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False Memories of Childhood Events in Young Adults: An Empirical Analysis of Theoretical and Legal Perspectives
No full textMost false memory research has relied on parent-generated narratives, yet few studies have used emotionally charged false events relevant to legal contexts, administered interviews remotely, or manipulated the familiarity of individuals included in the suggested events. Few also include self-ratings of memory. This dissertation examines the divergence between coder and self-ratings in this context and investigates whether interpersonal familiarity influences the formation of false autobiographical memories in young adults. In Study 1 (N = 73) and Study 2 (N = 64), participants were interviewed virtually via Zoom about two true events and three false childhood events (spilling punch at a wedding, being involved in an attempted kidnapping at a store, and an overnight stay with a male authority figure), with information described as having been provided by their parents. As expected, participants gave significantly higher memory ratings to true events than false ones, consistent with prior literature. Nearly one third of false event ratings were endorsed based on parental input, especially for the more plausible false events (e.g., the punchbowl and kidnapping), whereas the overnight stay had a floor effect. Study 2 replicated these findings using a counterbalanced event order and interviewers unaware of the hypotheses. Although the familiarity manipulation was not statistically significant, there was a trend toward greater false memory acceptance when the suggested event involved a parent. Notably, parental avoidant attachment predicted susceptibility to false memories. Study 3 (N = 107) used an independent sample to focus on plausibility and memorability ratings. Both were significant predictors of false memory. Implications for existing theories and forensic contexts are discussed
Quantum Chemical Investigations of Reactivity, Selectivity and Dynamics of Chemical Reactions
Get PDFThis dissertation is a series of investigations into reaction mechanisms of organic or organometallic transformations through the application of Density Functional Theory and high-level quantum chemical methods to generate models useful for understanding chemical reactivity, often arriving at unusual mechanistic proposals. The underlying quantum chemical methods and relevant topics are discussed in Chapter 1. Chapter 2 is about an unusual Post Transition-State bifurcation between a diradical and zwitterion intermediate. This post-transition state surface intersection (PTSSI) between diradical and zwitterionic states that causes a bifurcation in the reaction pathway was discovered through density functional theory calculations on potential energy surfaces and ab initio molecular dynamics simulations of cycloadditions between a bicyclobutane and a triazolinedione (BCB-TAD). It was predicted that changes to the solvent polarity would enable control over the dynamic selectivity in this system. In collaboration with the Schomaker group at University of Wisconsin-Madison, we obtained experimental evidence that supported this prediction. This work not only provides new insights into an unusual type of post-transition state bifurcation but also demonstrates how the nonstatistical dynamic effects that control selectivity for such reactions can be manipulated rationally to increase the yields of synthetically useful reactions. Chapter 3 is about a formal dyotropic rearrangement. A new reaction mechanism for the construction of dioxabicyclo[4.2.1]nonanone skeletons via a cation cascade has been proposed and examined by DFT and ab initio computations. This mechanism features the following steps: (1) intramolecular Friedel–Crafts-type cyclization with a methyl oxocarbenium cation formed by carboxylate disconnection, (2) electron-rich aromatic ring assisted methoxide loss followed by lactone formation, and (3) stepwise dyotropic rearrangement resulting in skeletal isomerization from a dioxabicyclo[3.2.2]nonanone to the dioxabicyclo[4.2.1]nonanone product observed experimentally. The high regioselectivity and driving force for the overall rearrangement were rationalized, and Lewis and Brønsted acid-mediated reactivities were compared. Chapter 4 is a collaboration with the Pitts group at University of California, Davis regarding reductive elimination from Te centre. Reaction Mechanism of both sp2 and sp3 C−F bond formation through formal reductive elimination from organotellurium(VI) compounds in superacidic media are investigated using DFT calculations. The results suggest that SbF5 plays an important role beyond fluoride abstraction. Chapter 5 is a series of collaborations with the Pitts group at University of California, Davis on SF5 and SF4CF3 radical addition across bicyclobutanes, propellane and azabicyclobutanes. Reaction mechanisms of Radical Chain reactions of addition of SF5Cl and SF4CF3Cl to bicyclobutanes, propellane and azabicyclobutanes had been investigated using DFT calculations. Reactivity indices and molecular properties were also predicted to explain unusual reactivity. Chapter 6 is a series of collaborations with the Panda Research Group at IIT Kharagpur on an extension of the Zweifel Olefination. Different stereoselectivity in formation of vinyl heteroarenes was observed for different heteroarenes under identical conditions. DFT calculations provide evidence for diverging reaction pathways leading to different stereochemistry. An unusual reversal of stereoselectivity with DDQ was found to lead to a stereoconvergent mechanism. Chapter 7 is a collection of organometallic catalysis involving a Ru/Cu dual metal mediated transformation collaborating with Manmohan Kapur Group at IISERB and two Co-mediated transformations collaborating with the Ponneri Ravikumar Group at NISER Bhubaneswar. These organometallic systems all exhibit interesting behaviors such as two-state reactivity and unusual reaction mechanisms. 
Multi-Word Representations in Minds and Models: Investigating the Storage of Multi-Word Phrases in Humans and Large Language Models
Get PDFOne of the remarkable feats of language learning is the ability to generate never-before-heard sentences. This remarkable feat derives from the ability to retrieve linguistic constructions from memory and combine them using generative knowledge of the language. In other words, humans are able to generate novel sentences by trading off between stored knowledge and generative knowledge. In the past, these two properties were thought to be mutually exclusive: if we store cat and have learned the plural formation in English, then we can generate the word cats without having to store it and thus cats would not be stored. On the other hand, if we haven't learned the plural formation in English, then cats must be stored holistically and accessed from memory. While this seems plausible, a lot of recent work has demonstrated that many high-frequency words and phrases may be stored holistically, despite the fact that they can also be generated compositionally using knowledge of the grammar.The evidence that high-frequency phrases may be stored holistically leads to many new questions. If storage isn't driven solely by productivity, then what is it driven by? Is it driven by the frequency of the phrase? Do other usage-based factors, such as predictability, also drive storage? Additionally, if an item that can be generated as a combination of its parts is stored holistically then in what circumstances do we retrieve the item from memory as opposed to generating it through rules of the grammar? Further, do holistically stored items retain their internal structure? That is, if a phrase is stored holistically, does it retain the representation of the individual words within the phrase?This dissertation focuses on what factors lead to humans storing a multi-word phrase when they could simply generate it by combining each of its parts individually. We show that not only frequent phrases are stored, but also predictable phrases. However, positing that multi-word phrases are stored holistically creates new challenges for theories of processing, which now must explain how multi-word phrases are represented and accessed. Thus, we also explore how holistically stored phrases are represented and processed. Further, we demonstrate that large language models also trade off between stored and generative knowledge in ways that are both similar and different from humans. Finally, we show that by positing that multi-word phrases are stored holistically, we can explain some aspects of language change
Tracing Nuclear and Cell-free Touch DNA on Fired Cartridge Cases Using Domesticated and Wild Fingerprints
Get PDFWith a large number of crimes involving firearms, any touch DNA recovered on a firearm or cartridge case can serve as crucial evidence. Touch DNA is a collection of epithelial cells and cell-free DNA deposited onto surfaces and objects through contact. Given the high likelihood that a suspect will leave touch DNA on a firearm and cartridge case, standardizing quantifiable methods for analyzing touch DNA is necessary. A domesticated hand, made of leather attached to a nitrile glove at the main points of contact, acts as a vector for DNA transfer. A domesticated fingerprint, a ground truth sample, containing a reproducible known quantity of DNA, can be applied to a domesticated hand. This research follows the transfer of touch DNA on a firearm and its fired cartridge cases through a direct transfer pathway using a ground truth positive control to quantify DNA recovery at every step, as well as through a wild fingerprint transfer where cell-free and nuclear touch DNA are separated. Utilizing a reproducible domesticated fingerprint will eliminate any variation in results caused by human (wild) fingerprints and allows for closer examination of what causes a decrease in DNA yield. To simulate hand-to-cartridge case transfer, a domesticated fingerprint was applied to the domesticated hand and transferred onto a cartridge. After firing, the DNA on the cartridge case was collected using a rinse-and swab method, extracted, and quantified. Wild fingerprint touch DNA was deposited on a cartridge, then fired. DNA was collected on the fired cartridge case using the rinse-and swab method, separated into cell-free and nuclear DNA fractions, extracted, and quantified. The results from the cell-free and nuclear DNA extractions were compared to see if there was a difference in the amount of genetic material that is collected. The mean and standard deviation of the DNA yield from multiple domesticated hand pathway runs were calculated. Using the ground truth sample, the average DNA recovered after the firing process was 0.0025 ng ± 0.0061 (with a calculated value of 99.9% of DNA lost due to firing. Understanding the DNA loss patterns at each step provides valuable insight for future multi-step transfer studies. The loss and recovery data can inform activity-level propositions, which aids in determining how DNA was transferred in a given scenario rather than solely using it for identification
An anatomy-based approach to understanding stress response and development in Pistacia spp.
Get PDFUnpredictable weather pattern is a well-known issue worldwide, and extensive effort is underway to improve the abiotic stress tolerance of current crops as well as to select and breed for novelty crops that are more stress tolerant. The US is the world’s top producer for pistachio (Pistacia vera), a woody crop species known for being relatively drought and salt tolerant. This makes it a popular crop in more arid regions, such as California. However, the mechanism behind its relatively high tolerance of drought and salinity stress is unknown, and the cause of the difference in tolerance between different cultivars is an area of high interest for the identification of plant resilience mechanisms to unpredicted weather patterns.Many perennial woody crops are frequently grown as grafted units, where the rootstock is used to convey abiotic and / or biotic stress tolerance to the scion producing the crop with the desired traits. In the first chapter of this dissertation, I compared the rootstock of the low tolerance Pistacia integerrima genotype against the high tolerance hybrid UCB1 (P. atlantica x P. integerrima) focusing on the anatomy of the root tip in search of traits associated with improved salinity tolerance. My studies showed that improved salinity tolerance is associated with increased deposition of suberin at the apoplastic barriers formed by the endodermis and exodermis. Furthermore, the more tolerant UCB1 genotype showed vacuolar sequestration of sodium ions in the root cortical cells. Both the elevated suberin and vacuolar sequestration is apparent only in the first 1 cm of the root tip, highlighting the importance of accounting for the spatial developmental gradient when examining cellular mechanisms of stress tolerance.The search for biomarkers to maintain crop productivity under climate change conditions requires that we first understand the developmental processes in the crop of interest. In the second chapter of this dissertation, I investigated the mechanism behind shell split in pistachio, a trait that occurs during its late-stage fruit development. Interestingly, unlike many of the fruits that naturally dehisces at maturity, the fruit of pistachio does not have a clear dehiscence zone, and its shell only contains a single type of sclerenchyma cell. In my study, I discovered that specialization in shell cell shape and size can create a region of mechanical weakness at the suture, facilitating shell split. Furthermore, the pistachio fruit shows bilateral symmetry, and the width of the kernel is associated with increased rate of shell split, suggesting that kernel expansion force along the sagittal axis may provide the mechanical force behind shell split.In addition to shell split, the hull, or the fleshy outer layers located exterior of the shell, also splits in pistachios. However, while shell split is a trait desired by consumers for the ease of consumption, hull split is an undesirable trait since an intact hull is necessary to protect the edible kernel from pests and pathogens. In the last chapter of my dissertation, I investigated the mechanism behind hull split in pistachios. I found that the filler parenchyma, located in the interior of the hull, expands during the fruit ripening process, while the hypodermal parenchyma located in the skin does not. This creates a “water balloon” like effect, where the internal expansion causes mechanical stress on the skin. The cellular phenotype is affected by weather as well, in which prolonged low-level moisture increases the rate of hull split while acute water exposure does not. In the age of large -omics datasets, the technological advancement in high resolution microscopy allows researchers to generate high quality images to create cellular “phenomics” that can explore previously unknown cellular mechanisms. Together, my work used microscopy and anatomy to study the development and stress response of pistachio, the former of which can help inform the latter to assist in the identification of ideal traits to select for, to optimize crop production under unpredictable weather patterns
Do Bilinguals and Monolinguals Differ in their Predictive Language Processing? An ERP Investigation
Get PDFA prominent idea in the study of language processing is known as Predictive Processing. This theory posits that we actively generate predictions for upcoming language input and that these predictions facilitate language processing. While this theory has been well studied with native speakers, much less is known about prediction in bilinguals despite the global ubiquity of bilingualism. Moreover, the literature on L2 Predictive Processing is mixed, with some studies suggesting that bilingual language users do not predict in their L2 and others suggesting that prediction in L2 is delayed. This dissertation aims to investigate whether native and non-native speakers of English differ in 1) the magnitude, 2) timing, and 3) content of predictively pre-activated information.Chapter 1 provides an overview of the literature on prediction during lexical-semantic processing and sets the stage for the empirical work by outlining open questions about prediction in a second language.Chapter 2 uses EEG/ERPs to examine the effects of accurate predictions and semantic relatedness on processing in the L2 of Spanish-English bilinguals and in Native English speakers. Data were collected as participants engaged in a two-word priming paradigm with a prediction task. Both groups showed clear N400 facilitation for accurately predicted words and semantically related words, and PNP effects to unpredicted related trials. In both groups, the effects of prediction accuracy preceded those of semantic relatedness, demonstrating that effects of prediction are separable from semantic matching effects. However, bilinguals showed a slight delay in the onset of both effects, suggesting that while they engage similar predictive mechanisms as monolinguals, they may maintain competing alternatives for longer, providing greater flexibility to adapt when predictions are not met. Overall, these findings indicate that predictive processing operates similarly across groups but with subtle timing differences that may reflect adaptive strategies in bilingual comprehension.Chapter 3 extends these findings by testing how specific predictive processing is with respect to different levels of linguistic representation. Namely, we investigate whether bilinguals and monolinguals pre-activate semantic features (concreteness), lexical wordform features (orthographic neighborhood), and lower-level visual features (word length) of the target word during processing. The findings showed that both groups predicted semantic and orthographic features prior to the target word, whereas evidence of prediction of visual features was less consistent. These results demonstrate that predictive processing operates at multiples levels of representation as suggested by hierarchical models not only for monolinguals but also for bilinguals in their L2. To our knowledge, this is the first study to investigate anticipation of different levels of representation during L2 processing using EEG.Chapter 4 provides a general conclusion about bilingualism and prediction given the main findings from prior chapters and discusses avenues for future research on these topics. Together, these findings indicate that bilinguals can engage predictive mechanisms in a similar way to monolinguals, while subtle temporal differences highlight the need to further investigate how language experience shapes the dynamics and flexibility of prediction. These results support hierarchical models of prediction and highlight conditions under which bilinguals predict in a similar manner to monolinguals. Future research should examine the role of factors such as proficiency, language dominance, and cognitive control in shaping these processes, as well as explore the consequences of prediction success and failure for learning and memory in bilingual contexts
Identification of Pannexin-1 Channel in Sensory Neurons as an Essential Molecular Intermediary in Pain Signaling Pathways
Get PDFChronic pain is a major global health issue, creating an urgent need for effective, non-addictive and personalized treatments. This dissertation addresses a critical gap by identifying the pannexin-1 (PANX1) channel in primary sensory neurons as a key molecular intermediary in pain pathways. A critical player in pain signaling is adenosine triphosphate (ATP), which exerts its effects through activation of subtype-specific purinergic receptors (P2X and P2Y) in both autocrine and paracrine mechanisms, amplifying and propagating pain signals. PANX1 channel has emerged as a primary non-vesicular ATP release channel and has been extensively implicated in inflammatory responses and pain development. However, prior research in pain has predominantly focused on PANX1 in non-neuronal cells, such as glia and immune cells. The specific function of PANX1 within dorsal root ganglion (DRG) sensory neurons remains a critical gap in knowledge. Based on numerous evidence that PANX1 is activated downstream of transduction channels like Piezo1 and TRP channels in other cell types, we hypothesized that PANX1 acts as a molecular intermediary, coupling TRP channel activation to purinergic signaling via ATP release in multiple pain signaling pathways.To test this, I first defined PANX1 expression in murine DRG using multiplex RNA in situ hybridization. PANX1 mRNA was found in 88% of TRPV1+ neurons, 100% of TRPM8+ neurons, 100% of TRPA1+ neurons, and 89% of CGRP+ peptidergic nociceptors. This high co-expression across nociceptor subtypes positions PANX1 within diverse pain transduction pathways, providing a necessary scientific premise to further investigate potential functional coupling between PANX1 and TRP channels. Next, I investigated functional coupling between TRPV1 and PANX1. Using a dye uptake assay, I showed that capsaicin-induced TRPV1 activation significantly increased ethidium bromide (EtBr) uptake in DRG neurons, an effect abolished by the PANX1 inhibitor trovafloxacin (TVX). This demonstrates that TRPV1 activation enhances PANX1 permeability. Furthermore, I developed an adeno-associated virus (AAV)-based pipeline for in vivo expression of the ATP sensor GRAB_ATP1.0 in DRG neurons to directly measure efflux. Moreover, I determined that PANX1 constitutively regulates neuronal intrinsic excitability in the absence of external stimulation. Current-clamp electrophysiology in cultured DRG neurons from our Panx1DRGcKO mice revealed that PANX1 loss reduces intrinsic excitability and alters firing patterns, shifting responses toward single rather than repetitive firing. This regulation was found to be sex dependent.In conclusion, this work identifies neuronal PANX1 as an essential regulator of nociceptor function and molecular intermediary in pain signaling pathways. I demonstrate that it is: (i) anatomically positioned in key nociceptor subpopulations, (ii) functionally coupled to TRPV1 activation and ATP release, and (iii) a constitutive, sex-specific modulator of intrinsic excitability. By identifying PANX1 as a critical node in pain pathways, this research establishes it as a promising therapeutic target for developing non-addictive, precision analgesics
Advancing Sustainable Postharvest Processing of Floral Hemp through Solar Drying Technologies and Optimization of Drying Kinetics
Get PDFPrior to recent regulatory amendments, the production, research, and consumption of hemp were prohibited. Following the rescheduling, hemp has been grown for food, fiber, and medicinal production. The postharvest drying of hemp flowers is a critical yet under-researched area in the production of high-quality medicinal and wellness products. When hemp is not dried properly or inefficiently, it is susceptible to decreased physical properties, compromised chemical quality, and microbial proliferation. Research gaps exist about the effects of drying on color, cannabinoid and terpenoid concentrations, and microbial load. Specifically, sustainable drying methods, such as solar dryers, have not been recognized as viable solutions for drying hemp flowers. This dissertation examines sustainable drying methods, engineering drying characteristics, and stakeholder practices to enhance quality preservation and mitigate postharvest losses in the hemp industry.In Chapter 2, an indirect solar pallet dryer was designed and evaluated for its effectiveness in drying hemp flowers under summer conditions. The system demonstrated potential as a low-energy, accessible solution for smallholder farmers, reducing moisture content from 71% to 13.5% (wet basis) while preserving cannabinoids, terpenes, and color. The forced-air treatment had higher temperatures than the solar and passive-air dryers, resulting in more pronounced color variability. The ΔE results indicate that all drying methods resulted in perceptible color changes in both cultivars, Maverick and AutoCBG. There was a significant increase in the major cannabinoid concentration when the tissue was dried compared to fresh tissue. The stored sample remained relatively constant, causing no significant change when compared to the fresh and dried groups. Solar-dried samples had higher terpene retention values compared to fresh, forced-dried, and passively dried samples, with storage having no significant effect on this difference. Additionally, force-dried samples often exhibited lower terpene retention due to degradation. Although microbial loads were higher than in passive drying, the solar method retained more terpenes and caused less color degradation, suggesting its viability with further optimization.Chapter 3 examines the drying kinetics of two hemp cultivars, Maverick and AutoCBG, under hot air-drying conditions at temperatures of 30°C, 50°C, and 70°C. The drying characteristics were modeled using both spheroid and ellipsoidal geometries. Results showed that higher temperatures accelerated drying and increased cannabinoid conversion while reducing terpene retention. Effective moisture diffusivity was examined in two cultivars, Maverick and AutoCBG, and similar trends showed that the increase in temperature for both ellipsoidal and spherical models showed a decrease in moisture diffusivity (Deff). For Maverick, moisture diffusivity increased with temperature from 6.8 × 10−5 to 3.1×10−4 m²/s (spheroid) and from 2.1 × 10−8 to 2.1 × 10−9 m²/s (ellipsoid), with corresponding activation energies of 34.7 kJ/mol and 1.19 kJ/mol, respectively. AutoCBG exhibited a similar trend, with Deff increasing from 6.1 × 10−5 to 2.5 × 10−4 m²/s (spheroid) and from 3.1×10−9 to 2.9×10−8 m²/s (ellipsoid). Both major and minor cannabinoids were highest at 70 °C and lowest at 30 °C in the dried sample due to decarboxylation. The opposite trend was seen in terpenes, where degradation was caused by high temperature. Lastly, a significant color impact was observed in the highest temperature. Specifically, 70 °C in AutoCBG showed a ΔE of about 15 when compared to 30 °C which was less than a ΔE of 7.Chapter 4 employs semi-structured interviews and an online survey of U.S. hemp farmers and processors, identifying key themes such as market variability, technological needs, and innovation. Four major themes from the interviews included i) market variability and challenges, ii) postharvest practices, research needs, and technological advances, iii) perceptions and sentiments, and iv) information sharing, collaboration, and innovation. In addition, survey results showed other key ideas crucial to postharvest processing that focused on farm size and productivity, along with observing established industries such as tobacco as key resources for supporting development of hemp markets. Together, these studies contribute to the development of sustainable, efficient, and stakeholder-informed postharvest practices for the hemp flower industry, supporting both product quality and market development. The major limitations associated with the environment and resources, as well as the labor required for harvesting and environmental heat, decreased the number of plants available for the study. This dissertation uses engineering and qualitative approaches for understanding current postharvest practices impacts on quality of hemp flowers as well as current challenges and perceptions of the industry. Drying modeling is important for equipment design and development by applying understanding of how long flowers take to dry, how easily moisture is removed, and the effects of drying on key qualities that are important. In addition, challenges described can be used as future research questions that will be useful for the establishment of markets. Future research will focus on model development bey incorporating more parameters such as relative humidity are air velocity will allow for a more precise understanding of how the environment is affecting the movement of moisture within different varieties. There should also be further qualitative studies focused on understanding the needs of farmers, industry stakeholders, and policy advocates
Automated and Quantitative Workflows for Human Milk Glycans, Proteins, and Carbohydrate Linkages in Foods
No full textHuman milk is a dynamic and complex biological fluid that provides essential oligosaccharides and proteins critical for infant nutrition, gut microbiota development, and immune system maturation. This dissertation presents a series of studies employing high-resolution mass spectrometry, targeted proteomics, and automated sample preparation workflows to quantify human milk oligosaccharides (HMOs) and proteins with high accuracy and throughput. Chapter 1 provides a comprehensive overview of the chemistry and structural diversity of carbohydrates, including those found in human milk and in the mature diet, emphasizing the biological functions of dietary carbohydrates and proteins. Chapter 2 describes the development and validation of a robust LC-MS platform for the quantitation of over 100 HMOs in the absence of commercial standards, demonstrating a novel quantitation approach. Chapter 3 details the implementation of a reproducible, scalable, automated sample preparation and data processing workflow for the quantitative HMO method outlined in Chapter 2, enabling large-scale HMO studies to be conducted with unprecedented speed and precision. Chapter 4 demonstrates the use of combined mass spectrometry techniques to develop a targeted, rapid-throughput method delivering absolute quantification of over 50 proteins in human milk, providing the first method capable of delivering proteomics information for large cohort studies. Chapter 5 integrates automated sample preparation into our previously described glycosidic linkage analysis workflow, showcasing another technique where automation can significantly increase the throughput while preserving reproducibility. Collectively, these studies advance analytical capabilities for nutritional research, providing high-precision methodologies that enhance our understanding of the molecular determinants of early-life and long-term health
Data-Driven Analysis of Charging Behavior and Infrastructure Needs for Vehicle Grid Integration in the United States
No full textElectric vehicles (EVs) are key to decarbonizing the transportation sector. However, their impact on emissions reduction and the power grid depends on when, where, and how they charge. EV adoption is growing quickly in the United States. Thus, it is important to understand how charging behavior affects the power grid and the environment. We need strategies that lower emissions, reduce grid stress, and still work for drivers. This dissertation develops multiple modeling frameworks to study EV charging behavior under different scenarios. It combines behavior prediction, cost-based optimization, and state-level demand simulation. Together, these methods help identify charging strategies that reduce costs and emissions.Chapter 1 introduces a predictive framework that identifies non-critical charging sessions using a deep learning model trained on second-by-second data from 66 battery electric vehicles in California. By forecasting daily vehicle miles traveled, the model enables drivers to skip or delay unnecessary charging, reducing emissions by up to 41% without changing travel behavior. The analysis shows that even in fossil-fuel-intensive states, emissions can be lowered significantly by shifting flexible charging to cleaner hours.Chapter 2 examines the economics of vehicle-to-grid (V2G) participation under different pricing schemes and charger access scenarios. Using real charging data and utility tariffs from four major California electric providers, the model estimates that drivers can earn up to $4,800 annually from V2G under optimal conditions. However, profitability varies by rate design, charger power level, and battery degradation, indicating that infrastructure and behavior-aware incentives are needed for V2G to scale.Chapter 3 develops a national-scale charging model that simulates when and where drivers charge by capturing the tradeoff between cost and convenience. We introduce a novel bi-level optimization framework that integrates state-specific travel patterns, electricity prices, and marginal emissions to estimate charging demand and emissions under multiple infrastructure and adoption scenarios. The results show that workplace charging with high-power chargers is the most cost-effective option for drivers in many states. In contrast, strategies that combine home and work charging at moderate speeds perform better for the grid by reducing peak load and aligning with clean energy. Under the emission-aware strategy, Midwest and Rocky Mountain states still exceed 0.7 lbs CO₂eq per mile due to coal-heavy grids. West Coast and Northeastern states remain below 0.3, supported by cleaner electricity from renewables.Thus, this dissertation offers a practical, scalable framework for estimating and guiding EV charging behavior in ways that support sustainable transportation while reducing stress on the power system. It provides data-driven tools that utility planners, policymakers, and researchers can use to estimate cost, emissions, and grid compatibility under real-world conditions