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Stormwater Quantity and Quality: An Assessment of Runoff Probability Estimation Methods and the Effects of Bioretention Soil Mixtures on Aquatic Toxicity
Get PDFStormwater runoff occurs when precipitation exceeds the available storage of a watershed. Urbanization changes the natural hydrology of a watershed as cover types, slopes, flow paths, and antecedent moisture conditions (AMC) are altered. Generally, there are two concerns associated with increased stormwater runoff: water quality and water quantity. Stormwater quality and quantity may be controlled through several types of stormwater management practices (SMPs). The goal of low impact development (LID) is to manage stormwater in a way that preserves the pre-development hydrological characteristics of a watershed, including its ability to infiltrate, evaporate, filter, and detain stormwater (Dietz, 2007; Prince George’s County, 1999; USDA NRCS, 1986; USEPA, 2000).
The runoff threshold of a catchment is often reported in the literature as a measure used to evaluate the effectiveness of LID techniques and is oftentimes determined using linear regression. However, due to the heteroscedastic nature of precipitation and runoff data, linear regression analyses can result in invalid conclusions, so the use of a binomial regression model was investigated. Precipitation and runoff data collected from five studies were assessed for homoscedasticity and applied to four linear regression models for the evaluation of the linear regression runoff threshold (LRRT) and the associated 95% bootstrapping confidence intervals. Log-transformation corrected the heteroscedasticity of two of the five precipitation and runoff datasets. While mixed-effect linear models accounted for heteroscedasticity, these models often resulted in extremely large confidence intervals. A binomial regression model was created to determine the likelihood of runoff based on precipitation depth. For each catchment, the 10%-90% runoff probability range (p10-p90) is reported to provide the user with a more comprehensive understanding of when a catchment produces runoff than the LRRT. The p10-p90 range reflects the effects that environmental factors may have on runoff generation. For example, impervious catchments with limited interaction with the vegetation and soil produce a narrow p10-p90 range. Conversely, LID practices encourage the interaction of runoff with environmental factors and result in a wider p10-p90 range. Utilization of the binomial regression methodology presented herein is recommended for evaluation of the likelihood of runoff for each precipitation depth.
Heavy metal concentrations in stormwater discharges are regulated and monitored as these pollutants can be toxic to aquatic communities (USEPA, 2007). The total concentration of a heavy metal does not represent the concentration of a metal ion available to aquatic organisms. For a heavy metal to be bioavailable for uptake through the gill, it must be in a dissolved form. When evaluating the toxicity of stormwater, the bioavailability and speciation of a heavy metal should be considered. The influent and effluent of four bioretention soil mixtures (BSMs) from ten storms were studied for the purpose of examining speciation shifts of stormwater pollutants and investigating potential changes to stormwater toxicity following filtration through BSMs. Further, this study sought to determine which, if any, of the BSMs were more adept at decreasing pollutant bioavailability. Visual MINTEQ 3.1 was used to predict pollutant speciation and the Windward Environmental, LLC Biotic Ligand Model (BLM) (v 3.41.2.45) was used to determine toxic concentrations of heavy metal species. No noticeable speciation shifts were noted within bioretention cell (BRC) effluent for cadmium, copper, lead, and zinc. However, the speciation of chromium effluent was dependent on the initial pollutant concentrations. A multiple-factor analysis (MFA) indicated that the four BSMs do not differ from one another in reducing the BLM-identified toxic limit (relative toxicity) of stormwater effluent for the BLM-selected aquatic organisms. BRCs are most effective at reducing the toxicity of stormwater when the stormwater contains high pollutant concentrations. At low pollutant concentrations, BRCs may increase the toxicity of the effluent stormwater through export of copper
The Lived Experiences with Accessibility of Undergraduate Students with Disabilities
Get PDFPeople with disabilities is the largest minority group in the United States, and it is a group any individual can join at any point in their life, so issues related to disability are relevant as they can impact anyone. There is a need for college educators and college counselors to increase their understanding of the experiences of college students with disabilities. More research related to accessibility on college campuses for students with disabilities will aid in fulfilling the needs of students with disabilities in areas of their overall lived experiences, sense of belonging, and disability identity development. The purpose of this study is to build on the body research about college students with disabilities and their sense of belonging and disability identity in relation to their experience with accessibility on campus. The current research study aimed to explore the lived experiences with accessibility of students with disabilities at a large public southeastern university to identify if there is an impact on their sense of belonging and how they develop their disability identity. The findings will be used to provide educators and administrators with resources to better support students with disabilities on college campuses and in the classroom
Investigating Consumer Perceptions Towards Apparel Made of Recycled Polyester Fabric
No full textThis study investigates consumer attitudes and purchase intentions toward apparel made from recycled polyester fabric, examining key moderating factors (i.e., need for uniqueness, fashion consciousness, environmental consciousness, and age) to understand the drivers behind sustainable fashion consumption. Grounded in Homer and Kahle's (1988) Value-Attitude-Behavior (VAB) model, the research analyzes how perceived values—functional, economic, emotional, and social—shape consumer attitudes, which in turn influence their purchase intentions. The primary objective was to explore how these perceived values interact with individual consumer traits to impact attitudes toward such apparel. Applying Constructive Controversy (2020), this study captures both favorable and opposing perspectives within sustainable fashion, offering insights into consumer conflicts when evaluating recycled polyester. Data were collected through an online survey with 285 participants across various age groups, utilizing quota sampling for representativeness. The results reveal that functional, economic, and emotional values significantly enhance consumer attitudes toward recycled polyester apparel, while social value does not. Furthermore, attitude influenced purchase intention toward apparel made from recycled polyester fabric. Additionally, the hypothesized moderating effects of need for uniqueness, fashion consciousness, environmental consciousness, and the research question exploring the moderating effect of age on the values-attitude relationship were not supported; however, an alternative model revealed that, aside from age, these traits significantly moderated the attitude-purchase intention relationship, providing insight into consumer characteristics unsustainable fashion choices. These findings enhance the VAB model by integrating consumer traits as moderators and suggest that marketers should highlight recycled polyester apparel's benefits while tailoring strategies to individual traits to boost adoption
Zero-Shot Multi-Label Topic Inference
No full textDespite significant progress in NLP research, we still lack a general-purpose inference tool that can effectively serve users from a wide range of application domains. One way to address this challenge is to create supervised training examples with custom-defined topics of interest as labels from each user and then train a classifier on those labels to infer topics. However, creating custom training examples is costly and time-consuming, and the scarcity of high-quality training examples presents a great challenge for data-hungry deep-learning models. Therefore, the machine learning (ML) community has recently been pushing toward zero-shot and transfer learning approaches. In this thesis, we discuss a cardinal yet relatively unexplored NLP task called “Zero-Shot Multi-Label Topic Inference”, which infers topics from documents where documents and topics were never seen previously by a model.
As no benchmark dataset was readily available for this task, first, we created two real-world datasets, i.e., “News Concept Dataset” and “Medical Concept Dataset,” to provide a way to evaluate this task. In the next phase, we performed a detailed study on how to leverage SOTA sentence encoders and Large Language Models (LLMs) for the task, where the topics are defined/provided by the users in real time. These models have indeed been shown to achieve superior performance for many downstream text-mining tasks and, thus, have been claimed to be fairly general. Through extensive experiments, we designed and developed 1) various embedding procedures, 2) multiple Zero-shot Topic Inference methods, 3) presented a comparative study of the state-of-the-art sentence encoders and LLMs, 4) evaluated Zero-shot models from a Multi-User Perspective, and 5) a case study on limitations of the SOTA sentence encoders.
Finally, we demonstrate potential applications of Zero-shot Topic Inference in two distinct domains: a) Policy-making in healthcare management and b) Evaluating student performance. We also discuss the potential implications of zero-shot models for low-resource languages and outline our future plans. This work presents a transformative potential of leveraging zero-shot and transfer learning principles to create more flexible, responsive, and broadly applicable AI systems, ultimately bridging the gap between rigid pre-trained models and the dynamic requirements of real-world applications
Assessing the Spread and Movement of Cotton Leafroll Dwarf Virus and Cotton Infesting Aphids in the Southern U.S.
Get PDFThe cotton aphid, Aphis gossypii Glover, is the only known vector to transmit cotton leafroll dwarf virus (CLRDV) to cotton, Gossypium hirsutum L., but the role of the aphid vector in the epidemiology of CLRDV is not defined. The overarching goal of this dissertation is to assess the role of the vector in the epidemiology of CLRDV.
The overall objective for Chapter 1 was to conduct a meta-analysis of 289 published effect sizes from 28 studies to better understand the relationships between plant pathogen vectors or vector-borne pathogens and landscape composition, while considering a series of biological, ecological, and environmental factors that may contribute to variation in the relationships. We found that vector and vector-borne pathogens were positively associated with landscape composition metrics, including the percentages of the landscape surrounding the study areas that were crop, non-crop, and natural habitat, but not with habitat diversity. Vector specialists exhibited a significant positive association with various landscape composition metrics while generalists did not, suggesting that specialists, with their narrower host range, may be more dependent upon particular landscape features. Generalists, however, have more host options, and thus their populations may not be as strongly associated with landscape features. Measurements of both abundance and incidence of pests exhibited significant positive associations with landscape composition metrics. For both persistent and non-persistent pathogens, the slope of the relationship between pathogen populations and landscape composition metrics increased as the spatial scale used to calculate landscape composition increased. This meta-analysis demonstrated that vectors and vector-borne pathogens were positively associated with the composition of the landscape, and that these relationships were influenced by different biological, ecological, and environmental factors. Future vector and disease management strategies should consider the impact that landscape composition may have on these pests, and how the landscape, along with cultural practices, can be manipulated to aid in pest management.
The overall objective for Chapter 2 was to determine the spatiotemporal distribution, abundance, and seasonal dynamics of the eight species of aphids that have been reported to feed on cotton. In 2020 and 2021, aphids were monitored from North Carolina to Texas using pan traps around cotton fields. All of the species known to infest cotton, excluding A. fabae, were detected in this study. Protaphis middletonii and A. gossypii were the most abundant species identified. The five other species of aphids captured were consistently low throughout the study and, with the exception of R. rufiabdominale, were not detected at all locations. The abundance, distribution, and seasonal dynamics of cotton-infesting aphids across the southern U.S. are discussed.
The overall goal for Chapter 3 was to investigate and understand how different biotic and abiotic factors may be driving CLRDV incidence in cotton. Field surveys were conducted in Alabama and Georgia from 2021-2022, to monitor A. gossypii, natural enemy, CLRDV weed hosts, and CLRDV incidence in cotton, alongside collecting data on landscape composition (percentage of cotton and natural habitat surrounding field sites at a 5 km radius), temperature, and precipitation. A structural causal modeling framework was used to assess how these different factors were driving CLRDV incidence in cotton. Aphis gossypii abundance, cotton, and temperature had a significant positive effect on CLRDV incidence. Temperature also had a significant positive effect on A. gossypii abundance, which likely mediated the effect of temperature on CLRDV incidence. Natural enemies, CLRDV weed hosts, natural habitat, and precipitation did not significantly affect CLRDV incidence or A. gossypii abundance. Aphis gossypii is the only known vector to transmit CLRDV to cotton, therefore an increase in vector abundance can increase the likelihood of pathogen presence and transmission. Temperature serving as the only driving factor for A. gossypii abundance in cotton explains the direct positive association between temperature and CLRDV incidence, and further complements the finding that A. gossypii abundance increased CLRDV incidence. Cotton serves as a host for both the vector and the virus, so this increase in cotton may have contributed to inoculum sources and spread that augmented incidence of the pathogen in the landscape. Cotton fields in areas with greater cotton production, higher A. gossypii abundance, and warmer temperatures are at greater risk for CLRDV infection. Warmer regions may experience elevated aphid infestations and population growth, that help increase CLRDV incidence. Our findings identify high-risk regions and the associated causal factors, guiding future research for effective management strategies. They also inform growers and researchers about priority areas for monitoring and management efforts.
The primary goal for Chapter 4 was to use DNA high-throughput sequencing to conduct a gut content analysis to reveal the dietary history of A. gossypii prior to colonizing cotton. .Aphis gossypii alatae were collected from cotton fields in Alabama, Georgia, Louisiana, Texas, South Carolina, North Carolina, Virginia, and Mississippi in 2020 and 2021, and used in the gut content analysis. A total of 92 plant genera were detected in this study, 66.30% of which contained at least one species reported to be a host for A. gossypii. Gossypium was detected in more aphids than any other plant genus (66.14%), which is intuitive given that aphids were collected from cotton. Convallaria (17.55%) was the second most frequently detected plant genus, followed by Triticum (8.46%), Prosopis (6.58%), Melilotus (6.58%), and Solanum (5.64%). An additional 86 genera were detected in 85.26 % of the aphids sampled, indicating a high level of variability detected among plant genera. This study highlights gut content analysis as a valuable tool for understanding A. gossypii's dietary history and plant interactions
eTETRAS: An Extension To The Essential Tremor Rating Assessment Scale
No full textEssential tremor (ET) is a widespread neurological condition characterized by tremors in an individual’s limbs, significantly impacting one’s quality of life. It stands as the most prevalent type of movement disorder, often leading to debilitating effects. Various scales, such as the Fahn-Tolosa-Marin (FTM) tremor rating scale and The Essential Tremor Rating Assessment Scale (TETRAS), have been developed and used by physicians to classify the severity of ET. While the FTM scale is highly utilized in ET severity diagnosis, it relies on subjective assessments of the tremor. TETRAS, on the other hand, provides a more quantitative analysis of ET severity by ranking the severity of the tremor based on tremor magnitude and addresses the shortcomings of FTM. Though TETRAS is seen as an improvement to FTM, both scales often lack the necessary resolution and granularity for optimal diagnosis and treatment. Moreover, ET severity rating scales like TETRAS require a trained professional (such as a neurologist) to be present, and even in such cases, physicians use TETRAS as a metric baseline to visually approximate the severity of the tremor. This reliance on manual approximations by clinical personnel may introduce inconsistencies and bias due to variability in technique and accuracy, diminishing proper assessment and subsequent treatment. To address the limitations of TETRAS, an extended version of TETRAS (eTETRAS) was developed that leverages machine learning and wearable sensors to quantify ET severity across the entire joint range of motion. Specifically, the proposed eTETRAS utilizes a deep neural network (DNN) to classify ET severity over the range of motion of the affected limb using joint angle measurements, eliminating the need for a clinician diagnosis. To enable a rapid DNN response (i.e., nearly real-time ET classification) with enhanced resolution, the DNN assesses the severity of ET every 0.5 second increment. After the second iteration of eTETRAS, the DNN-based classifier achieved a training accuracy of 99.59% and 100% for the simulated and experimentally obtained datasets, respectively. Moreover, eTETRAS attained a training accuracy of 99.65% when trained using the combined simulation and experimental dataset.
In Chapter 1, an overview of TETRAS and its key issues are presented. This discussion serves as a foundation for exploring the motivation, clinical efficacy, and intellectual merit of the proposed eTETRAS classifier, which incorporates a comprehensive literature review. Chapter 2 discusses the ET signal generation and the experimental protocol. Chapter 3 proposes and tests a preliminary version of eTETRAS. This includes a single-dimensional DNN that uses knee encoder data as inputs into the DNN. Chapter 4 advances the work in Chapter 3 by presenting and testing the latest iteration of eTETRAS, which includes joint stiffness data concurrently with knee encoder measurements as inputs into the DNN to create a two-dimensional DNN. In Chapter 5, the thesis is concluded by highlighting the clinical and intellectual contributions of eTETRAS, and its future developments
Predictive Sensor Analytics for Power Side-Channel Monitoring of Security Applications in Fused Deposition Modeling
No full textThe advent of Additive Manufacturing (AM) changed the landscape of the manufacturing
industry resulting in new possible designs and products. Around the same time, Industry 4.0 got
great attention from the industrial community. Industry 4.0 works across systems connecting
machines and different sub-systems and collects and analyzes data to make decisions. This
type of interconnected system is considered to be the direction in which the manufacturing
world is moving. Thus, it is safe to say that AM will be incorporated into Cyber-Physical
Systems (CPS) in some capacity. A CPS consists of three components: a cyber component,
a physical component, and a combination of these two components. An increased number
of interaction points in the CPS leaves the system vulnerable to attacks, either in terms of
intellectual property theft or the system can be sabotaged. Traditional cybersecurity protocols
may mitigate the attacks to some extent, but they can not handle the physical component of the
system, keeping space for some attacks. In such scenarios, side channels like sound, vibration,
and heat may provide certain information regarding the process, irrespective of whether the
system is compromised or not.
This work is aimed at analyzing the power side-channel signal in a Fused Deposition
Modeling (FDM) process, a type of AM process. The main similarities and differences between
a cyber system and CPS and their different strategies are discussed. A method is designed to
predict the power signature based on the CAD design. A comparative study of the predicted
and actual power signature can provide certain information about the machine and process,
including anomaly or sabotage detection.
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Evaluating Cookery Characteristics, Consumer Acceptability, and Electronic Assessment of Attributes in Ground Beef Patties
Get PDFWith consumers driving beef demand, attributes such as flavor, wholesome, safe, and affordability remain at the forefront of the meat industry. Three unique studies were created and conducted to evaluate quality attributes of ground beef and are presented throughout this thesis. Consumer friendly technology such as sous vide is growing in popularity and the industry application to use across the foodservice sector is eminent. Additionally, technologies such as electronic nose and electronic tongue provide researchers the ability to analyze sensory components of a meat product without the use of human subjects. The current studies will highlight fresh and cooked characteristics of ground beef patties, explore the impact of adding alternative proteins and cooking method on sensory attributes. Sous vide cookery has gained popularity among in-home and fine dining consumers, yet its application in quick-service settings remains limited. To address this gap, ground beef patties were produced to assess how sous vide cooking time affects moisture, color, and objective tenderness. Patties cooked for 30 minutes exhibited significantly greater cook loss, Allo–Kramer Shear Force (AKSF), and darker color (L*) compared to those cooked for 60 or 90 minutes (p 0.05). This indicates that sous vide cooking duration affects moisture, color, and texture characteristics of ground beef patties prior to grilling. In the second study, ground beef patties were cooked from frozen using various cooking methods. Evaluation of cooked patties show patties cooked on the griddle (GRID) exhibited significantly longer cooking times (p < 0.0001) compared to those cooked in the oven (OVEN) and clam shell (GARL). Additionally, GRID-cooked patties showed the highest percentage of cook loss compared to OVEN (p < 0.0001) and GARL (p = 0.0223). GRID-cooked patties required more Allo-Kramer shear force (p < 0.0001), indicating less objective tenderness compared to OVEN (p < 0.0001) and GARL (p = 0.0988). These findings highlight that the choice of cooking method significantly impacts the cooked characteristics of frozen patties. The final study aimed to evaluate the textural, color, and flavor characteristics, along with volatile compounds, of ground beef patties formulated with varying levels of beef heart inclusion. Patties were prepared with 0%, 6%, 12%, or 18% beef heart, with the remaining meat derived from shoulder clod. Overall, patties with beef heart did not require additional cooking time (p = 0.1325) nor exhibited higher cook loss (p = 0.0803). However, higher beef heart inclusion led to increased hardness (p = 0.0030) and chewiness values (p = 0.0316), deeper internal redness (p = 0.0001), and decreased consumer preference (p = 0.0367). These findings indicate that the inclusion of beef heart alters both the quality characteristics of ground beef and its consumer acceptability. Overall, results from these studies provide new foundational knowledge to an extremely popular consumer meat protein in ground beef that may elicit alternative processing and manufacturing techniques to consider throughout the meat and food industry
Comparative Utilization, Effectiveness, and Safety of Denosumab versus Zoledronic Acid in Patients with Metastatic Lung, Breast, and Prostate Cancer
No full textBackground: Metastatic lung, breast, and prostate cancer patients (MLBPC) experience several skeletal related events (SREs), which significantly decrease survival and quality of life. Denosumab (DS) and Zoledronic acid (ZA) are recommended treatments for these patients to prevent such SREs. However, there is a lack of evidence regarding the patterns and predictors of initiation, comparative effectiveness, and safety of DS and ZA using real-world data from older MLBPC patients in the United States.
Methods: We used Surveillance, Epidemiology, and End Results (SEER)-linked Medicare data from 2011-2019. We identified newly diagnosed MLBPC patients 66 years and older who initiated DS/ZA treatment within 12 months of cancer diagnosis. Demographic characteristics, healthcare utilization, comorbidities, and disease and treatment attributes of new users were evaluated during a washout period (12 months) prior to the first DS/ZA prescription. Trends in treatment initiation were assessed using Cochran-Armitage tests. To compare time to first SRE between DS and ZA new users, hazard ratios (HR) with 95% confidence intervals (CI) were obtained from both the unadjusted and adjusted (inverse probability of treatment weighting) Cox models. Competing risk regressions were performed with death as the competing risk outcome. Persistence to treatments and incidences of predefined adverse events were compared using Chi-square tests. Finally, SRE prediction machine learning models were trained and developed, including LASSO logistic regression (LR), random forest (RF), adaptive and extreme gradient boosting (AdaBoost & XGBoost), and support vector machine (SVM). Evaluation matrices including accuracy, sensitivity, specificity, area under the curve (AUC) were used. Baseline risk factors/features were analyzed using Shapley Additive Explanations (SHAP) values to rank variables important in predicting SRE.
Results: In 2012-2017, DS initiation trends increased across all individual cancer cohorts as well as the overall MLBPC sample, while ZA initiation notably decreased in the metastatic breast cancer (MBC) and MLBPC cohorts (all p<0.0001). Patients more likely to initiate DS over ZA were older at diagnosis, Hispanic, single, eligible for low-income subsidies, urban residents, had multiple comorbidities, impaired renal function, and prior chemotherapy use. DS significantly delayed the time to first SRE in MBC (adjusted HR=0.81, 95% CI=0.76-0.90) and metastatic prostate cancer (MPC) (HR=0.82, 95% CI=0.70-0.96) compared to ZA, but not in metastatic lung cancer (MLC) (HR=0.90, 95% CI=0.81-1.04). The results remained similar in the competing risk models. 1-year persistence with DS was significantly higher compared to ZA (72.4% vs 66.3%; p<0.0001) in MBC, and similar in MPC and MLC. Renal failure incidences were higher in ZA users (5.45% vs 4.31%, p=0.047 in MLC; 7.42% vs 4.91%, p=0.005 in MBC; 9.81% vs 6.76%, p=0.015 in MPC), while hypocalcemia was higher in MLC (5.62% vs 2.87%, p<0.0001) and MPC patients (7.19% vs 3.76%, p=0.001) receiving DS. In predicting SRE events, AdaBoost performed the best in both the DS (AUC=0.89) and ZA (AUC=0.90) user MLC cohorts. RF was the best performing models in both MBC and MPC with AUC values around 0.91. LR and DT performed poorly compared to the ensemble models and SVM with AUC values ranging between 0.69-0.77 in MLC, 0.72-0.79 in MBC, 0.70-0.72 in MPC. Significant differences according to the DeLong’s test (all p<0.05) were observed between the best and worst performing models. SHAP analysis revealed differences in feature importance ranking in predicting the outcome between DS vs ZA users within the same disease cohort.
Conclusions: This real-world study confirmed the increased trend in uptake and superior effectiveness in delaying SREs onset for DS compared to ZA in older MLBPC patients. The different safety profiles of these drugs may guide their uptake and warrant patient monitoring. The novel machine learning models performed well in predicting SRE and identified the most important predictors of SRE in older MLBPC patients receiving DS/ZA
Influence of Synthesis Conditions on Photo-Electrochemical Performances of CuGaO2
No full textIn recent decades, our changing energy needs have significantly driven chemical research on metal oxides. The continuous innovation of new materials and the investigation of their properties for energy conversion devices are expected to deepen our understanding of the interesting electrochemical behavior of various metal oxides. This thesis explores the systematic design and synthesis of metal oxide and metal nanostructures, examining their roles in solar energy conversion and catalysis. The research covers a broad spectrum of materials chemistry, from fundamental aspects like the impact of synthesis conditions to more practical applications, including the design of dye-sensitized photoelectrochemical cells to evaluate the performance of the synthesized materials in p-type DSSCs. Chapter 2 presents an application using semitransparent CuGaO2 nanoplates as photocathodes in dye-sensitized photoelectrochemical cells. My research has focused on understanding the interfacial charge transfer properties and electronic structure of delafossite CuGaO2 nanoparticles produced via hydrothermal methods. This study utilized variations in synthesis parameters, such as precursor pH and post-synthetic annealing atmosphere, to passivate surface defects. An optimal thickness of 1.5-2 μm was achieved, yielding the highest charge collection efficiency for samples synthesized at pH 9. Additionally, H2 annealing further enhanced the photocurrent for samples synthesized at pH 9. In Chapter 3, a systematic investigation was conducted into the impact of synthesis temperature on CuGaO2 at pH 9. Employing Mott-Schottky analysis provided insights into acceptor state densities and apparent flat-band potentials (Efb). Notably, the Efb values of the CuGaO2 exhibited a gradual decrease with higher reaction temperature, indicating a decrease in the Fermi energy level of the charge carriers. In constructing p-type dye-sensitized photoelectrochemical cells using the CuGaO2 films, the photovoltage increased with the rising temperature up to 2300C. The optimal nominal temperature for the synthesis was determined to be 2300C, which contributed to enhancements in both carrier lifetimes and observed photocurrent as well as photovoltage within a three-electrode photoelectrochemical cell and reduced the carrier transport times within the nanoparticle semiconductor network. Finally, in Chapter 4, we explored the application of Surface Enhanced Raman Spectroscopy
(SERS) to monitor the decarboxylation of 4-mercaptobenzoic acid (4-MBA) and its isomers. This method offers insights into chemical reactions occurring near metal nanostructures, helping to elucidate the reaction pathway and identify new species