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REEVALUATING MICHIGAN\u2019S HISTOSOL SOILS : THE FUTURE OF TEAL CARBON AND AN ASSESSMENT OF SAMPLING TOOLS AND METHODS
No full textThesis (M.S.)--Michigan State University. Crop and Soil Sciences - Master of Science, 2025Histosols are widely distributed throughout the state of Michigan. These soils are primarily found in inland wetlands and contain a wide variety of organic soil materials, including fibric, hemic, sapric, limnic, and sulfidic components. The formation of Michigan\u2019s Histosols within herbaceous, woody, and even subaqueous wetland ecosystems reflects the ecological diversity of these landscapes. Together, these characteristics represent a complex and varied portfolio of Michigan\u2019s organic soils.Among the most difficult analyses to perform in Histosols is the determination of bulk density. Bulk density is a critical soil property, as it enables the estimation of carbon content within a given volume of soil. Precise carbon stock assessments are increasingly important in the context of climate change mitigation and the growing carbon credit market. As attention shifts toward blue carbon (carbon sequestered in marine subaqueous soils) and teal carbon (carbon sequestered in freshwater wetlands), and with Histosols potentially playing a significant role in Michigan\u2019s carbon sequestration, the accuracy of their current mapping and characterization must be ensured. Histosols present unique logistical and technical challenges in the field. Standing water, limited accessibility, and fundamental differences from mineral soil protocols complicate both observation and collection. Because site conditions vary widely, careful selection of tools and techniques is essential. Factors such as hydrology, degree of decomposition, and surface stability influence whether samples will be representative and suitable for laboratory analysis. Minimizing disturbance during collection directly improves the reliability of analytical results. This paper examines the distribution and characteristics of Histosols across Michigan, their role in carbon stock mapping, and best practices for sampling and analysis using both field and laboratory techniqueDescription based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
HIGHLIGHTING THE ROLE OF GAS-PHASE KINETICS IN CHEMICAL VAPOR DEPOSITION OF NANODIAMOND
No full textThesis (Ph.D.)--Michigan State University. Electrical and Computer Engineering - Doctor of Philosophy, 2025The formation of nanodiamond through chemical vapor deposition (CVD) is a nuanced processand understanding the details of the physico-chemical processes involved in its synthesis synthesis can greatly enhance the capability of controlling the properties of the synthesized material. This work is geared towards developing a unified understanding of the gas phase and surface kinetics that can be universally applied across various CVD reactors for nanodiamond synthesis. Detailed structural and electrical properties of nanodiamond films grown in H2 /CH4 /N2 plasma were systematically studied as a function of reactor parameters. The standard deposition setup was used to produce films that are a product of gas phase and surface processes combined. The overall resistivity of all the films was tunable over 4 orders of magnitude. Sample characterization through Raman spectroscopy and scanning electron microscopy (SEM) revealed a progressive and highly reproducible material phase transformation, from nano-crystalline diamond to nanocrystalline graphite as deposition temperature increased. The rate of this transformation was heavily dependent on N2 content estimated by secondary ion mass spectroscopy. While diamond deposition is mostly understood in terms of surface kinetics, gas phase processes are not well analyzed. The standard deposition setup was altered to suppress surface kinetics by separating the plasma from the substrate. The collected sample, a product of gas phase kinetics, showed experimental proof of self-nucleation of nanodiamond in the gas phase, calling attention to the analysis of the plasma. Through plasma modeling, gas temperature and CH3 radical distribution were identified to be the key determinants of the location of diamond formation in the plasma. Activation energy values for nanodiamond formation were calculated taking gas temperature into account, which were found to match with those for single crystal diamond (SCD) formation. A iii unified growth mechanism for nanodiamond and SCD was thus proposed concluding that the rate limiting reactions for nanodiamond and SCD formation are the same. With the evidence that nanodiamond formation is possible in the gas phase, alternate reactor designs with greater compactness, portability and compatibility with plasma diagnostics tools were attempted to study the gas phase in greater detail. Radiofrequency and microwave flowthrough plasma reactors were utilized to study the effect of gas temperature measured through high-resolution optical emission spectroscopy on carbon allotrope (??2 vs ??3 ) formation. From this comparison, high gas temperature 2000 K was proven to be necessary for ??3 phase (i.e. diamond) formation. Results from plasma simulations using the microwave flow-through reactor design were in agreement with those for the industrial standard microwave CVD systems providing encouraging novel findings towards understanding the plasma engineering that universally apply to any microwave plasma CVD reactor.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
ORDER-DISORDER TRANSITION IN SUPERIONIC ACrX2 COMPOUNDS AND THEIR ALLOYS : IMPACT ON ELASTIC AND THERMAL TRANSPORT PROPERTIES (A = Ag, Cu; X = S, Se, Te)
No full textThesis (Ph.D.)--Michigan State University. Mechanical Engineering - Doctor of Philosophy, 2025Interest in the superionic layered ACrX2 (A = Ag, Cu; X = S, Se) compounds arises from the intriguing possibility that the same structural characteristics enabling fast ion transport\u2014 such as soft bonding, high anharmonicity, and intrinsic disorder\u2014 also contribute to exceptionally low lattice thermal conductivity. In this study, we systematically investigate the relationship between these behaviors by measuring the elastic and thermal properties of this family of layered chalcogenides. First, phase pure AgCrSe2, CuCrSe2, AgCrS2 and CuCrS2 compounds were synthesized, followed by temperature-dependent measurements of elastic moduli, thermal diffusivity, heat capacity, and electronic properties. Thermoelectric figure of merit was determined for each compound. To explore the effect of anion-site disorder, we synthesized a series of alloyed compounds with mixed anion occupancy, specifically CuCrSe2-xSx (x = 0.1, 0.25, 0.5, 0.75 and 1.0). Structural refinement via Rietveld analysis of X-ray diffraction (XRD) patterns was conducted to extract lattice parameters, and corresponding thermal and ionic properties were experimentally characterized. We investigated three central hypothesis- (1) that the superionic conductors exhibit mixed occupancy at the anion site, showing evidence of forming solid solution; (2) that the mixing at anion site significantly affect thermal and elastic properties of the compounds; and (3) that the order-disorder transition temperature of the compounds changes as a function of composition of the alloy. We further extended our investigation to the CuCrSe2-xTex system (x = 0.1, 0.15, 0.175, 0.2, 0.3, 0.4). We studied the formation of solid solution up to x = 0.15 and suppression of phase transition temperature towards room temperature. Variable-temperature X-ray diffraction and thermal diffusivity measurements were conducted to track the order-disorder/superionic transition temperature (Tc) of the compounds. The transition temperature was found to be highly composition-dependent, exhibiting a decreasing trend with the incorporation of larger anions; CuCrSe1.85Te0.15 had the lowest Tc at 282 K, marking the first reported instance of Tc < 300 K for this crystal structure type. We also investigated the elastic properties and speed of sound in the CuCrSe2-xTex series as a function of composition and temperature. We show that the samples soften sharply as anion size increased. As a function of temperature, we see only a small inflection the temperature coefficient of elasticity, dCij/dT, at order-disorder phase transition, confirming prior findings that long-wavelength acoustic phononsare largely unaffected by the phase transition. These findings demonstrate that tuning interatomic distances and bond stiffness through anion-site alloying can effectively tailor the behavior of solid-state ionic conductors.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Identification and characterization of intracellular Brucella inhibitors and host genes that support Brucella infection
No full textThesis (Ph.D.)--Michigan State University. Microbiology and Molecular Genetics - Doctor of Philosophy, 2025Brucellosis is a widespread zoonosis caused by intracellular bacterial pathogens of the genus, Brucella. While it is well established that nearly all Brucella species infect and replicate within mammalian phagocytes, our understanding of the specific host factors and host-cellular processes that facilitate Brucella infection into host cells remain incomplete. To address this gap, we conducted both pharmacologic and genetic screens to identify pathways and genes that are critical for supporting Brucella infection in macrophages, yielding new insights into pathogen biology. Through a luminescence-based small molecule screen, I identified clinically approved dihydropyridines that reduced the fitness of the intracellular pathogen Brucella ovis within mammalian phagocytes. I initially hypothesized a host-directed mechanism of action for dihydropyridine treatment however, dose-response assays in axenic medium revealed that these drugs can directly inhibit B. ovis growth at concentrations just above that used in the screen. To explore the genetic basis of B. ovis dihydropyridine sensitivity, we selected for mutants capable of growing in the presence of the dihydropyridine cilnidipine. Cilnidipine-resistant mutants carried single-base deletions in the bepE transporter pseudogene that restored an open reading frame, enabling expression of a functional RND-family transporter. Reversion mutations that restored the bepE open reading frame increased B. ovis resistance not only to dihydropyridines but also to a broad range of cell envelope-disrupting agents. Conversely, deleting bepE in Brucella abortus, a closely related zoonotic species that retains an intact version of the gene, increased its sensitivity to envelope disruptors in vitro and to cilnidipine in the intracellular niche. As a complementary approach to small molecule screens, genetic screens can also reveal potential pharmacologic targets for host-directed therapy and inform novel pathogen biology. To this end, we conducted a genome-wide forward genetic CRISPR-Cas9 screen in THP-1 human macrophage-like cells to identify host genes that support B. ovis and B. abortus early infection. This screen revealed pan-infection host factors, and novel host cellular pathways that support Brucella infection, including the mechanistic target of rapamycin (mTOR) signaling pathway, with key genes such as LAMTOR2 and AKT1 emerging as notable hits. We validated the involvement of several candidate genes by generating targeted knockouts in THP-1 cells and through pharmacological inhibition, confirming their roles in promoting Brucella infection. These results illuminate a diverse set of host factors and cellular pathways that support infection, offering new insights into Brucella infection biology and identifying potential targets for future therapeutic intervention.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Improving water management in potato fields : irrigation scheduling and tillage practice
No full textThesis (M.S.)--Michigan State University. Biosystems Engineering - Master of Science, 2025Potato cultivation, as a staple crop is vital to food security, is highly sensitive to soil moisture fluctuations due to the presence of a shallow root system. Consequently, effective water management aligned with the crop's growth stages is essential to optimize yield and crop water productivity in potato production systems. This study optimized irrigation scheduling thresholds using the calibrated AquaCrop model with field data to evaluate yield outcomes and crop water productivity under different irrigation scenarios. The trade-off analysis using the AquaCrop model for irrigation scheduling indicated that irrigation at 40% to 60% FC resulted in improved yield and crop water productivity WPc, representing win-win scenarios across all years (2014-2024) and under two different soil conditions. In addition, tillage practices significantly influence water dynamics by enhancing precipitation retention and reducing runoff. This study utilized HYDRUS-3D modeling to evaluate soil water dynamics under reservoir tillage and compared it to conventional tillage. The results showed that reservoir tillage led to the most significant reductions in instantaneous runoff (80\u201390%) compared to conventional tillage, particularly under rainfall intensities below 20 mm hr 121. However, when rainfall intensity exceeded 30 mm hr 121, the effectiveness of reservoir tillage in reducing instantaneous runoff declined significantly, nearing the levels observed with conventional tillage. The study highlights how targeted irrigation and strategic tillage together improve yield stability, water productivity, and environmental stewardship in Michigan potato fields.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Generalizability of insect brain-based cancer detection
No full textThesis (Ph.D.)--Michigan State University. Biomedical Engineering - Doctor of Philosophy, 2025Cancer is the second leading cause of death globally. Survival rates and quality of life vary depending on the cancer stage at diagnosis. Early and non-invasive detection is key to improving cancer patient outcomes. The composition of the human volatilome, which includes volatile organic compounds (VOCs) emitted through breath, sweat, urine, etc., can be used to determine health. Cancerous cells have metabolic abnormalities that are reflected in changes to the VOCs. Engineered sensors have been able to detect some of these changes, but have difficulty providing rapid analysis, portability, and sensitivity. Biology has solved the problem of detecting volatiles with the olfactory system. Biological olfactory systems can be sensitive to below parts-per-billion (ppb) concentrations, rapidly detect and classify complex mixtures, and generalize between similar mixtures. Insects have well studied olfactory systems that are easily accessed for neural recordings. This work uses the entire insect olfactory system, from the chemosensory array (antenna) to the odor processing centers within the brain to detect cancer odors. I combine the neural output from the insect olfactory system with biological odor encoding rules and computational tools to create a biosensor. First, I show that honeybees can detect ten different compounds associated with lung cancer and that I can use the population olfactory neural signal to classify these compounds (Chapter 3). The honeybees were also able to go a step further and classify synthetic breath modeled on lung cancer patients that had a mixture of compounds with concentrations at ppb and sub-ppb ranges. At this point I switched to locusts, because they are a sturdier and longer lasting sensor, to detect oral cancer cell lines (Chapter 4). The locust sensor was able to differentiate between the headspace of oral cancer cells, non-cancerous cells, and a media control, and even distinguish between individual oral cancer cell lines. Then, I used the locusts to detect breast cancer and lung cancer cell lines delivered to the same locust, showing the generalizability of the biosensor and the potential for it to be a multicancer screening tool (Chapter 5). In an even further afield application, the locusts were able to classify the odors from biofilms, a potentially dangerous and antimicrobial resistant community of bacteria (Chapter 6). Finally, I began to develop a breath-to-biosensor delivery system for the next steps in the development of this technology (Chapter 7). The insect olfactory system biosensor can detect and differentiate between oral cancer, breast cancer, and lung cancer cell lines, and bacterial biofilms by creating a \u2018fingerprint\u2019 for the different odors. These findings bring us closer to a breath-based diagnostic for the early and non-invasive detection of cancer, which will improve the care and treatment of cancer patients.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
ESSAYS ON ECONOMIC IMPACTS OF MARKET CHANGES IN THE FOOD AND AGRICULTURAL INDUSTRY
No full textThesis (Ph.D.)--Michigan State University. Agricultural, Food and Resource Economics - Doctor of Philosophy, 2025This dissertation explores key challenges in today\u2019s food systems, with a focus on three key areas: consumer demand for novel food products aiming to address food safety and sustainability issues, the impact of anti-poverty policies on food processing workers and their implications for food security, and the effectiveness of food policies such as sugar-sweetened beverage taxes in promoting public health. The research provides a comprehensive analysis of how market changes affect various stakeholders in the food and agricultural sectors.The first essay, titled \u201cConsumer Preferences and Demand for Conventional Seafood and Seafood Alternatives: Do Ingredient Information and Processing Stage Matter?,\u201d explores consumer preferences and demand for plant-based seafood alternatives (PBSAs). Two discrete choice experiments were conducted, allowing consumers to evaluate fresh and processed PBSAs in relation to traditional fish and shellfish counterparts and other potential alternatives such as tofu. The study finds that preferences for PBSAs vary based on the specific product, processing stage, and individual characteristics, indicating a high level of preference heterogeneity. The stimulated market share analysis reveals that reducing the prices of PBSAs could lead to a substantial increase in their market share, potentially reaching up to 5% for certain products. However, the impact of price reductions on market share depends on the product type (fresh vs. processed) and the species being replicated. Notably, the preference ordering of PBSAs closely resembles those of conventional seafood products, indicating that consumer preferences for animal-based seafood species extend to the PBSA sector. The study also identifies differences in preferences based on demographics and consumption habits. These results offer valuable insights for stakeholders, such as marketers and policymakers.The second essay, titled "Minimum Wage Impacts on Labor Market Outcomes in the U.S. Food Manufacturing Industry,\u201d investigates how minimum wage policies affect food processing workers, an often-overlooked group compared to young service-sector workers. Focusing on the food manufacturing industry, which employs over 1.5 million individuals and plays a critical role in the food supply chain, this study addresses an important gap by examining how minimum wage hikes affect hourly wages, working hours and weeks, and employment and whether the impacts differ across subsectors and demographic groups. The findings indicate significant wage increases but no adverse employment effects in the overall food manufacturing industry. However, subsector and subpopulation analyses reveal significant heterogeneity, with wage gains concentrated in certain subsectors and positive employment effects for males and natives but negative effects for immigrants. These findings emphasize the need to consider industry-specific and demographic characteristics when evaluating the impact of minimum wage policies.The third essay, titled "Effects of Sugar-Sweetened Beverage Taxes and Added Sugar Labeling,\u201d evaluates the effects of sugar-sweetened beverage (SSB) taxes and added sugar labeling on the purchasing behavior of carbonated soft drinks (CSD), a major contributor to excessive sugar intake. This study examines SSB taxes in Philadelphia (1.5 cents per ounce), Seattle (1.75 cents per ounce), and San Francisco (1 cent per ounce), using NielsenIQ Consumer Panel data from 2014 to 2020. Employing the generalized synthetic control method, the analysis provides robust empirical evidence while accounting for time-varying unobserved factors. The estimated effects are heterogeneous across the jurisdictions, with only Philadelphia demonstrating the anticipated negative impact on CSD purchases, which weakens over time. This finding is likely due to Philadelphia\u2019s higher average pre-tax purchases of CSD, as well as its higher tax rate and pass-through rates. Additionally, Philadelphia has a larger proportion of low-income households, which may be more sensitive to price changes. Based on the estimated effects in 2020, coinciding with the nationwide implementation of added sugar labeling, this study finds no significant effects from the combined policy measures of SSB taxes and the labeling. The findings underscore the importance of context-specific considerations when designing and implementing such policies and the potential need for complementary strategies to enhance the efficacy of SSB taxes and labeling in improving public health.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
ENHANCING DIGITAL HEALTH EDUCATION : AI-BASED ASSESSMENT OF ACTIONABLE GUIDANCE AND INCLUSIVITY ON DIGITAL PLATFORMS
No full textThesis (Ph.D.)--Michigan State University. Business Administration \u2013 Information Technology Management \u2013 Doctor of Philosophy, 2025With one in three U.S. adults turning to the internet, particularly platforms like YouTube for health advice, the quality and inclusivity of online health content significantly impact public understanding, decision-making, and health outcomes. While professionally produced content often adheres to expert guidelines, it represents only a small portion of health-related videos. In contrast, user-generated content is more diverse but often deficient in adherence to established standards, raising concerns about misinformation and the underrepresentation of marginalized voices. Digital platforms are known to amplify societal biases, and prior research shows that algorithmic recommendations can exacerbate disparities in visibility and reach. Manual evaluation of such content is labor-intensive and unscalable. Leveraging Artificial Intelligence (AI) presents a promising approach to assess both the quality and fairness of health-related video content efficiently. In this dissertation, I explore how AI can evaluate actionable guidance, inclusivity, and representativeness in videos. Using a large dataset of diabetes-related videos, study 1 examines how presenter demographics influence video popularity, measured by average daily view counts. Voice and facial recognition models predicted gender and race with high accuracy (up to 93%) and revealed that presenter characteristics impact viewership, particularly when faces are not visible. Study 2 introduces a hierarchical co-attention mechanism within a transformer-based architecture to evaluate whether videos provide actionable guidance, using Patient Education Materials Assessment Tool (PEMAT) criteria. The model achieved 76.4% accuracy, outperforming state-of-the-art classifiers. Together, these studies offer scalable, AI-driven tools to promote equitable and reliable digital health communication by assessing both actionable guidance and inclusivity in video content.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
DEVELOPMENTS ON COLLABORATIVE SENSING AND DATA-DRIVEN CONTROL FOR INTELLIGENT VEHICLES
No full textThesis (Ph.D.)--Michigan State University. Mechanical Engineering - Doctor of Philosophy, 2025Recently, intelligent vehicle systems have significantly advanced through the integration of collaborative estimation, sensing and data-driven control methods, aiming to enhance vehicle safety, efficiency, and stability. These intelligent vehicles leverage collaborative frameworks that integrate data-driven control methodologies, enabling real-time adaptation and proactive decision-making based on continuous analysis of historical and real-time data. This integration significantly enhances the vehicles' abilities to collectively interpret complex environmental interactions, anticipate potential hazards, and optimize performance, thus achieving superior outcomes compared to individual vehicle systems. However, significant challenges remain, primarily stemming from system complexity, modeling uncertainties, and real-time performance requirements. In this thesis, three innovative methodologies are proposed to address these challenges by advancing collaborative sensing and data-driven control for intelligent vehicles.First, inspired by mechanical systems, we introduce a novel microscopic traffic model based on a mass-spring-damper-clutch analogy. This model effectively characterizes longitudinal vehicle interactions within traffic flow, capturing drivers' car-following behaviors and reaction time delays, while explicitly addressing the bi-directional impact between leading and following vehicles. Stability analyses were done to give the conditions under which string stability holds. Additionally, an efficient online parameter identification algorithm, leveraging recursive least squares with inverse QR decomposition, is developed and validated using real-world driving data and connected vehicle datasets, enabling accurate real-time estimation of driving-related parameters crucial for predicting vehicle trajectories.Second, we propose a cloud-assisted collaborative estimation framework that employs Gaussian Process (GP) models to enhance road information discovery. Unlike traditional single-vehicle methods susceptible to measurement errors and model uncertainties, our framework integrates multiple heterogeneous vehicle measurements with cloud-based GP estimations. Each vehicle refines its local estimation using "pseudo-measurements" obtained from cloud-based GP outputs, subsequently updating the cloud model in a recursive and collaborative manner. Comprehensive simulations and hardware-in-the-loop experiments confirm significant improvements in estimation accuracy and robustness.Third, we present a model-free vehicle rollover prevention strategy using Data-Enabled Predictive Control (DeePC), circumventing explicit system modeling challenges. DeePC utilizes historical input-output data to directly predict vehicle behavior, enabling proactive rollover prevention even under challenging driving scenarios. To enhance computational efficiency, a reduced-dimension DeePC employing singular value decomposition is proposed. Extensive validations through high-fidelity CarSim simulations on diverse vehicle types demonstrate DeePC's superior performance over traditional model-based approaches, maintaining vehicle stability while preserving maneuverability under extreme conditions.Overall, the developed frameworks effectively address the complexities inherent in intelligent vehicle systems, demonstrating substantial potential for real-world applications and contributing significantly to the field of collaborative sensing and data-driven vehicle control.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references