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The ability to systematically alter the electronic properties of organic materials is vital for their incorporation into next-generation electronic devices. Polycyclic aromatic hydrocarbons (PAHs) are of significant interest for organic electronics, as relevant properties are highly dependent on their size, structure, and functionalities, and thus can be tuned to fit a wide variety of applications. Due to the enormous number of structural isomers available in larger PAHs, the development of design protocols is necessary to efficiently develop high-performing materials. Linear extension of the aromatic core, such as that seen in the acene series, is an efficient yet underexplored method for tuning the electronic properties of 2-D PAHs. The development of synthetic procedures is necessary to systematically explore the properties of the larger aromatic compounds. This work will explore novel synthetic routes that allow for the systematic exploration of acene-fused PAHs of similar size but vastly different electronic properties. Such work demonstrates that by strategically altering the mode of ring fusion, PAHs can be tuned for applications such as organic field-effect transistors (OFETs) and quantum information science (QIS). The impact of linear ring extension in 2-D PAHs is also explored, demonstrating that the electronic structure of larger PAHs can be systematically tuned with significant implications for their applications and stability. The functionalization of a series of organic dyes, with the goal of tuning their optical properties for implementation into wearable radiation sensors, will also be discussed.

Abstract: Traumatic brain injury (TBI) continues to be a significant cause of morbidity and mortality worldwide. Despite significant progress in understanding the complex pathophysiology of TBI, the underlying mechanisms remain poorly understood. The primary brain damage is acute and irreversible. However, secondary brain injuries often develop gradually over months to years, creating an opportunity for critical therapeutic interventions. In the past decade, research on TBI biomarkers has seen significant progress. This progress has been driven by the diverse nature of TBI pathologies and the challenges they present for evaluation, management, and prognosis. TBI biomarker proteins resulting from axonal, neuronal, or glial cell injuries have been extensively studied and widely used. However, their detection in peripheral blood specimens may be limited due to difficulties in crossing the blood-brain barrier in sufficient quantities. Even with the advances made in TBI research, there remains a clinical need to develop and identify novel TBI biomarkers that can address these limitations and provide more accurate and accessible diagnostic tools.

As part of the ongoing effort to substitute finite fuel and chemical resources with renewable ones, biomass is emerging as one of the most promising sources. Biomass consists of three main components of cellulose, hemicellulose, and lignin. Traditionally, cellulose has been used extensively in pulping industry, while lignin has been considered waste and is burned to generate heat. Lignin, a complex aromatic polymer component of biomass, has the potential to be used as a source of aromatic chemicals and pharmaceutical synthons. The recalcitrant nature of lignin, the lack of effective lignin breakdown methods and analytical techniques to analyze it are the main obstacles to obtaining high-yield chemicals from lignin. Mass spectrometry has proven to be one of the most promising analytical techniques and it is widely used in the pharmaceutical and chemical industries.  The goal of this work is to develop analytical methods using mass spectrometry and lignin model compounds. Additionally, this work focused on the development and application of quantitative Derivatization Followed by Reductive Cleavage(q-DFRC) for the evaluation of various biomass pretreatment methods. 

Since most commercially available lignin model compounds fail to mimic the structure of native lignin, it is necessary to develop compounds that more closely reflect the functionality of native lignin. The first project of this dissertation is focused on developing precursors for synthesizing b-O-4 model compounds and modifying their functional groups. The precursors have been synthesized and analyzed using gas chromatography-mass spectrometry. These precursors were used to synthesize b-O-4 model compounds that exhibit all characteristics of the native lignin. 

The second project involved the synthesis and mass spectral analysis of a mixed linkage trimer containing both b-O-4 and b-5 bond types. A detailed analysis of the mass spectral fragmentation of lignin trimer with lithium adduct ionization is presented. The developed analysis of the lignin trimer facilitates the structural elucidation of lignin breakdown products. 

The third project involved the application of q-DFRC as one of the lignin breakdown techniques to evaluate different biomass pretreatment methods. Ethanosolv, dioxosolv, co-solvent enhanced lignocellulosic fractionation (CELF), hydrotropic, and acetic acid/formic acid pretreatments were evaluated by q-DFRC with deuterium-labeled acylated monolignols internal standard. An evaluation and comparison of the quality of lignin obtained from each of these pretreatments was conducted. This dissertation provides valuable information for the advancement of mass spectrometric analysis of lignin, and it can be applied to lignin oligomer analysis. Furthermore, the q-DFRC results provide insight into how various pretreatments are related to the extent of condensation in extracted lignin. 

Organic semiconductors have gained attention recently due to their potential applications in flexible, low-cost, lightweight electronics and solar cells. However, developing new organic semiconductors with improved performance remains a significant challenge due to the vast space of possible molecular structures. Furthermore, the high cost and time-consuming nature of experimental synthesis and characterization hinder the rapid discovery of new materials. To overcome these challenges, this dissertation presents a novel data-driven approach. The primary focus of this work is the development of data-driven tools, namely machine learning models, to predict critical electronic and structural properties of molecular organic semiconductors. These models are trained on a large dataset of quantum chemical calculations, enabling the efficient screening of thousands of candidate molecules. In addition to developing the predictive models, this work includes creating a user-friendly web platform. The platform enables scientists and engineers to access the models and rapidly explore the chemical space to design new materials. The platform also includes visualization and analysis tools to guide the design process and facilitate collaboration between researchers. The data-driven tools developed in this research have the potential to significantly accelerate the discovery and development of new molecular organic semiconductors, paving the way for the next generation of flexible electronics and renewable energy technologies. Overall, this dissertation offers a practical and innovative framework for designing organic semiconductors, leveraging data-driven approaches to overcome the challenges of the traditional experimental trial-and-error process.