Small-angle neutron scattering (SANS) is a powerful tool that provides access to molecular-level structure in a variety of material, such as soft matter, colloids, and biomolecules. Currently, researchers must follow a manual and inefficient procedure to reduce and analyze data due to inconsistent meta-data storage and varying reduction processes. Thus, our project is aimed to improve the automated reduction of SANS data and streamline data flow at the experiment site by the use of machine learning (ML) and data science techniques. We propose a machine learning classification algorithm, trained on a historical SANS dataset, to automatically sort raw data into six categories of scattering profiles and relevant background measurements required for reduction. The first stage of our research project uses a feature reduction model to create a subset of most significant and relevant meta-data in the historical scattering data set. After pre-processing, we then explore different ML models (e.g. neural networks) to sort the data in a comprehensive, accessible, and maintainable form that can be easily applied to sorting data directly from experiment sites. In addition, we incorporate a Postgres database to improve data management as well as an interactive front-end to enable user interaction. Currently, we are building meaningful and useful data processing and management methods by starting with a small SANS dataset from the Pozzo Research Group at the University of Washington. In the future, these methods will be expanded to larger historical SANS datasets to train our machine learning models. By developing this machine learning algorithm that can utilize experimental information and scattering patterns for auto-labeling of raw data and relevant meta-data, we will enable more efficient storage, reduction, and analysis of SANS data for researchers in the future.

The delivery of cell and drug-based chemotherapeutics to tumors have presented major challenges in effective cancer treatment. Opportunities to improve current small molecule drug delivery systems exist by increasing overall delivery specificity and decreasing harmful off-target effects. Towards this, we have recently developed a chemical framework for creating user-programmable hydrogels that undergo programmed degradation in response to multiple environmental cues following Boolean logic. Exploiting this methodology, user-specified combinations of environmental inputs (e.g., tumor-presented enzymes, reducing conditions) yield material breakdown and accompanying therapeutic release. To translate these materials for chemotherapeutic delivery in vivo, we established strategies to formulate these stimuli-sensitive materials into nanogels that circulate in the bloodstream before acting on the desired target site. We developed techniques to formulate gels on the 50-250 nanometers size scale, one which should enable circulation in the blood and uptake within tumors based on the enhanced permeability and retention effect. Different ultrasonication conditions allowed us to tune nanogel, size and dispersity. This system is scalable, translational, and simple to recreate. In the future, these materials can effectively hone and selectively deploy small molecule chemotherapeutics to tumors in patients.

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.

Precise spatiotemporal control over biochemical cue presentation is necessary to mimic the complex, heterogenous environments found in biological systems. Achieving this level of control within engineered microenvironments would allow for the manipulation of cell growth and differentiation, which could be utilized in tissue engineering and drug delivery. To this end, we developed a method that utilizes fusion proteins made from a novel PhotoCleavable protein linker (PhoCl) and a protein of interest (POI). This method allows for spatiotemporal control of POI release from hydrogels in response to cytocompatible violet light (λ = 405). This system is flexible, as PhoCl can be conjugated to many different POIs, including fluorescent proteins, enzymes, and growth factors, and was found to not affect protein function. Additionally, PhoCl undergoes a green-to-red transition after photocleavage, allowing for real-time tracking and quantification of POI release. As PhoCl cleaves in response to visible light, which is less damaging to cell function and has a greater tissue penetration depth than the traditionally used UV light, PhoCl fusion proteins hold promise for use in vivo. To demonstrate the feasibility of this system, PhoCl fusion proteins were formed with several fluorescent proteins (e.g., mRuby, sfGFP, mCerulean). Conjugating these fusion proteins into gels and exposing them to patterned light produced spatiotemporal localized release of proteins with micron scale resolution, which was demonstrated through fluorescent imaging of the photopatterned gels. To support the potential in vivo applications of this system, PhoCl was also used in mammalian cell studies with epidermal growth factor (EGF). These studies showed the expected increased cell growth in response to photomediated EGF release. This illustrates the potential versatility of the PhoCl system in biological applications, thus supporting the relevance of this novel system to tissue engineering and drug delivery methods.

The most effective semiconductors used as absorber layers for solar cells have concerns regarding high capital expenditure (CapEx) for new manufacturing facilities, earth abundance, toxicity, or cost-volatility of the materials. Solution processing is a low cost, low temperature development method leading to lower CapEx. The exploration of new photovoltaic materials seeks to develop an earth abundant, non toxic semiconductor via solution processing with efficiencies comparable to materials like silicon or CdTe. Bismuth rudorffites (chemical formula A_aBi_bX_a+3b) are an interesting category of new materials, proven to be solution processable, to have high absorption, and to be capable of cell efficiencies over 4%. My project seeks to optimize the thin-film morphology and the open circuit voltage (V_oc) of bismuth rudorffite layers, both of which are crucial to achieving high efficiencies. A good morphology will be phase-pure and densely packed, with large grains. By determining the effects of each parameter of the thin-film deposition process (spin-coating, in our case) through Scanning-Electron Microscope imaging and X-Ray Diffractometry, I have determined an optimized deposition procedure leading to good morphology. The utilization of Absolute Intensity Photoluminescence techniques (AIPL) allows for prediction of the V_oc to a high degree of precision without building an entire solar cell, instead only measuring the absorber layer. By illuminating the absorber and detecting the re-emitted light, models can determine the density of "radiative recombinations" of electrons and holes, which correspond to electrons that would be capable of generating a voltage and providing electrical power. By using this method and by building an understanding of rudorffite crystal growth, I have attempted to reduce "non-radiative recombinations," increasing the PL and hence increasing the capacity for high V_oc in rudorffite cells. Here is presented current data, results, and recommended experiments necessary for rudorffites to be a successful photovoltaic material.

Nearly 22 million HIV-positive people are receiving antiretroviral therapy in order to suppress their HIV infections. They need consistent viral load monitoring to track viral suppression and detect the possibility of viral rebound. Nucleic acid amplification tests (NAATs) are used to measure the viral load in a patient's blood. Traditional, laboratory-based NAATs require complex robotic systems to automate HIV RNA purification, amplification, and detection from blood. Since the majority of those living with HIV are located in low and middle income countries, there is a need for rapid viral load monitoring at the point of care (POC). We aim to provide accessible HIV viral load testing through low-cost, integrated POC NAAT devices. These proof-of-concept devices operate as a two-step assay to extract and detect nucliec acids in blood. An electrophoretic separation technique called isotachophoresis (ITP) separates HIV RNA from other components in a blood sample. An isothermal nucleic acid amplification assay amplifies the purified, concentrated nucleic acids in order to detect and quantify their presence. We present our development of a novel ITP system to remove potent contaminants from Proteinase K (PK) digested serum and extract highly pure nucleic acids automatically. Through computational modelling, a dual trailing electrolyte (TE) buffer system was designed to exploit the isoelectric point of PK for its removal, while simultaneously concentrating nucleic acids away from serum components. We demonstrate system control through comparison of experimental observations to model predictions by performing dual-TE ITP on pH paper. We also show that the dual-TE system improves upon previous limits of detection for DNA extraction and detection from complex samples. Our system processes 40 microliters of blood in 20 minutes using only simple buffers, a paper strip and an electric field - making it an ideal tool for use in a rapid NAAT for HIV viral load testing. 

Developing treatments for complex disease is hindered by a variety of obstacles including clearance by the reticuloendothelial system, degradative proteases of blood and tissue microenvironments, and overall limited tissue penetration. Nanoparticles hold potential as drug delivery platforms for overcoming these problems, where they can be used to encapsulate and protect enzyme therapeutics and improve delivery to specific organs of the body. Using poly(lactic-co-glycolic acid) (PLGA) polymeric nanoparticles with a poly(ethylene glycol) (PEG) coating and catalase as a model enzyme, we compared two methods of nanoparticle formulations for enzyme encapsulation: nanoprecipitation and double emulsion. We tested the effects of F127 (Pluronic F127), P80 (polysorbate 80), and PVA (poly(vinyl alcohol)) surfactants on the size, polydispersity index (PdI), zeta potential, and enzymatic activity of nanoparticles formulated via nanoprecipitation. We determined PVA to be the ideal surfactant with the greatest enzyme loading for nanoprecipitation (~3.6%), similar to the activity obtained from double emulsion (~3.7%). For double emulsion, we determined similarly that PVA worked the best out of the surfactants tested. However, the nanoprecipitation method provided little to no enzyme protection in the presence of a protease with complete deactivation within 4 hours, while double emulsion nanoparticles extended activity through 24 hours (~6.6% of original activity). We further optimized the double emulsion method by altering the sonication times, determining that a sonication time of 15 seconds yielded particles with an activity of ~3.5% while maintaining similar size, zeta potential, and PdI. We also found that sonication conditions significantly affected enzyme deactivation. With a modified bicinchoninic acid assay, we measured total protein concentration within the particles, allowing us to calculate enzyme percent deactivation from formulation processes. Altogether, our work shows that enzyme-loaded nanoparticles made via the double emulsion method achieve high encapsulation and protection of enzymatic cargo for drug delivery.

As the global burden of neurological diseases continues to grow each year, there exists a need for drug delivery vehicles that can overcome barriers specific to the brain. The heterogeneous brain extracellular matrix (ECM) is an understudied barrier to effective therapeutic delivery, particularly in the presence of disease states. We utilize a novel multiple particle tracking (MPT) approach to characterize microstructural changes in perineuronal nets (PNNs), a key structural mediator of plasticity, in the developing brain. Our overarching goal is to develop a combined approach of MPT, statistical analysis using Python-based software packages, immunohistochemistry (IHC), and mRNA expression profiles to monitor changes in PNN structure and function through development and in the presence of neuroinflammation and oxidative stress processes. For this, we cultured 300 μm-thick organotypic whole hemisphere (OWH) brain slices prepared from postnatal day 35 (P35) rats. We treated slices with 100 ng/mL lipopolysaccharide (LPS, induced-inflammation model) or 100mM glutamate (MSG, induced-excitotoxicity model) for 3 h, removed the toxin and fixed slices at 1 h, 6 h, and 24 h after toxin removal. Fixed slices were stained using Wisteria floribunda agglutinin (WFA) and PNN counts were quantified using the ImageJ software package. In LPS and MSG-treated slices, 100-nm particles were added to live slices stained with WFA and MPT was performed in WFA+ regions, followed by statistical analysis of individual nanoparticle trajectories. From the IHC imaging and PNN count quantification, we observed differences in the number and morphology of PNNs present in the MSG and LPS models when compared to healthy controls. Characterizing structural changes in PNNs in living tissue furthers our understanding of the impact of neuroinflammation and oxidative stress on neuronal plasticity, and the subsequent impact on progression of neuropsychiatric diseases.

BiI₃ is a nontoxic and relatively abundant material with a bandgap of ~1.8eV that displays promising photovoltaic properties. However, BiI₃ solar cells have not reached efficiencies of greater than 1%. One of the major problems is the existence of deep defects in the materials which serve as trap states and increase non-radiative recombination. Here, defect engineering by both isoelectronic and non-isoelectronic doping of BiI₃ is studied to reduce the concentration of deep defects and increase the concentration of free charge carriers, aiming to improve the photovoltaic properties of BiI₃ solar cells. To evaluate the effect of dopants on defect passivation in BiI₃ films, photoluminescence and photoconductivity measurement are applied to determine quasi-fermi level splitting (QFLS) and carrier diffusion lengths, respectively. Additionally, dark photoconductivity measurements are used to determine carrier concentration. Preliminary results have shown an increase in QFLS under an iodine rich environment, though not significantly enough to drastically impact BiI₃ solar cell performance. There is much room for further exploration of dopants and their effect on BiI₃. With every dopant tested we learn more about the properties of BiI₃. This work will deepen our understanding of optoelectronic physics and defect chemistry of BiI₃ solar cell materials, optimize the quality of BiI₃ thin films, and increase the efficiency of BiI₃ solar cells.

Computer vision models are used to help analyze biomedical images for diagnosis and treatment through looking for differences between images by a comparison to a template image. For instance, optical coherence tomography (OCT) is used to diagnose and treat retinal issues. When looking at the brain, injury, cellular uptake and characteristic features vary across regions, therefore images are often segmented into established brain regions to determine how the brain is impacted in a particular study. Current models fail to work in segmenting brain regions because each brain has variation in local microstructure, making it difficult to compare one brain to another. Furthermore, when brains are sliced, the exact location within the brain can be difficult to pinpoint, particularly in regard to depth, because the regions vary slice to slice. Therefore, my research addresses the increasing need for a method of analysis to align and compare images from brain regions across slices from a single brain, and from brain to brain. Using scikit-image analysis tools, I extracted information from cell images and videos of nanoparticles obtained in brain slices and determined trends within various regions. My program extracted cell density, shape, and death, then analyzed the uptake of nanoparticles to determine where a small segment of an image is most likely located within the brain. Iterating over the entire image generated a rough map of the regions within the brain which is refined using mapping descriptions detailed in literature. This research resulted in a systematic program that uses image analysis tools to extract features of defined brain regions. This program allows for quick, accurate and consistent analysis of regional differences of cellular features, nanoparticle distribution, toxicity, and other important measures.

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.

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.

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.

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.

Hydrogen is a non-polluting, sustainable energy alternative to fossil fuels. One method of generating hydrogen is by splitting water with sunlight and a semiconductor photocatalyst. In this study, determining the most viable semiconductor in terms of efficiency and safety for the environment is critical for making hydrogen energy available for commercial usage. When the catalyst absorbs UV light, water molecules in contact with the activated surface are split. Hydrogen is produced more rapidly when the photocatalytic crystals have a high surface area to volume ratio. Therefore, greater efficiency can be attained by reducing the size of the photocatalyst crystals. Nanometer-sized crystals can be synthesized within nanopores, thereby avoiding loss of surface area by agglomeration. Particular attention has been focused on placing the most efficient and cost-effective semiconductor inside a matrix of cellulose fibers and introducing doping, quantum dots, and other engineering strategies to improve the suitability of the photocatalyst. Selecting the most fitting photocatalyst candidate for large scale mass production of hydrogen is imperative for providing a viable source of renewable energy.

Solar steam generation has received considerable attention as one of the most promising solar energy harvesting technologies for applications in water desalination, sterilization, distillation, and power generation. Recent research suggests that plasmonic metal nanocrystals can be used as efficient light-to-heat transducers in solar-to-steam applications due to their strong localized surface plasmon resonances; however, large-scale implementation of solar stream generation based on plasmonic metal nanostructures is restricted due to their high cost, structural stability, and low recycling rates. I propose to develop earth-abundant copper iron sulfide nanocrystals as alternative photothermal transducers and apply them in solar steam generation. These nanocrystals can be engineered for efficient light-to-heat conversion and strong, broadband solar absorption. Moreover, they not only consist of earth abundant elements but also have high recycling rates. I plan to work towards enabling large-scale solar stream generation for the sustainable development of our society.

Organic solar cells (OSCs), particularly all-polymer solar cells, have risen as an exciting alternative to standard inorganic solar cells. Their low-cost synthesis, easily tunable properties, and solution-processable fabrication enable facile scale-up for high-throughput production. For commercial viability OSCs will need to have power conversion efficiency (PCE), which is determined by the material characteristics and energetic properties, comparable to their inorganic counterparts (>20%). The question this research aimed to address is: How do processing conditions govern the electronic properties of the photovoltaic layer? Two primary methods were used to evaluate the energy levels: Cyclic Voltammetry (CV) for the highest and lowest occupied molecular orbitals (HOMO/LUMO) and Ultraviolet-Visible Spectroscopy (UV-Vis) for the optical bandgap. A binary blend comprised of a polymer donor, known as PSEHTT, and a polymer acceptor, N2200, was used as a proxy model. Blends of compositions ranging from 0 to 100 weight % PSEHTT were coated on platinum wires for CV and glass substrates for UV-Vis measurements. The collected data behaved as expected, exhibiting a decreasing trend in HOMO level from -5.29 eV to -4.94 eV as the weight % PSEHTT increased. The evolution of the LUMO level with blend compositions was rather challenging to obtain due to possible photoexcitation in the blend leading to free charge available for continuous current extraction. To verify, the samples were isolated from light over the course of the CV measurements to prevent interference. This should result in more accurate LUMO level approximations which are expected to display a similar trend to the HOMO level. Elucidating the relationship between blend composition and blend electronic properties enables a precise and facile device optimization process for highly efficient OSCs.