Organic photovoltaic (OPV) cells are an emerging technology that is experiencing continued breakthroughs such as reaching a power conversion efficiency (PCE) of 17.3% in August, 2018. OPVs have the potential to become a major source of energy in our future and a more sustainable energy option than traditional solar cells. In addition to contributing a lower environmental impact than common silicon-based solar cells, OPV cells can be made to be flexible, lightweight, and are comparably inexpensive to fabricate. They are also quite customizable via molecular engineering providing the opportunity for much novel architecture. Our research team focuses on innovating a modular processing system for OPV cells in the form of multi-component fibers by continuously coating device layers onto wires and winding the fiber with a secondary electrode. Using a small, user-friendly system allows us to focus on the most important factors that affect the morphology and PCE of the resulting OPV fiber. After characterizing the fibers we are able to consider what changes need to be made to the modular system, allowing us to better advise on the design of a larger-scale manufacturing process for organic photovoltaic fibers.

Hybrid organic inorganic perovskites are a promising highly efficient photovoltaic material that can be solution processed at low temperatures enabling an inexpensive solution to rising renewable energy demands with high-volume, scalable manufacturing of solar cells. Small scale perovskite devices are successful using spin coating; however, this needs to translate to larger scale deposition systems such as roll-to-roll slot die printing. Understanding the crystallization and morphology dependence of these materials is essential to enabling slot-die coated perovskite films on scalable systems and transitioning this technology to the market. In order to model crystallization rates of printed layers, we used in-situ optical and photoluminescence microscopy during printing of perovksite films to determine crystal growth rates and evaluate perovskite conversion. Printing parameters were manipulated through variation of temperature, atmospheric conditions, ink recipes, and substrate surface energy generating a model to achieve desired grain size and morphology of the perovskite layer across an array of relevant potential perovskite photovoltaic device stacks. Following classical models, we determined the necessary parameters to translate these fundamentals to perovskite crystallization and grain growth. We further explored the conversion and degradation of the perovskite phases through the printing process, which plays a significant role in device performance, through in situ photoluminescence microscopy, as well as verification through X-ray diffraction. Verification of the observed grain sizes and morphology was also done through scanning electron microscopy, to ensure optical measurements and analysis were accurate. With efficiencies of perovskites approaching current industry standards of silicon, perovskites are increasingly becoming the clear answer to solar industry demands. This research is essential in enabling scalable methods with the potential to revolutionize the solar industry with large scale fully printable devices.

The cooling process for aluminum cast molded parts impacts the material properties and quality of the final product.  Understanding what microstructure and properties produced by a given mold is critical for the mass production and design of consumer and industrial parts.  This research analyzes the cooling of a pure aluminum cast molded part under variable corner geometry, in order to predict properties of the finished aluminum product.  This project goal was to create a flexible, three dimensional model of the cooling process for an aluminum cast part, using finite volume analysis. The model takes into account both static and dynamic material states and properties.  The model's goal was to be robust and flexible enough to be utilized for a wide range of material properties, as well as various corner geometries, including chamfer, fillet and a 90˚ corner.  In order to verify the accuracy of the model, and assess microstructural effects, the cast geomtry has been tested with a real mold and aluminum.  Temperature readings for the mold are taken in order to assess the models accuracy.  We predicted that the fillet geometry will cool the slowest of the three geometries, with the chamfer being second and the 90˚ corner being the fastest. With this in mind we predicted that the fillet will have the the greatest cooling continuity, with the chamfer being second and the 90˚ corner being last.

Natural dermal armors are inspiring the development of advanced engineering materials and next generation flexible armors. Fish scales are an exemplary candidate and consist largely of laminated plies of unidirectional type I collagen fibrils. The mechanical properties of fish scales depend on the interpeptide bonds within the triple helix of the collagen fibrils. Adjusting the strength of these bonds to change the performance of the scales has applications to the design and functionality of bioinspired flexible armors. Here, elasmodine scales were exposed to polar solvents to adjust the extent of intermolecular bonding. Changes in the mechanical properties were evaluated in uniaxial tension and at two different strain rates. Results showed that the constitutive behavior was highly dependent on the intermolecular bonds. A significant increase was observed in elastic modulus (stiffness), strength and toughness as a result of increasing the extent of interpeptide bonding via solvents with low affinity for hydrogen bonding. A 300% increase was seen in the elastic modulus of scales soaked in acetone compared to HBSS at the highest strain rate. Furthermore, the importance of interfibril bonding was dependent on loading rate. Overall, results showed that the “protecto-flexibility” of fibrous armor materials can be improved by activating interfibril bonds and that this could spawn approaches for tuning armor performance.

The goal of this project is to encode peptides, i.e., short amino acid sequences, in terms of smaller molecular components such as their VSEPR (Valence Shell Electron Pair Repulsion) features for training interpretable models with reasonable predictability of functionality. This enables us to go beyond the limitations imposed by treating peptides as sequences of letters, thereby enabling a generalized encoding that works for lipids and other biomolecules that are of interest in a comparable scenario. Biological processes are rarely disjoint and often complicated which lends justification to our approach. Current methods for binding affinity prediction, such as one-hot encoding, where letter-based sequences are converted to a binary representation, do not take into account molecular level features. Combined with a neural network, such a simple encoding is better at predicting affinities of short peptides, e.g., 5-9 Amino acids long, but with an increase in length from 9 to 10, the predictability suffers an exponential drop. Several alternatives have been employed in literature, but they also suffer from the negative impact of distal effects. In the VSEPR approach, encoding peptides in terms of their component functional-group geometries enables us to encode the actual physical length, rather than the number of amino acids. This leads to an overlap between peptides of different length, thereby reducing the fall in predictability. In this encoding, we create 5 channeled matrices with each channel corresponding to ‘central-atom connectivity’, ‘bond-types’, ‘bond-lengths’, ‘bond-angles’ and ‘lone-pairs’ that is then fed through a Deep Residual-Neural-Network. The metrics used to evaluate the models are Pearson-Correlation, Spearman-Rank-Correlation-Coefficient, and Area-under-Receiver-Operating-Curve. With this technique, we were able to consistently predict binding affinities of peptides without an appreciable loss between 9 or 10 length peptides. This method would allow one to create length invariant encodings, not limited to just peptides, significantly improving the practicality of using such a model. The research is supported by NSF/DMR-DMREF program under Materials Genome Initiative.

Our laboratory, GEMSEC, which operates at the intersections of biology-materials-informatics fields, is developing materials and methods to seamlessly bridge biology with solid-state devices towards establishing the foundations of future hybrid devices, e.g., bioelectronics, bionanosensors, and biomolecular fuel cells. Towards this goal, we use the smallest functional biomolecule, peptide, combined with the smallest functional solid in materials science, i.e., single atomic layer materials. Herein, we study the interactions of genetically designed peptides with surfaces of graphene, a semimetal. A phage display library-selected peptide, GrBP5, is a graphene-binding dodecapeptide that has a wide range of applications. Since peptides have short amino acid sequences, they are known to display intrinsically disordered structures in solution. Here we study the conformational propensities of the WT peptide and its rationally designed mutants under a variety of experimental conditions (pH, concentration, temperature, time, etc.) to understand their behavior on solid surfaces that includes surface phenomena from binding, surface diffusion, intermolecular interaction and self-organization. Molecular dynamics (MD) simulations of WT-GrBP5 and its mutants have been completed in water and on graphene for 200ns, 20,000 timeframes under different temperatures and pH values that range from 5 to 55 oC and 3.5 to 10.0, respectively. The analyses, including the RMSD maps and Ramachandran plots, show explicit folding propensities, stable and unstable structures, for a given sequence under a given set of experimental conditions. The computational modeling, backed up by experimental validations carried out under similar conditions, are leading to the design of novel peptide sequences with predictable behavior under desired environmental conditions. The fundamental understanding of the differences in conformational behavior of GrBP5 mutants are now extended to other solid-binding peptides that are specific to semiconductor and insulator single layer materials providing the much essential information for the design of hybrid devices of the future. The research supported by NSF/DMR-DMREF program.

The Genetically Engineered Materials Science and Engineering Center (GEMSEC) labs revolve around designing and synthesizing genetically engineered peptides for inorganic materials (GEPIs). Experimentally characterizing GEPIs can be slow, and therefore a computational method that can predict functionalities would greatly accelerate the development of bio/inorganic interface design and implementations. The Pairwise Similarity Score is a proven predictor of relative binding affinity and has been used to predict GEPIs specific for quartz, gold, hydroxyapatite, and MoS2. In previous work, a similarity matrix was updated based on whether a peptide (Strong or Weak binding) had higher similarity to strong peptides and less similarity with weak peptides. Our method instead obtains the most ideal similarity matrix via stochastic gradient descent to best predict the relative binding affinities. The values in an amino-acid similarity matrix are randomly initialized and subsequently updated until convergence by minimizing the errors in binding affinity prediction. 5-fold cross-validation is used as a metric to evaluate performance on test data. We expect to observe higher predictability with this learned similarity matrix than using a literature matrix. This would compound work done by the high throughput screening, confirming count numbers observed during phage display are correlated with their actual binding affinity, while using a novel large dataset to test known successful predictive models. All in all, the work carried out in this project accelerates the development pace of bio-nano-devices of the future. The research is supported by NSF/DMR-DMREF program under the Materials Genome Initiative.

Solar energy has unmatched potential as the energy source of the future and semiconducting polymers (SP) offer a unique set of properties that can address many of the current barriers that restrict solar technology. SP are exciting because they have untapped potential for improvements in efficiency and they offer a cheap, energy-efficient alternative to silicon due to the ability to scale their production to industrial applications via film deposition techniques, like roll-to-roll printing. Solution processing via roll-to-roll printing is transformative, allowing for low-energy, high-throughput manufacturing of flexible devices. Previous work by Tatum and Resing investigated crystallinity in SP film microstructures through the self-assembly of highly ordered nanowires. This project expands upon this by utilizing a Python classification program to generalize relationships between morphology, optoelectronic properties and processing conditions of organic photovoltaics (OPV). Films of these materials will eventually enable stretchable and deformable electronic devices, but the nano- and microstructures are currently stochastic and inconsistent in their morphologies and resulting properties because processing and chemical conditions influence the domain size of the components and the distribution of those domains throughout the film. Using atomic force microscopy (AFM), a relatively cheap and quick technique, the active layer domains have been spatially resolved based on differences in their mechanical properties. These properties are strongly correlated to electronic performance factors such as fill-factor, short-circuit current and open-circuit current. For this project, OPV with an active layer of Poly(3-hexylthiophene):Phenyl-C61-butyric acid methyl ester has been fabricated with systematically varied processing conditions. A library of data has been established, containing AFM images, the device morphology and OPV performance data. This experimental data set of unprecedented compositional resolution aids in the evaluation of cutting edge simulation techniques, creating a more accurate computerized simulation model for OPV. 

Glioblastoma multiforme (GBM) is a highly aggressive variant of brain cancer that has been a focal point of chemotherapeutic development for years. However, initial drug screening using traditional in vitro culture of GBM cells frequently produces encouraging results that do not translate well to animal models and clinical application. To address this disparity, implementation of three-dimensional tumor modeling can better emulate the microenvironment that tumor cells experience in situ, improving accuracy of early in vitro screening. We developed two chitosan-based polymer blends to produce biocompatible, porous scaffolds that mimic the extracellular matrix and promote cell adhesion. Scaffold production was done in 96-well cell culture plates for high-throughput drug screening with a large sample size. These scaffolds were used to grow human GBM cell lines U-118 MG, U-87 MG and GBM6 for 14 days, confirming cell compatibility with the materials and promoting formation of tumor spheroids. The cultures were treated with the established chemotherapeutic agent temozolomide (TMZ) for 72 hours, and cells were then tested for metabolic activity using the Alamar Blue resazurin assay. We demonstrated increased resistance to chemotherapeutics in cells with this induced morphology relative to cells grown in two-dimensions for all cell lines and both scaffold compositions. Additionally, based on gene and protein expression analysis, GBM cell spheroids more strongly expressed cancer stem cell characteristics and greater malignancy. The presence of GBM resistance to chemotherapy and enhanced characteristics associated with in situ tumors indicates the potential of using chitosan-based tissue scaffolds for more accurate high-throughput screening of novel GBM treatments.

Nuclear magnetic resonance (MR) is a phenomenon which may be harnessed to provide high resolution images of the soft tissues of the body and aid in the diagnosis of many diseases. MR imaging relies on measuring the alignment, perturbation, and realignment of the magnetic dipole moments of hydrogen nuclei composing water molecules. Differing rates of realignment, or relaxation, of the magnetic moments of the hydrogen nuclei after perturbation creates contrast in MR images. This contrast can be enhanced by the introduction of magnetic field disturbances in the vicinity of hydrogen atoms. Clinically, contrast enhancement in MR imaging is achieved with chelates of the strongly paramagnetic metal, gadolinium. However, increasing evidence indicates that gadolinium can cause nephrogenic systemic fibrosis in patients with renal damage. Iron oxide nanoparticles (NPs) may be safer alternatives than gadolinium-based contrast agents given iron’s biodegradability and physiological role in hemoglobin. This research optimizes iron oxide NPs for use as contrast agents in MR imaging. We evaluate two important MR imaging parameters, the transverse and longitudinal relaxivity, of iron oxide NPs as a function of core size at two different magnetic field strengths. Our findings show that both the transverse and longitudinal relaxivities of iron oxide NPs decrease with decreasing core size at a low field strength, but transverse relaxivity decreases while longitudinal relaxivity increases at high field strength. Furthermore, we find that the transverse relaxivity component is more strongly influenced by core size than the longitudinal relaxivity. These trends in MR parameters as a function of core size will allow for the optimization of iron oxide NP as contrast agents for MR imaging.

Magnetic Resonance Imaging (MRI) is an invaluable imagining modality that allows physicians to quickly analyze the anatomical and physiological processes of the body. To improve contrast efficiency, MRI contrast agents are frequently administered to patients. However, the most commonly used T1 contrast agent – gadolinium-based contrast agent (GBCA) – has been associated with many adverse health effects, such as reduced white blood cell count, increased serum levels of inflammatory cytokines, and vacuolar degeneration. As such, there is an urgent need to design a T1 contrast agent that has a short half-life and that does not produce toxic endpoints in patients. To address this need, the present study developed an iron-oxide nanoparticle (IONP)-based T1 contrast agent by synthesizing 4 nm oleic acid-coated IONP and reacting them with phosphine oxide-PEG. Next, this study characterized the biological and the magnetic properties of the generated IONP system in vitro in order to determine its suitability as a T1 contrast agent. Finally, this study also analyzed the synthesized contrast agent’s use in other biomedical applications, specifically targeted drug delivery. Previous research has shown that cells can undergo ferroptosis – a regulated form of cell death – upon loss of activity of the lipid repair enzyme glutathione peroxidase 4 (GPX4). As such, a silencing RNA (siRNA) that can mark GPX4 mRNA for degradation was conjugated to these IONPs in order to downregulate GPX4 expression in mesenchymal stem cell – a type of stem cell that are particularly involved in cancer progression and that are especially resistant to radiotherapy – and sensitize them to radiotherapy. Ultimately, the findings of this study not only offer a safer alternative to GBCAs but also provide a foundation for a versatile, tunable IONP system that can be used in a variety of biomedical settings.

Nanoparticles have been touted to exhibit extraordinary properties, which could ultimately reverse environmental damage thought to be irreparable. However, is their synthesis process scalable? What are their macro-scale impacts? This life cycle analysis looks at the environmental impacts of producing iron-oxide nanoparticles used as an additive to help detect and track gastrointestinal gut bleeding to the microscale. It discusses the environmental impacts of using a nano-scale technology. An attributional life cycle inventory model with geographic specificity in Seattle, WA has been conducted. Data sources include the US GREET database and FineChem, along with the published results in "Synthesis of phase-pure and monodisperse iron oxide nanoparticles by thermal decomposition" in Kemp et al.

The impacts assessed include contribution to climate change, water consumption, resource consumption, and energy consumption. ReCiPe will be used for midpoint and endpoint charaterization. Preliminary results show that in this case, the uncertainty related with required dosage size of nanoparticles for this specific application yielded a large variance for potential impact. However, using the concept of disability-adjusted life years (DALY), it is shown that this technology provides a net benefit for human health.

Our Lab, GEMSEC, uses molecular biology, bioinformatics, genome sciences, and engineering for de novo design of short amino acid sequences for various applications such as tooth remineralization strategies in dentistry, biosensing in cancer diagnostics, and bioelectronics in single-molecule detection. Designing and constructing peptides for a desired function begins with selecting the appropriate sequence of amino acids with the predictive conformation that affects the function. In this project we use latent-space representation (matrix factorization) in conjunction with a simple neural network to create a model that is able to predict peptide binding affinity to several alleles of MHC-I protein. Python was used to encode amino acids by creating data frames defining the functional groups within them, differing by n-terminus, intermediate, and c-terminus of each amino acid and their placement along the backbone of each structure. A tensor was created using the data frames describing each amino acid to encode the 9- and 10-length sequences of thousands of unique peptides from the Immune Epitope Database. Each chemical structure and peptide sequence can be described by k attributes, or latent features. Matrix factorization was used to discover the latent features and send this feature encoding to a neural network (NN) to determine binding affinity. The goal is to minimize the mean-squared-error by stochastic gradient descent in a supervised learning protocol. The two modules of matrix factorization and NN provide an optimum between interpretability and predictability simultaneously.The successful prediction of peptide binding affinity towards nanoscale targets provides novel opportunities for drug design towards targeted public health initiatives and in technology applications such as bio/nano hybrid devices. This research is supported by NSF/DMR-DMREF program under the Materials Genome Initiative.

Biological mineralization is the formation of minerals in hard tissues guided by proteins. Unique aspects of these minerals include the molecular control of hierarchical structure, intricate architectures, and multifunctional properties for inspiration in bionanotechnology and nanomedicine applications. Numerous biomineralization strategies have been developed in hard tissue regeneration therapies. However, there is currently no in-depth understanding of how proteins regulate the synthesis of these inorganics or the physiological formation of the minerals. The ability to control mineral formation for biomedical applications, therefore, is still limited to the use of a few mineral-directing proteins extracted from tissues. Biomineralization can also be controlled using short peptide domains derived from natural proteins known to have a regulatory role in mineralization. Our laboratory has designed peptides derived from amelogenin (ADPs), the key protein in tooth formation, using combinatorial selection and computational design, whose utility in rebuilding hydroxyapatite (HAp) mineral on tooth has been demonstrated in numerous case studies. The goal here is to understand the fundamental mechanisms of biomineralization guided by ADP5 and develop a methodology to form HAp with exclusive control of its growth kinetics and mineral crystallography. We designed mutants of ADP5 to investigate changes in mineralization kinetics, nucleation, and morphology. In the current study, we are establishing the conditions for ion-peptide interactions on the onset pH for mineral nucleation using calcium/phosphate and mutant ADPs. The goal is to gain insights into the correlation between sequence domains and biomineralization outcomes eventually facilitating greater control over the reaction and further optimize remineralization approach. The developed method has a high potential to develop non-invasive oral health care materials and methods by restoring mineral loss, the root cause of dental ailments and, eventually, help bring clinical and over-the-counter dental products into the market with preventive, restorative, therapeutic, and cosmetic characteristics. Sponsored by SoD Spencer Funds.

Whispering Gallery Mode (WGM) sensors have unprecedented sensitivity in the optical detection of label-free biomolecules. These sensors can detect surface adsorption and have been used to detect single molecule adsorption and interaction processes. By observing resonance shifts during molecular interactions, WGM sensors can characterize a molecule’s surface adsorption. The goal of this project is to develop a robust WGM dip sensor array controlled by a three-axis stage in order to perform high-throughput characterization of peptide binding and adsorption within a 96-well plate format. The peak of spectral absorbance is the WGM resonance, and as this changes with surface adsorption we measured a spectral shift. Using this spectral shift in combination with the known concentration of our peptide species, we determined binding kinetics. The WGM sensor was used to characterize different peptide sequences to further understand the effects of peptide mutations on binding kinetics. A single microsphere resonator was used as proof of principle and will eventually be adapted to an array of eight WGM microsphere resonators to generate large amounts of data. This high throughput approach will provide the much needed large amount of quality data that is necessary for the development and adaptation of machine learning and applied statistical analysis algorithms toward the eventual development of artificial intelligence platforms in material science. The project is supported by NSF-DMREF through the Materials Genome Initiative.

The study of peptide self-assembly on solid surfaces has the potential to catalyze numerous nanotechnological advances such as biosensors and nanoelectronics. A comprehensive understanding of the factors that influence peptide binding to solids would allow for expansive integration of biomolecules and solid-state devices, and organic-inorganic interface bridging will permit greater information flow between biological systems and technological devices. The Genetically Engineered Materials Science (GEMSEC) lab has engineered peptides that are capable of binding to substrates such as graphene, monolayer molybdenum disulfide, and boron nitride nanosheets. Utilizing experimentally determined binding affinities of these binding peptides alongside a database of biochemical and physicochemical properties of amino acids, we have developed a method to computationally predict short amino acid sequences that preferentially bind to atomically flat surfaces. Matrix factorization and linear regression is used to train a model capable of predicting an experimentally observed peptide count number (observed during sequencing of eluate of phage display biopanning) from 8 Total Similarity Scores (TSS) that are calculated from 8 novel similarity matrices. This model is then used to predict the ranking of actual binding affinities of genetically engineered peptides to monolayer molybdenum disulfide from fluorescence microscopy experiments. The ability to predict solid binding by peptides will facilitate further research into peptide structure and functional properties upon adsorption. Ultimately, these methods will be implemented in a cohesive software platform using machine learning and signal processing tools to allow determination of sequence-property linkages and pattern recognition in a larger bioinformatics context, and allow for nanomedical and nanotechnological advances at the intersection of materials science, biology, and genetics. Supported by the NSF-DMREF program through the Materials Research Initiative.