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.

Although crystalline two-dimensional (2D) protein arrays are often found on the surface of archaea and bacteria where they form a protective S-layer, their potential in bionanotechnology applications remains unfulfilled. Progress in computation has recently allowed the (re)design of proteins for self-assembly into arbitrary structures. We are working with a rationally modified protein from S. typhimurium that can self-assemble into large (> 100 µm) and thin (~ 5 nm) hexagonal 2D arrays pierced by ~ 3 nm pores upon addition of divalent cations (e.g., Ca²⁺). The goal of our research is to test the ability of these arrays to organize gold nanoparticles (AuNPs) with desirable plasmonic characteristics. To this end, we stain protein arrays with the lipophilic fluorescent dye Nile Red, and analyze fluorescence microscopy images to quantify how the decoration of arrays with various concentrations of AuNPs affects the rate of photobleaching of the Nile Red fluorophore. Understanding how AuNPs bind to protein arrays could lead to further applications, such as templated growth of inorganic materials or co-assembly of enzymes and inorganic catalysts.

In vitro aptamer ribosensors use small molecule binding kinetics to sensitively discriminate between concentrations of a target ligand. Ribosensor devices have been used to quantify metabolite production for metabolic engineering but are also potentially useful for fields such as point-of-care diagnostics. To be useful in this field, further understanding of the parameter space in which these devices work is required. Two big unknowns within the parameter space are how different media conditions and how the use of lyophilized T7 RNA polymerase affect ribosensor function. To evaluate the potential of ribosensors in point-of-care applications, we measured RNA aptamer ribosensors with in vitro fluorescence assays of the ribosensors in both synthetic urine and its control media. Lyophilizing the T7 polymerase allows for easier transport of heat-sensitive proteins; enabling the use of ribosensors as shelf-stable molecular diagnostics even in low resource clinical care settings. We demonstrate the effects of different factors during lyophilization on the function of aptamer ribosensor devices and optimized lyophilization conditions for optimal function from the reconstituted enzymes. These optimized conditions utilize cryoprotectants to ensure that the T7 polymerase maintains its stability during and after the lyophilization process. Cryoprotectants that have been found to best stabilize T7 polymerase are the addition of sucrose solution and liquid nitrogen flash freezing. This project clarifies the conditions that are essential in transitioning the ribosensor devices from the laboratory to point-of-care diagnostics. We show how these new conditions affect the ability of the ribosensor devices to make accurate measurements. Future work will aim to modify the design for the RNA aptamer ribosensors to function more optimally within the point-of-care parameter space.

Fluorescent nanocrystal quantum dots (QDs) show promise for biomedical application, but are often negatively associated with cellular toxicity. To investigate the potential of QDs as a biomarker in the brain, a systematic evaluation of potential toxicity is necessary. In this study, we evaluated QDs with various surface functionalities and assessed toxicity as a function of concentration and exposure time. We utilized organotypic brain slices obtained from healthy postnatal day 14 (P14) rat pups. Four cadmium selenide (CdSe)-core QD conjugates were evaluated: mercaptopropionic acid (MPA), hydroxyl-polyethylene glycol (PEG-OH), amine-PEG (PEG-NH2), and methoxy-PEG (PEG-MeO) at 0.01, 0.1, and 1.0 μM concentration. Cell toxicity was primarily quantified by measuring lactate-dehydrogenase (LDH) production, which is an indicator of cell death, over a five-day period. Results were supplemented with confocal microscopy analysis of two imaging-based assays: propidium iodide, a stain of the nuclei of dying cells, and fluoro-jade C, a stain of degenerating neurons. QD-MPA treated slices had 7-8% greater toxicity than the non-treated (NT) control. All other functionalities were comparable to the NT control except QD-PEG-OMe, which had 3% lower cytotoxicity suggesting a possible neuroprotective effect. Alternatively, certain functionalities (MPA, NH2) may show lower than expected toxicity due to aggregation before cellular uptake. Compared to 0.1 μM concentrations, 0.01 μM QD treated groups had around 4% lower toxicity. Similarly, 1-3% greater toxicity was observed in extended QD exposure conditions (24h) versus shorter exposures (1h). Results show that toxicity is dependent on surface chemistry, concentration, and exposure time. This is useful in identifying QD conjugates with low cytotoxicity in the developing brain. Understanding QD toxicity can lead to rational design of QDs for site and cell-specific uptake in the brain as a biomarker of neurological disease severity, improving the selectivity of current imaging techniques and providing a powerful diagnostic with regards to diseased cell fate.

Photovoltaic (PV) electricity generation has become much cheaper in recent years and as a result is becoming a larger percentage of total energy production. However, growth is limited due to the high capital expenditure (CAPEX) required to build new PV factories with current technologies. Solution processing techniques (spray coating, roll-to-roll, etc.) to deposit thin-film absorber materials such as CuIn(S,Se)₂ and Cu(In,Ga)(S,Se)₂ represent a much lower CAPEX alternative to current PV processes. Using simple metal chloride precursor salts dissolved in polar aprotic solvents, our group has shown solar power conversion efficiencies of 13.4%, which is a world-record for solution processed CIS. Understanding the complexation chemistry of precursor salts in solution is essential to making stable solutions which produce homogeneous absorber layers after thermal annealing. Using solubility experiments and calorimetry to examine interactions between the precursors in anhydrous dimethylformamide (DMF), we were able to infer participating molecules and stoichiometry of the complexes formed in solution between the metal chlorides, thiourea, and solvent molecules. We have also made CIS absorbers under various thermal annealing conditions, and studied the resulting changes in 1) elemental composition profiles using glow-discharge optical emission spectroscopy (GDOES) and energy-dispersive X-ray spectroscopy (EDX) and 2) film morphology using scanning electron microscopy (SEM). These results represent steps forward in improving solution processing techniques for low CAPEX solar cell manufacturing which will increase the prevalence of renewable energy to combat global warming.

Silica nanoparticles are extensively used in semi-conductor industry as abrasives in various polishing steps. It is important to be able to conduct rapid, robust surface area measurements of the dispersion that do not require dilution and drying. The focus of this research is to explore nuclear magnetic resonance (NMR) technique as a means for determining surface area of silica nanoparticles and its sensitivity to temperature and trace level contamination. NMR is a technique that can be used for determining the surface area of silica particles in dilute and concentrated solutions. After application of large magnetic field, water protons in the vicinity of nanoparticles relax faster than protons in the bulk liquid. Thus, the average relaxation rate of the sample is proportional to the particle loading, particle surface area, and a proportionality constant, specific surface relaxivity, that is unique for different materials. Silica nanoparticles of sub 100 nm size are studied. Specific surface relaxivities for the materials are found and the effect of various contaminants active in NMR are studied. The lowest limits of detection for possible industrial contaminants are obtained. The interaction of surfactants with silica particle surface are studied using NMR technique. The gelation process of silica nanoparticles and its relationship with relaxation time is investigated by time-resolved dynamic light scattering (DLS) and NMR. Only recently has NMR become available as a bench-top instrument targeting widespread adoption for process control and monitoring. This study advances the knowledge on the various ways this relatively new technique can be applied to the characterization of sub-100 nm silica particles, what is useful for semi-conductor industry and nanoparticle slurry manufacturers.

Hydrogen fuel is a non-polluting, sustainable energy source that is a very attractive alternative to fossil fuels. A popular way to obtain this fuel is splitting water with semiconductor photocatalysts, which is inexpensive and efficient. This method relies on sunlight alone to activate the water-splitting photocatalysts. Once the catalysts have absorbed UV light, they begin to split the water molecules that are in contact with their surface. As a result of this, hydrogen fuel is produced more rapidly using a given amount of catalyst when the catalytic crystals have a high surface area to volume ratio, which means the smaller the crystals are, the more efficiently they work. To keep these crystals small, our research group uses a unique method developed by Dr. G. Allan to synthesize and store semiconductor photocatalysts. Instead of synthesizing them normally, we form the crystals inside cellulose fibers. This keeps them from losing surface area to agglomeration. The micropores of cellulose force the catalyst to form as small, insoluble, nanocrystals in a cheaper fashion than traditional methods. We then focused on finding the most effective semiconductor photocatalyst to be stored in fiber. The chosen photocatalyst must have specific properties. It must be synthesizable in an aqueous solution at less than 40 degrees celsius to keep the cellulose pores from collapsing or charring, it must be non-toxic, and it must have a band gap between 1.8eV to 2.7eV in order to absorb visible light. Our project group has spent the last few months researching different materials to determine the most beneficial materials, and we have concluded that the best possible candidates are black titanium oxide, niobium pentoxide, bismuth vanadate, zinc oxide, molybdenum sulfide, and tungsten trioxide. We hope to use these findings to promote an alternative source of clean energy into the market.

Drug delivery to the brain is difficult due to the strictly regulated barriers and a complex microenvironment. Nanoparticles can help overcome these barriers to delivery by providing highly stable and tailorable platforms. Charged nanoparticles have an electrostatic double layer that ensures colloidal stability, while sterically stabilized particles have ligands (e.g. polymers, surfactants) that improve colloidal stability. In the brain, changes in the local pH, temperature, and ionic composition of the surrounding media, either in normal physiological function or in the case of injury, can change the stability of suspended nanoparticles. However, there are limited systematic studies of nanoparticle stability in representative brain microenvironments. This research focuses on how calcium concentration, a key ion in the brain microenvironment, influences aggregation kinetics in artificial cerebrospinal fluid (aCSF) and how other physiological factors (temperature, pH) impact nanoparticle aggregation kinetics. We designed an experiment to examine how divalent cations (calcium, magnesium) alter the aggregation kinetics of both carboxylated polystyrene (PS-COOH) and poly(ethylene glycol)-coated polystyrene (PS-PEG) nanoparticles in physiologically relevant conditions. We hypothesized that the PS-PEG nanoparticles would remain stable at physiological conditions, while the PS-COOH nanoparticles would experience aggregation. Dynamic light scattering (DLS) was used to characterize the nanoparticle hydrodynamic diameters. Each experiment was performed in aCSF at a range of pHs (5.5, 7.2, 8.5) and temperatures (23, 37°C). We demonstrate that PS-PEG nanoparticles have higher aggregation thresholds than PS-COOH nanoparticles. We also show that higher temperatures accelerate aggregation kinetics of both particle types. Acidic environments seem to provide a protective effect for PS-PEG nanoparticles, while basic environments can cause catastrophic aggregation even when the particles are stabilized with PEG. Future research will examine the effect of PEG chain length (2k, 5k, 10k) as well as PEG coverage density on aggregation kinetics in the brain.

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.

Metabotropic glutamate receptor 5 is a receptor for glutamate, one of the major excitatory neurotransmitters. Glutamate serves many functions throughout the nervous system, such as activating microglial cells, the immune cells of the nervous system. Microglial activation then initiates the release of cytokines. Cytokines are responsible for triggering signaling cascades that lead to pro- and anti-inflammatory immune responses. Although cytokines play an important role in mitigating disease and injury states in the nervous system, excessive cytokine release may cause ongoing neuroinflammation and neurotoxicity through a complex series of pathways. mGluR5 deficiency has been linked to neurotoxic cytokine release as well as neurological disorders such as Fragile X Syndrome, autism spectrum disorders, and schizophrenia. The Nance lab established the first mGluR5 rat knockout (KO) colony to investigate the links between glutamate dysfunction and neuroinflammation. This project quantitatively analyzes the levels of pro- and anti-inflammatory cytokines in mGluR5 KO pups, across gender and genotype. RNA was isolated from mGluR5 postnatal day 12 (P12) to P14 KO, wild-type (WT), and heterozygous (HET) pups and converted to cDNA. RT-PCR was performed to analyze mRNA levels of TNF-α, IL-1-β, IL-6, TGF-β, IL-10, GCP-II, and IL-12-β cytokines. 2-ΔΔCt values demonstrated 2 to 4-fold changes in these cytokines due to differences in genotype and gender, relative to the housekeeping gene GAPDH. Characterizing cytokine profiles leads to greater understanding of the interplay between inflammation and ongoing injury in the developing brain, and may elucidate pathways for therapeutic intervention. Identifying specific cytokines linked to increases in neuroinflammation will also provide further insight into the roles that glutamate, and glutamate receptor deficiency, play in the development of neurological disease.

Quantum dots (QDs), fluorescent semiconductor nanocrystals, can be used as a biomarker and diagnostic tool for central nervous system (CNS) diseases because of their unique physical properties. To serve as an effective diagnostic platform, QDs must diffuse from the point of access to a desired target within the brain. To maximize diffusion within the CNS, QDs need to remain monodisperse, and avoid any adhesive interactions or steric limitations imposed by the brain microenvironment. Therefore, characterization of physicochemical properties and overall colloidal stability in physiologically relevant solvents is required to understand QDs diffusivity within the CNS. A methodology based on dynamic light scattering (DLS) was employed to assay QD hydrodynamic diameter, where increase in particle size served as an indicator of QD aggregation and instability. Time dependent stability was investigated by incubating QDs in 1xPBS, a low concentration salt solution, and artificial cerebrospinal fluid (aCSF), a more complex medium that mimics in vivo interstitial fluid. Measurements were recorded for 24 h at both room and physiological temperatures. Compared to 1xPBS, QDs without poly(ethylene glycol) (PEG) ligands showed more rapid and significant aggregation in aCSF while PEGylated QDs remains relatively stable in both mediums. To explore the rate at which QDs initially aggregate, particle size was measured for the first 200 seconds post-incubation in solutions of varying concentrations of calcium ion, an ion which plays an important role in many neuronal processes and is often found in abnormal amounts in the brain in diseased states. The results showed that the rate of initial aggregation is concentration dependent and is diminished by PEGylation. With further analysis of QD stability in CNS relevant mediums, this methodology will aid in establishing key properties that govern QD behavior in the brain microenvironment, which can be used to employ QDs as an effective biomarker for brain diseases.

Chalcopyrite copper iron sulfide (CuFeS2) nanocrystals have gained recent interest due to their metal-like optical response, despite their complete lack of free charge carriers. These novel optical characteristics make CuFeS2 an intriguing material for a variety of applications, including photovoltaics and photothermal applications. Unfortunately, synthesis of high quality CuFeS2 nanoparticles is difficult due to the difference in the reactivity of the two cations, often resulting in binary or polydisperse nanocrystals. To gain better control over nanocrystal morphology, we synthesized CuFeS2 nanocrystals via a hot injection method and studied the effect of the cation precursor ratio on the resulting properties of the nanocrystals. Transmission electron microscopy, X-ray diffraction, and UV-vis-NIR spectroscopy were used to analyze the nanocrystal composition, morphology, and optical characteristics. Based on the characterization data, a 1:2 molar ratio of copper to iron precursor was optimal for producing high quality, monodisperse CuFeS2 nanocrystals. These developments in morphological control will aid in future studies of quasi-static resonances in these materials.

Current diagnostic techniques for bacterial infections are time-consuming, and lead to over prescription of wide spectrum antibiotics. Surface plasmon resonance (SPR)-based biosensors can be used to reduce these diagnostic times, but often suffer from diffusion-limited mass transport. Dielectrophoresis (DEP), the movement of dielectrically polarized particles due to application of electrical inhomogeneous fields, can be used to overcome these mass transfer limitations in SPR devices. Current DEP-enhanced SPR devices separate particles with different dielectric properties by generating an electric field gradient and inducing positive DEP (p-DEP) force and negative DEP (n-DEP) force simultaneously. Depending on the dielectric properties of the particles, the particles are collected either in negative DEP at the center of electrodes or in the positive DEP at the edges. My research focuses on fabricating DEP-enhanced SPR biosensors for rapid detection and separation of pathogenic bacteria. Interdigitated electrodes are chosen to reduce detection limits and increase sensitivity. The structures of electrodes are fabricated by soft lithography techniques. To optimize the performance of DEP, electrode patterns with various gap widths are tested. Ultimately, the DEP-enhanced SPR sensors developed in my research could serve as a powerful diagnostic platform for rapid and sensitive detection of pathogenic bacteria by reducing the time of diagnosis and minimizing morbidity and mortality from infectious diseases.

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.

Traditionally, drugs intended for use in the central nervous system (CNS) have lower success rates than drugs used to treat any other part of the body, with very little drug reaching target disease cells in the brain. This limited success is brought about by several physiological barriers, including the blood-brain barrier (BBB), the blood-cerebrospinal fluid barrier, and the highly dynamic and densely packed brain microenvironment. Nanoparticles, if designed properly, have been shown to bypass the BBB and diffuse into the brain extracellular space (ECS), making them a promising option as drug delivery vehicles for CNS diseases moving forward. However, evaluating a nanoparticle’s ability to overcome these barriers both in vivo and ex vivo remains difficult because the brain microenvironment is dynamic, heterogeneous, and variable from brain to brain. To combat this, we aim to develop a gel model of the brain microenvironment that can be used to assess the diffusive ability of nanoparticles in vitro. The model consists of a low concentration (0.4%) agarose gel loaded with proteins found within the brain ECS. To ensure the method used to construct the gels is both controllable and reproducible, characterization of the gels was carried out using a combination of rheological and multiple particle tracking experiments. Rheological experiments determine the gel’s bulk mechanical properties, while multiple particle tracking using probe nanoparticles characterized the gel’s pore size distribution. The insight gained from these characterization techniques allows us to make more informed predictions of how various nanoparticles will diffuse when loaded into these gels.

Nanowires have shown significant promise as high-capacity, conversion-type lithium-ion battery negative electrodes. Investigating the local properties of these materials during cycling has primarily been done via in-situ transmission electron microscopy or synchrotron-based techniques. Both techniques require highly specialized equipment that is not readily available. Atomic force microscope (AFM)-based measurements of electronic and ionic transport offer another alternative. However, analyzing these electrode materials via AFM has proven difficult due to the large surface variations in Z-height and the flexibility of the wires, which can trap and damage AFM tips. Therefore, sample preparation becomes critical. In this study we screened a variety of preparation methods including epoxies and resins and from those results, determined successful methods to prepare and ultramicrotome samples to create thin slices of electrode that can support analysis via AFM. These images allow for the elucidation of surface characteristics to support future surface functionalization and show that AFM can be applied to the imaging of these types of electrode materials to obtain nanoscale properties. A stronger understanding of local properties in these materials is critical to future developments that are highly anisotropic and require nanostructures.