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Advancing subject-specific biomechanical models of soft tissues
No full textDecember 2024School of EngineeringSubject-specific modeling of soft tissue biomechanics is crucial for precise and effective medical interventions, particularly in simulating disease progression and surgical outcomes. Traditional finite element method (FEM) models, while accurate, often lack subject-specific material properties, are computationally intensive, and require labor-intensive manual mesh generation. This thesis addresses these limitations by integrating quantitative imaging, deep learning, and automated meshing techniques to enhance the accuracy, efficiency, and applicability of subject-specific models in clinical settings. The research in this thesis is centered around three specific aims: First, we develop a method to refine the mechanical properties of soft tissues in FEM models using quantitative magnetic resonance imaging (MRI) data. By using T2 relaxometry data to refine regional material properties in FEM models, we enhance their specificity and accuracy, enabling more precise simulations of tissue mechanics. Testing on subjects from the Osteoarthritis Initiative dataset shows that T2-refined models estimate more localized principal stresses and shear strains, with better correlation to MRI Osteoarthritis Knee Scores compared to homogeneous models. Second, we propose a deep learning framework designed to accelerate biomechanics simulations of soft tissue deformation. Utilizing the PointNet++ architecture, our network accepts detailed facial mesh point cloud data and input displacement to predict deformation efficiently. To further enhance simulation accuracy, we introduce a spatiotemporal incremental method, incorporating spatial and temporal information via a Graph Neural Network and a temporal memory mechanism. This approach significantly reduces simulation time compared to FEM while maintaining high accuracy, demonstrating the feasibility of rapid simulations in orthognathic surgical planning. Third, we present an automated method for generating subject-specific meshes of facial soft tissues from cone beam computed tomography images using Google MediaPipe for real-time facial landmark detection. This novel approach automates the creation of volumetric meshes, significantly reducing preparation time and enhancing the efficiency and scalability of personalized surgical planning. By addressing these critical issues, this thesis advances the field of subject-specific soft tissue biomechanics modeling, improving the accuracy, speed, and clinical utility of simulations. The methodologies developed in this work enhance patient outcomes, streamline clinical workflows, and broaden the application of personalized medicine, ultimately leading to more precise and effective medical interventions.Ph
Decoding complexity and navigating climate cascades in firms
No full textMay2025School of ManagementThe three-essay dissertation delves into two critical areas in finance: (1) Impact of climate change on production networks, and (2) Information processing costs in financial markets. The first two essays are in the domain of climate finance addressing the adverse spillovers caused in production networks of firms by the billion-dollar hurricanes. The third essay delves into the realm of capturing information processing costs that drive the information asymmetry in financial markets by utilizing machine learning methods. Essay 1. Leveraging major, or `billion-dollar', hurricanes as natural exogenous shocks to production networks, we analyze how supply-chain linkages propagate climate-induced economic spillovers. We find that customers that utilize suppliers in hurricane affected zones experience a 2.5% decline in their sales growth relative to customers who do not have suppliers in the same zone. Firms with higher network centrality are, however, insulated to a significant extent against spillovers from disaster-stricken suppliers. We find that a one-standard-deviation increase in a firm's centrality in its network mitigates this adverse impact on sales growth by approximately 37%. Our findings also reveal that a supplier’s proximity to disaster zones increases the likelihood that contract terminations occur by approximately 3.1%, prompting firms to realign strategically with suppliers that they perceive as facing lower climate change exposure. Furthermore, network effects are key to supply-chain adaptations, with more central customers better positioned to manage relationship terminations and form new ones in response to climate change exposure. Essay 2. Using billion-dollar hurricanes between 2011 and 2019, we examine the immediate effects of these disasters on the option-derived implied volatility of firms. Our findings indicate that firms headquartered within the disaster zones experience increases in implied volatility ranging from approximately 3% to 4.5% relative to firms outside the disaster zones. Similarly, firms with plants located within disaster zones exhibit increases in implied volatility of approximately 1.1%. When exposure is measured as the percentage of plants or revenue affected, the increases in implied volatility are even more pronounced, ranging between approximately 3.6% to 11%. We further investigate the firm-level behavior as a response to the ex-ante uncertainty induced by the hurricanes. We find that firms with greater operational exposure and higher implied volatility shift revenue to unaffected locations. Higher implied volatility also raises the likelihood of supplier contract termination by customers, but supplier firms that relocate production are less likely to face terminations and more likely to form new ties. Additionally, we investigate implied volatility spillovers through supply-chain connections between disaster-affected firms and those in unaffected areas. Our analysis reveals no significant evidence of such spillovers within the production networks of these firms. Essay 3. We investigate the role of machine learning (ML) model complexity in capturing the information processing costs that lead to information asymmetry in financial markets. The basic idea is that informed traders are better suited to process complex, non-linear relations between observable characteristics and future returns. As such, we propose and compute an ML-derived complexity metric to capture the magnitude of the relative advantage informed traders have over noise traders. We hypothesize that increased model complexity leads to increased information asymmetry. To this end, we show that our model complexity metric is positively associated with several well-known proxies of information asymmetry. Specifically, we find positive relations between firm complexity and future return volatility, wider bid-ask spreads and elevated probabilities of informed trading (PIN).Ph
Protein aggregation and drug discovery for alzheimer’s disease and cancer
No full textMarch2025School of ScienceAlzheimer's disease (AD) is the most common form of senile dementia, characterized by progressive and irreversible memory loss and impaired cognitive function. The two major pathological hallmarks of AD brains are amyloid plaques in the extracellular space and tau neurofibrillary tangles within neurons. Both forms of protein aggregates spread in a prion-like manner as the disease progresses. Therefore, aggregation inhibitors capable of targeting both amyloid and tau aggregates represent a promising approach for drug development. Carbon dots (CD) can inhibit protein aggregation, which have also demonstrated high biocompatibility, minimal cytotoxicity, and significant blood-brain barrier (BBB) permeability. To develop new drugs to treat AD, CDs have been synthesized from various precursors such as Congo red, citric acid, urea, metformin, and others, and were characterized by Thioflavin T aggregation assays, atomic force microscopy (AFM), transmission electron microscopy (TEM), nuclear magnetic resonance (NMR), and other methods. We found that Congo-red derived CDs (CRCDs) inhibit tau aggregation as well as amyloid-beta aggregation, acting as dual inhibitors. This research offers promising drug candidates for AD drug discovery.For in vitro aggregation experiments, tau requires an inducer, such as heparin, heparan sulfate, or other polyanionic agents. Using ThT, AFM and TEM, we have observed that the sulfation pattern on heparin can affect its ability to induce tau aggregation. Specifically, N-acetylation, or the removal of the N-sulfate group (N-desulfation), results in nearly a total loss of tau aggregation. Interestingly, the removal of the 2-O sulfation and the 6-O sulfation have little effect on the aggregation potential of tau. The importance of N-sulfation was demonstrated in a variety of tau constructs, including full length 2N4R tau, truncated K18 (the microtubule binding region), and tau (188-441) (2N4R with 187 N-terminal residues deleted). Although, variations in the aggregation as a response to N-desulfated heparin was observed between isoforms, specifically observed with increases in concentrations. Other aspects of the heparin-tau interaction to promote aggregation were also investigated such as pH, heparin monosaccharide length, and concentration.
Cancer is another leading cause of death in the United States, second only to heart disease, but treatment options are still limited. Two proteins of much interest in cancer research are p53 and CypD. Mutated p53 is present in over half of all human cancers, making it the most important tumor suppressor. Cyclophilin D (CypD) is important in antitumor activity via regulation of apoptosis, necrosis, and mitochondrial permeability by interacting with the mitochondrial permeability transition pore (mPTP). We have shown that CypD binds to the truncated p53 (p53tr) mutants, found in many cancers, including p53 196R*, 213R*, and Ψ. In a subsequent NMR titration, even at the lowest concentrations of p53tr, the mutants bound to CypD and largely reduced its NMR signal. Upon addition of the green tea catechin EGCG, we saw a significant recovery of the CypD signal, suggesting that the binding was partially reversed or inhibited by the presence of EGCG for p53Ψ. This data suggests EGCG, and related polyphenols, may be effective against cancers harboring the p53Ψ mutation. Although, p53-R213* and p53-R196* demonstrated evidence of aggregation and further signal was lost after the addition of EGCG. The oxidation of EGCG, and aggregation potential of p53 and CypD were then explored.Ph
Continuous monoclonal antibody purification with capture via precipitation
No full textAugust2025School of EngineeringNew purification processes are required to meet the global need for high-purity, high-volume, high-dose protein therapeutics, such as monoclonal antibodies (mAbs) for the treatment of Alzheimer’s disease, high cholesterol, and infectious disease. We developed a novel intensified, continuous purification process comprised of a precipitation-based capture step followed by two flowthrough chromatography polishing steps that can meet this need by eliminating Protein A affinity chromatography, the bottleneck of the current “platform” mAb manufacturing process. We pre-process harvested cell culture fluid (HCCF) to deplete host cell DNA and remove media components that interfere with mAb precipitation, capture the mAb via precipitation using synergistic bulk precipitants (ZnCl2 and PEG), dewater and wash the precipitate slurry using hollow fiber microfiltration modules in a countercurrent flow configuration to enhance impurity removal, redissolve the washed precipitates at pH 3.5 to enable low pH viral inactivation, and employ two orthogonal flowthrough subtractive adsorbers with minimal intermediate conditioning for polishing. This process can be operated in an integrated, fully continuous mode. It addresses the volumetric throughput, process mass intensity, and cost-of-goods bottlenecks as well as the equipment and supply chain complexities associated with the platform Protein A-based capture step that currently limit global mAb manufacturing capacity. This eminently scalable process also readily accommodates increasing upstream product titers, as the precipitation-based capture step becomes more efficient as mAb concentration increases. We demonstrated precipitation-based capture with mAb HCCF feed materials from multiple industrial partners, gaining key insights which support further process development and suggest that the capture process may be platformable. During HCCF pre-processing, we deplete host cell DNA via CaCl2 precipitation to significantly reduce DNA persistence in the process, which facilitates complete redissolution at acidic pH and allows the redissolved precipitate stream to be directly applied to the first polishing step without further stream conditioning. We also pre-concentrate and diafilter the DNA-depleted HCCF in a single-pass tangential flow filtration step to remove culture media components that interfere with mAb precipitation and to standardize the precipitation feed concentration and buffer matrix, which permits the use of similar, low precipitant concentrations for quantitative precipitation (> 95%) for all mAbs studied. For the capture step, we found that the addition of CaCl2 during precipitation leads to the formation of more densely packed precipitate particles, resulting in higher sustainable flux values and better impurity removal in the dewatering and washing operations. We attained maximum sustainable conversions of 65-75% in the hollow fiber microfiltration modules, which led to host cell protein (HCP) levels as low as 11,500 ppm for redissolved precipitates. At maximum sustainable conversion conditions, we achieved yields as high as 94%, buffer consumption as low as 370 mL/g mAb, and throughput as high as 33 g mAb/m2/h (based on total membrane area of the dewatering and washing hollow fiber modules) for the precipitation capture step.
We integrated the precipitation-based capture step with two flowthrough polishing steps and demonstrated fully continuous operation of the monoclonal antibody purification process. Following precipitation capture, we redissolved the washed mAb precipitates via in-line dilution at low pH to enable loading of the first polishing step. We utilized novel flow attenuation hollow fiber ultrafiltration modules to match the flow rates of the redissolution and neutralization steps with the subsequent polishing operations, enabling fully continuous operation of the process without the use of surge tanks. For polishing, we employed two orthogonal flowthrough subtractive adsorbers and performed an in-line pH adjustment (neutralization) between the steps. We utilized the combination of a hydrophobic adsorbent (activated carbon) and a mixed-mode anion exchanger (Capto Adhere ImpRes), which results in excellent clearance of residual impurities including HCPs and aggregate species at high mAb yields. We achieved > 90% yield for each processing step and observed a significant increase in precipitation capture yield during prolonged operation at steady-state conditions, resulting in an overall purification process yield exceeding 82%. We reduced host cell protein concentrations to below 10 ppm and high molecular weight impurity levels to approximately 1% in the final purified product.
We intensified the precipitation-based capture step by increasing the precipitation feed concentration by a factor 3. We attained a maximum sustainable conversion of only 40% for the intensified process, leading to significantly lower impurity removal in the dewatering and washing steps and HCP levels of approximately 50,000 ppm for redissolved precipitates. In future implementations of the intensified precipitation capture process, filtration performance in the dewatering and washing steps must be improved to enable high-capacity flowthrough polishing chromatography operations that meet final purity targets. Intensification of the precipitation capture process resulted in significant improvements in throughput (147 g mAb/m2/h) and buffer consumption (93 mL/g mAb). We performed an environmental analysis which revealed that intensification via feed pre-concentration resulted in substantial improvements in sustainability metrics for the continuous precipitation capture process. However, additional process intensification, which can be achieved by further pre-concentration of the precipitation feed, will be required to make continuous precipitation capture competitive with continuous Protein A capture relative to environmental footprint. We also performed a simple scaling analysis of process economics which suggested that process intensification reduced the Cost of Goods for continuous precipitation capture to a value lower than continuous Protein A capture.Ph
Development of integrated and scalable platforms for mrna synthesis, purification, and thermostable mrna-lipid nanoparticle drug formulation
No full textAugust2025School of EngineeringMessenger RNA (mRNA) therapeutics are poised to transform modern medicine, but their widespread adoption is limited by challenges in large-scale manufacturing, impurity removal, and formulation stability. This dissertation presents a series of integrated solutions for the production, purification, and stabilization of mRNA medicines, emphasizing the translation of laboratory innovation into scalable, industrial processes. The thesis work began with the design and synthesis of mRNAs of varying lengths, encoding for concatemeric EGFP proteins, which serve as reference materials for studying the impact of sequence and structure on product quality and delivery. These synthetic mRNAs were thoroughly characterized for integrity, size, and purity using electrophoresis, HPLC, bioanalyzer analyses, and next-generation sequencing (NGS). Following purification, the EGFP mRNAs were encapsulated in lipid nanoparticles (LNPs) using clinically relevant lipid compositions. A comprehensive series of lyophilization protocols were then developed and optimized to produce stable, freeze-dried mRNA-LNP formulations. The physicochemical and functional stability of these lyophilized products was evaluated over extended storage at a range of temperatures, providing new insights into the factors that govern long-term stability of mRNA therapeutics. To address early-stage process impurities, a polyethylene glycol (PEG)-citrate aqueous two-phase system (ATPS) was developed for the rapid and scalable removal of proteins and rNTPs from crude in vitro transcription reactions. The ATPS workflow leverages unique interfacial adsorption properties of mRNA to enable high-yield and gentle separation directly from unprocessed reaction mixtures. This method significantly accelerates the purification process and reduces the burden on downstream chromatographic steps. For final polishing and impurity clearance, a tandem chromatography strategy was established. Hydrogen bonding chromatography was employed as a first step for the efficient removal of double-stranded RNA (dsRNA) and process-related impurities. This was immediately followed by oligo d(T) affinity chromatography, which selectively captures full-length, polyadenylated mRNA. The two-stage process is fully compatible with large-scale manufacturing, provides high product purity, and meets regulatory requirements for clinical-grade mRNA therapeutics. These advancements provide a robust and scalable framework for the manufacturing of high-purity, thermostable mRNA therapeutics. We hope that the resulting workflow not only meets current regulatory and quality demands but also provides a foundation for the broader deployment and global accessibility of next-generation mRNA medicines.Ph
Antibacterial polymers for biofilm removal
No full textAugust2025School of EngineeringAntibiotic-resistant infections have been rising with the abuse of antibiotics, while the number of new antibiotic drug approvals is declining. Bacterial-resistance-related infections are undermining public health and should obtain more attention. Host Defense Peptides (HDPs), also known as antibacterial peptides, are the solution of nature against bacterial infections. HDPs are amphiphilic small peptides, mostly with fewer than 50 amino acids and positive charges at physiological pH. They are antibacterial while not prone to induce resistance. Due to the instability of peptides, and the difficulty and high cost in chemical synthesis, more efforts were made to the synthesis of polymeric HDP mimics. Over the last decades, the design and synthesis of antibacterial polymers have been well researched on the control over their structural parameters – like hydrophobicity/ hydrophilicity balance, cationicity, molecular weight, functional groups, degree of polymerization, and chain length. These HDP-mimetic antibacterial polymers showed broad-spectrum antibacterial activity and low toxicity toward mammalian cells. The performance of antimicrobial polymers depends sensitively on the cationic species, charge density, and spatial arrangement of cations. Chapter 2 introduced the first example of antimicrobial polymers bearing bulky tetraaminophosphonium groups as the source of highly delocalized cationic charge. The bulky cations drastically enhanced the biocidal activity of amphiphilic polymers, leading to potent activity in the sub-micromolar range. In this work, we reveal for the first time that bulky phosphonium cations are associated with markedly enhanced biocidal activity, which provides a new strategy to develop more effective self-disinfecting materials.
The above-mentioned research from Chapter 2 focuses on killing bacteria in solution with antibacterial polymers. Polymers can also be processed into coatings for antibacterial surfaces, capable of killing bacteria upon contact. Nonetheless, it is imperative to acknowledge that any antibacterial surface will inevitably encounter issues with biofilm accumulation which are intricate assemblies of microorganisms embedded within crosslinked polymer structures built by microorganisms. In Chapter 3, we develop a simple, dynamically reversible method of polymer surface coating that enables both chemical killing on-contact, as well as on-demand mechanical delamination of surface-bound biofilms by triggered depolymerization of the underlying antimicrobial coating layer. This work provides a new and simple strategy for antimicrobial coatings that can kill bacteria on-contact for extended timescales, followed by triggered biofilm removal under mild conditions.
A notable drawback of degradable poly(disulfide) antibacterial coatings lies in the complete loss of functionality following triggered degradation. In order to enhance the practical utility of these coatings, the incorporation of surface-regenerating capabilities emerges as a pivotal strategy for considerably prolonging the surface lifespan. In Chapter 4, I introduced the study of poly(disulfide)s thermosets with azo-containing crosslinkers as potential material candidates for applications in renewable antibacterial/antifouling polymer coatings. At the decomposition temperature of the azo groups, the crosslinkers break down, leading to network decrosslinking and the generation of radicals that initiate degradation of the poly(disulfide) backbones. These materials enable heat-triggered degradation and the potential for surface regeneration, paving the way for renewable antibacterial and antifouling coatings.Ph
A multi-factor approach to bias benchmarks for language models
No full textMay2025School of ScienceBias benchmarks are important ways to assess fairness and bias of language models (LMs), but the design methodology and metrics used in these benchmarks are typically ad hoc. Current work overlooks statistical biases in the benchmark which cause inaccurate conclusions in the benchmark analysis when unaccounted for. We advocate that methods from health informatics for design and analysis of experiments (e.g. clinical trials) would facilitate understanding which potential biases are investigated by a benchmark and provide more insightful and accurate quantification and analysis of observed biases. Specifically, we propose an approach for multi-factor analysis of LM bias benchmarks. Given a benchmark, we first identify experimental factors of three types: domain factors that characterize the subject of the LM prompt, prompt factors that characterize how the prompt is formulated, and model factors that characterize the model and parameters used. We use coverage analysis to understand which biases the benchmark data examines with respect to these factors. We then use multi-factor analyses and metrics to understand the strengths and weakness of the LM on the benchmark. Prior benchmark analyses reached conclusions by comparing one to three factors at a time, typically using tables and heatmaps without principled metrics and tests that consider the effects of many factors. We propose examining how the interactions between factors contribute to bias and develop bias metrics across all subgroups using subgroup analysis approaches inspired by clinical trial and machine learning fairness research. We illustrate these proposed methods by demonstrating how they yield additional insights on the benchmarks, SocialStigmaQA and BBQ. We discuss opportunities to create more effective, efficient, and reusable benchmarks with deeper insights by adopting more systematic multi-factor experimental design, analysis, and metrics.M
Beyond performance metrics: dynamic directed functional connectivity as neural biomarker of surgical skill proficiency
No full textMay2025School of EngineeringObjective assessment of surgical skills is critical for ensuring proficiency, enhancing professional certification processes, and improving patient safety. Existing methods, however, rely on subjective human judgment, introducing bias and limiting reproducibility. While recent approaches have leveraged kinematic data and neural imaging to provide more objective evaluations, these methods evaluate performance outcomes, functioning as metrics, i.e., output measures that help explain what happened during a performance, such as how quickly or how accurately the task was completed. While metrics are valuable for providing a snapshot of performance, they fall short of explaining the underlying neurophysiological process driving the performance, i.e. how and why skill acquisition occurs at the neural level.This thesis introduces dynamic directed functional connectivity (dFC) as a novel neural biomarker that has potential for surgical skill assessment. Unlike traditional metrics, biomarkers offer a physiological basis for evaluation, capturing both the strength and direction of neural information flow in the brain to provide deeper insights into the brain's role in motor skill acquisition and proficiency. Using electroencephalography (EEG) and an attention-based Long Short-Term Memory (LSTM) model to compute non-linear Granger causality, we quantify dFC among key brain regions involved in psychomotor surgical task execution. As a biomarker, dFC enables the evaluation of dynamic neural processes underlying learning and skill execution at both group and individual levels, complementing existing performance metrics. Coupled with hierarchical task analysis (HTA), dFC facilitates subtask-level assessments, while a convolutional neural network (CNN) classifies skill levels with higher accuracy and specificity than traditional approaches in laparoscopic surgery.
For the first time, dFC is applied to map stages of the well-established Fitts and Posner motor learning model, providing new insights into the neural mechanisms underlying skill acquisition and retention. dFC effectively identifies and tracks progression through various stages of this model, and its stability over a six-week washout period highlights its utility in monitoring long-term retention. Importantly, control group analysis confirms that observed neural adaptations are specific to training rather than external factors.
Additionally, we investigate the neurophysiological impact of physical versus virtual reality (VR) based simulators on brain connectivity during surgical tasks. Functional near-infrared spectroscopy (fNIRS) combined with Granger causality revealed a statistically significant effect of training on physical and virtual simulators, which depends on expertise level. Short-separation channel analysis ensures that these effects reflect neurophysiological changes rather than extracerebral artifacts.
By establishing dFC as a robust, biologically grounded, objective, and reproducible neural biomarker, this work provides a comprehensive and individualized framework for evaluating and optimizing surgical training protocols. This work addresses critical limitations of subjective and performance-based metrics, paving the way for personalized training protocols, improved simulator designs, and a step towards developing evidence-based certification standards. Ultimately, these advancements promise to elevate surgical education, enhance training efficiency, and bolster patient safety.Ph
Asymmetric consequence relations and many-valued semantics
No full textMay2025School of Humanities, Arts, and Social SciencesThis work is concerned with the definition and motivation of asymmetric logics. These are many-valued logics with different sets of designated values for the premises and the conclusions. Their existence will be motivated both philosophically and formally. Not only do they constitute examples of novel logics that can model some aspects of our reasoning, but they are formally and historically important as counterexamples to the famous thesis of Roman Suszko. Suszko’s Thesis states that every logic is (logically) two-valued. Importantly, asymmetric logics can be given a many-valued (Suszko-style) semantics (which was first suggested by Grzegorz Malinowski in his notion of q-consequence). However, these logics are non-Tarskian, which forces the need to reevaluate the standard definition of validity. The asymmetric logics ST and wST are discussed and motivated in further detail.M
Synthesis and characterization of monomeric ruthenium-based catalysts for water oxidation
No full textMay2025School of ScienceNature converts solar energy into chemical energy (in the form of carbohydrates), releases dioxygen, and fixes carbon dioxide through photosynthesis. Light-driven water oxidation, one of the most energetically demanding reactions in nature (2H2O → O2 + 4H+ + 4e-), occurs in the photosynthetic reaction center, photosystem II (PSII). The structure of PSII revealed the tetranuclear manganese-calcium-oxo (Mn4Ca-oxo) cluster in the oxygen-evolving complex (OEC) that is known to catalytically initiate the water oxidation to produce protons, electrons and the release of dioxygen. Inspired by the catalytic manganese-calcium-oxo (Mn4Ca-oxo) cluster, several catalysts for artificial water oxidation have been developed with varied metal centers. The scope of the metals includes the first-row transition metals, ruthenium, and iridium. In Chapter 1, we describe the water oxidation reaction in Nature and progress on the development of artificial water oxidation complexes over the past few decades. Among the aforementioned metals, ruthenium-based catalysts have been studied extensively to explore the structure-activity relationships in the hope of illuminating a strategy for designing an efficient catalyst for water oxidation. Based on the current molecular ruthenium models for water oxidation, we designed, synthesized and characterized a series of ruthenium complexes with a negatively charged dicarboxylate backbone.
Chapter 2 describes the synthesis and characterization of ruthenium-based complexes with symmetric backbone ligands, such as, pda2− (2,6-pyridinyldiacetate), pba2− (pyridine-2,6-bis(α-oxo) acetate) and pdc2− (2,6-pyridinedicarboxylate) with various ancillary ligands, tfmp, py, pic and dmap, differing in electron-donating ability. 1H NMR was employed to investigate the electronic effect of ancillary ligands on the protons from the backbone ligands and those from the coordinated ancillary ligand. UV-Vis spectroscopy was used to study the electronic absorption of the Ru-pba family. The UV-Vis spectra revealed that the electron-donating ability of ancillary ligands can affect the metal-ligand charge transfer (MLCT) bands. The 1H NMR spectra revealed that a stronger electron-donating ancillary ligand will result in an upfield shift of the peaks.
Chapter 3 describes the synthesis and characterization of ruthenium-based complexes with asymmetric backbone ligands, such as, cmpc2− (6-(carboxymethyl)-pyridine-2-carboxylate) and cpa2− (6-carboxy-α-oxo-2-pyridine acetate) with various ancillary ligands, tfmp, py, pic and dmap, differing in electron-donating ability. 1H NMR spectroscopy was employed to investigate the electronic effect of the ancillary ligands on the protons from the backbone ligands and those from the coordinated ancillary ligand. UV-Vis spectroscopy was used to study the electronic absorption of the Ru-cpa family. Comparison of the 1H NMR and UV-Vis spectra are presented in this chapter.
The crystal structure of the complexes revealed an O-Ru-O bite angle ranging from 172° – 178° in the complexes with little distortion of the octahedral configuration, indicating increased stability and ease of access to bind water molecules at the metal center. Functional catalytic studies of these complexes in the future will contribute insight on the structure-activity relationships and serve as a motivation for the design of novel molecular catalysts for water oxidation.Ph