PDL) results from a serious infection in the gingival tissue (gum) that can eventually lead to tooth loss and jawbone damage. The disease is common with more than 3 million cases in the US per annum. Bacteria build up in plaque lead to gingivitis and periodontitis under improper oral hygiene. If left untreated, the supporting tissues of the teeth e.g., cementum and periodontal ligaments will be lost, therefore making the teeth and supporting tissues vulnerable to bacterial attack, leading to serious infections and, even, to death. Current approaches in regenerating periodontal ligaments include the use of bioactive molecules and barrier membranes for guided tissue regeneration using human stem cells. Although the utilization of such materials enhances the cell proliferation and differentiation to a degree, the absence of cementum-like tissue prevents the complete regeneration of periodontal ligaments on the tooth surface. The aim of this project is to develop a biomimetic strategy to restore cementum tissue and regenerate the periodontal ligaments using human periodontal ligament (hPDL) cells in vitro. Using peptide-guided remineralization, we created a new cementum-like mineral layer on exposed dentin. The hPDL cells are then cultured and seeded on the novel cemento-mimetic layer and induced to differentiate. The proliferation and differentiation of the hPDL cells are monitored in detail using 3-(4,5-Dimethylthiazol-2-yl)- 2,5-diphenyltetrazolium bromide (MTT) and alkaline phosphatase (ALP) assays, respectively. Our results show that the newly formed cemento-mimetic mineral layer facilitates the hPDL growth and differentiation. The method described herein offers a unique biomimetic solution to regenerate periodontal ligaments and thereby ultimately prevent tooth loss and eliminate periodontal disease. This work is supported by WA-State Life Sciences Discovery Funds, UW-School of Dentistry Spencer Funds, and Amazon-UW/CoMotion Catalyst Program.

Synthetic polymers such as polyester fleece have been used in garments for decades without realization of the significant amount of microscopic fibers that they shed, or the impact of that pollution on our ecosystem. Water samples were collected to determine the presence and chemical composition of plastic microfiber waste in various local water sources such as Lake Washington, the Puget Sound, different tap water sources, and at various stages in the local waste water treatment process. Plastic microfiber waste has been found in surface and tap water around the world, and in this project the degree of plastic microfiber contamination in local waters was determined. To do this, water samples were collected at a variety of locations and filtered, and the resulting filtered matter analyzed using optical and scanning electron microscopy to determine the presence and size of plastic microfibers.  FTIR (Fourier transform infrared) spectroscopy was used to determine the plastic's chemical composition.  The results of these tests indicated the origin of these microfibers if they are of a type of plastic commonly used in garments (polyester), and the size and presence in samples from different sources indicated if current water purification infrastructure is adequate to remove these microfibers from the water supply. The information gathered can be used to determine the magnitude of the problem and effectiveness of current solutions at a local scale. Recommendations are made regarding the curtailing of this problem.

The goal of our project is to create an electronic device capable of early detection of pancreatic cancer (PC) with high selectivity and sensitivity. PC projects a very low survival rate often due to late-stage cancer diagnosis. Recent research has established that there are PC biomarkers prevalent throughout the body for several years before symptoms emerge. The consequent wider time window presents an opportunity for these biomarkers to be detected at their initial low concentrations thus allowing for early diagnosis. Our device uses a modular sensing construct consisting of an immobilized probe molecularly bound to the surface of the sensor device. Detection occurs when a target biomarker specifically binds to the probe and changes the electrical properties of the sensing surface that is measured quantitatively. Validating the functionality of the sensing construct and its properties is accomplished through a variety of molecular adsorption and binding techniques that assess each step; from probe immobilization to target detection. Using this modular design, research is underway to develop an array of sensors, thus potentially revolutionizing rapid medical diagnostics to provide long-term health monitoring of PC and other cancers. 

In scientific research labs, in general, experiments are generally treated as a black box: a prepared sample goes in, something happens, and one gets results that are then obtained via elaborate characterization steps. Several important dependent or correlated parameters are either discarded or ignored because of a lack of coherent dependency analyses that require critical thinking, linking, and pattern recognition. In this research we are working to stop treating experiments and computational simulations as black boxes, and create a cohesive platform where materials used, processes and parameters utilized and results achieved can be brought together as separate but related sets of databases. In the next step, the relationships between all the different parameters can then be connected, analyzed and visualized. Machine learning and AI techniques can then be used to predict results using these databases, thereby reducing experiment time, and taking away the traditional ‘trial and error’ method of experimentation. The research involves creation of a software interface, with numerous image and signal processing tools and applications running on libraries made customizable to research fields, types of experiments, etc. Assorted variety of services such as parallelization, compression, data analysis, and visualization, caching (among others) are also provided. We are improving the accuracy of time series data analysis and using fingerprinting to depict all parameters for improved predictability, flexibility and accuracy. When fully developed, we anticipate that the program will enable experimental and computational researchers to extensively use, customize and apply data analytics, machine learning and AI even in niche research in the hard sciences at the intersection of biology and genetics, materials science (physics, chemistry) and engineering, and computational modeling and informatics, enabling faster and accurate cross disciplinary innovation in technology and medicine. The research is supported by NSF-DMREF (DMR-1629071) program at GEMSEC-MSE, as part of National Materials Genome Initiative.

A strategic area of focus in the field of molecular biomimetics integrates biology and inorganic materials to create novel bioelectronic devices. Using combinatorial mutagenesis, the GEMSEC lab at UW has developed genetically engineered peptides for inorganics (GEPIs) consisting of short (~12 amino acids) sequences that specifically bind to solid state materials (i.e. graphite, MoS2, BN). This binding specificity allows for seamless integration between biological and solid substrates, uniquely bridging the worlds of biology and solid state devices. The structure of the bio/nano interface in these systems has profound impacts on the subsequent device properties, performance, and durability. Therefore it is important to understand how the processing conditions affect the structure -- a cornerstone of materials science and engineering. To this end, we aim to develop a phase diagram that will allow for quantitative prediction of the bio/nano interface’s self-assembled structure. The peptide is deposited in solution onto a graphite surface, incubated under specifically defined experimental conditions, then examined using atomic force microscopy (AFM). Herein, we investigate the effects of experimental conditions (peptide concentration, incubation temperature, and electrochemical bias/pH) on the peptide's assembled structure on 2D layered materials. These fundamental parameters are essential to elucidate the kinetics and thermodynamics of the molecular assembly process allowing for further design, engineering, and coding of the processes of these hybrid materials systems, providing the much needed fundamental science toward biology-guided solid state devices.

Dental hypersensitivity (DH) is a common oral health condition in the U.S. affecting the majority of the adult population. It is caused by the exposure of dentin due to the demineralization of the protective cementum or enamel that covers the tooth surface. When the dentinal tubules are exposed, nerve fibers in the pulp or predentin are stimulated by the displacement of the fluid and report pain. The stimulus that triggers the onset of pain can be of thermal, chemical or mechanical origin. There is still no effective agent to completely resolve the patient’s discomfort with DH. Over-the-counter products are commonly advised in the management of DH while toothpastes containing strontium, oxalate or potassium salts, or fluoride are recommended with limited efficacy to reduce the sensitivity from DH. Restorative materials using composite, glass ionomer or amalgam are adapted to treat the affected area with limited success. The goal of this project has been to develop a biomimetic treatment by restoring cementum tissue using a peptide-guided remineralization approach, thereby occluding the exposed tubules with a newly formed mechanically and thermally stable mineral layer. The College of Engineering working closely with School of Dentistry-UW involves mimicking the hypersensitivity condition by removing enamel/cementum of extracted human teeth to expose underlying dentin. The samples are then treated with peptide-guided remineralization resulting in 10+ micrometer thick new layer over the damaged dentin. Our results exhibit a highly effective way to occlude the exposed dentinal tubules by a newly formed mineral layer which penetrates into the dentin tubules. The method described herein offers a unique biomimetic treatment protocol for dental hypersensitivity, which will be developed as a platform technology for effective in-clinic and over-the-counter hypersensitivity treatments. The work is supported by WA-State Life Sciences Discovery Funds, UW-School of Dentistry Spencer Funds, and Amazon-UW/CoMotion Catalyst Program.

This project focuses on material variables that govern the toughness of glass-based materials by mimicking the architectures of mollusk shells. Currently, a large challenge in utilizing strong materials for engineering purposes is their inherent lack of toughness at large deformations. By stacking plates of strong, brittle material together in the same manner as nacre, and by employing special adhesives to redistribute energy, it could be possible to increase the toughness by creating a new, composite material. The nacre stacking sequence was chosen for this project due to its high toughness in order to achieve a new, glass-composite material which is much tougher than the base reinforcement (glass) alone. Composites are materials made out of more than one material. Composites have a strengthening component (reinforcement) embedded within a matrix material that holds everything together. This project created four-inch long beams of glass-reinforced-adhesive laminates, with varying thicknesses of adhesives. The beams were subjected to impact testing in order to determine their toughness. The Instron Dynatup impact tester was used for these tests. The Dynatup is a drop-tower and utilized a steel, wedge-shaped indenter as the impacting surface. In addition to the toughness (energy dissipation) data recorded by the Dynatup, fractography was conducted using optical and scanning electron microscopy of the fracture surfaces. The results of the monolithic stacking sequence were compared to those of the nacre-like stacking sequence, and conclusions were then determined. Applications that involve improved vehicle windshields and other systems are the goal of this study. 

Phytoliths are microscopic silica bodies that form in many plants. Being resistant to organic decomposition, they can be used to track plant evolution and past vegetation changes. Although they were traditionally considered as non-functional cellular by-products, recent studies suggest that phytoliths may have photonic, structural, nutritional and defensive benefits in plant growth and survival. Grasses, which are large terrestrial producers of biogenic silica, are the suitable model system for studying the adaptive significance of silica accumulation in plants. Identification of phytoliths within the plants (for studying function) and inside the sediments (for studying evolution of vegetation) have so far been limited to optical microscopy-based methods where fluorescent dyes are commonly used to increase contrast between silica bodies and plant tissue. Due to their non-specific nature, however, fluorescent dyes often accumulate in different parts of the cells and silicified tissues, causing false positives and leading to incomplete staining and difficulties to construct 3D structures. With exquisite molecular recognition and assembly properties, solid-binding peptide tags (dubbed GEPIs) are surface functionalization moieties that can be chimerized with fluorescent and utilized as material-specific probes to selectively label variety of inorganic materials at surfaces and within interfaces. Our goal is to develop peptide-based phytolith-specific fluorescent probes to identify and delineate phytolith shapes with high precision and in situ. Using phytoliths-specific peptides probes, designed and chimerized with fluorescent molecules, our goal is to incubate different grass species, and visualize them using laser-confocal microscopy. We hypothesize that the novel molecular construct enable specific detection of phytoliths in situ. The method we are developing will offer a unique solution for fast and accurate identification and 3D organization of inorganic nano- and micro-structures in tissues from living plants and in paleontological samples.