University of Illinois at Chicago
University of Illinois at Chicago: UIC INDIGO (INtellectual property in DIGital form available online in an Open environment)Not a member yet
21439 research outputs found
Sort by
Telehealth Effect on Show Rate in Neonatology Follow Up Clinic: A Prototype Model
No full textThis prototype study evaluates the impact of telehealth on reducing no-show rates in a neonatology follow-up clinic using a randomized quasi-experimental design, while assessing the feasibility of a future comprehensive intervention. Targeting a reduction in no-show rates from 25% to 15%, the study employs a telehealth pamphlet as an instrumental variable in a two-stage estimation model. This prototype model uses generated data for 650 participants, offering a structed framework for this analysis. Results demonstrate that telehealth use is associated with lower neonatal follow-up clinic no-show rates, underscoring the potential for telehealth to improve healthcare access and outcomes for infants and families. The results of this prototype study support the implementation of a comprehensive intervention project based on a quasi-experimental design to test the potential effectiveness of offering telehealth to help reduce no shows rates in neonatology follow-up clinics
Physical Activity and Brain Health in Older Latino Adults: Is There a Moderating Role of Acculturation
No full textThree complementary studies were conducted to examine the role of language-based and social-based acculturation in the relationship between physical activity (PA) and cognitive or brain health among older Latino adults. Manuscripts one and two were cross-sectional studies, and manuscript three was a randomized controlled trial using secondary data analysis.
Manuscript one aimed to examine whether language-based and social based acculturation moderated the relationship between self-reported PA and cognition in older Latino adults. Using baseline data from 200 non-demented participants in the Rush Alzheimer’s Disease Research Center Latino Core study, results showed that language-based acculturation moderated the associations of PA with working memory and semantic memory, such that higher language acculturation strengthened both relationships. Social based acculturation also moderated the PA–working memory link, with stronger associations observed among those with lower social acculturation. These findings indicate that distinct acculturation domains uniquely shape how PA relates to specific cognitive functions in older Latinos.
Manuscript two aimed to examine whether language-based and social based acculturation moderated the relationship between physical activity and brain structure, specifically total gray matter and white matter volumes in older Latino adults without dementia. Using cross-sectional data from 55 participants in the RADC Latino Core study, MRI results showed that social-based acculturation significantly influenced the association between physical activity and total gray matter volume. Participants with lower social-based acculturation exhibited a stronger positive link between physical activity and gray matter volume. There were no significant effects of language-based acculturation and no effects on white matter volume. These findings suggest that older Latinos with lower social based acculturation, who may retain more culturally rooted social networks, could derive greater brain structural benefits from physical activity. This highlights the importance of sociocultural context when designing physical activity interventions to support healthy brain aging.
Manuscript three assessed whether language-based acculturation and social based acculturation moderated changes in physical activity and cognitive performance over time among older Latino adults enrolled in the BAILAMOS Latin dance program or a health education control. The analysis revealed that higher social based acculturation enhanced gains in semantic memory among dance program participants, while no moderation was observed for language-based acculturation or for changes in physical activity or other cognitive domains. This suggests that although a culturally tailored dance intervention benefits all participants, social based acculturation may selectively amplify certain cognitive improvements.
Together, these studies demonstrate that language-based acculturation and social based acculturation shape cross-sectional associations between physical activity and both cognitive function and brain structure, while culturally grounded interventions can benefit older Latino adults across all levels of acculturation, with social based acculturation providing additional cognitive advantages
Nanoparticles Targeting at Inflammation Site
No full textThis thesis presents the development and translational evaluation of advanced nanoparticle-based drug delivery systems designed for the sustained and localized delivery of hydrophobic therapeutics, with a focus on pazopanib. Addressing key challenges in drug solubility, stability, and scalable manufacturing, this work explores polymeric, lipid-based, and peptide-modified nanocarriers tailored for chronic disease applications.
In the context of osteoarthritis (OA)-associated pain, two classes of biodegradable polymeric nanoparticles—PEG-b-PCL and PLGA—were investigated for their distinct drug release kinetics. PEG-b-PCL enabled prolonged, near zero-order release, whereas PLGA exhibited a rapid burst release. These insights informed the development of a clinically translatable formulation, PEG-PCL-NanoPaz-t, produced via flash nanoprecipitation followed by spray drying. This scalable approach achieved over a 9,000-fold increase in production rate and maintained the therapeutic efficacy of pazopanib in a canine OA model, demonstrating extended pain relief and improved drug solubility.
In parallel, the formulation and stability of lipid nanoparticles (LNPs) were enhanced through rational surface engineering. A comparative study between conventional PEG-lipids and zwitterionic peptide–lipid conjugates revealed that C(EK)₄-modified LNPs significantly improved membrane integrity and reduced enzymatic degradation, as demonstrated by synchrotron-based X-ray scattering. These EK4-modified LNPs successfully encapsulated pazopanib and exhibited long-term colloidal stability, suggesting their potential application in targeted renal carcinoma therapy.
Collectively, this work highlights how material-driven design can overcome pharmacological and manufacturing bottlenecks in nanoparticle drug delivery. The results contribute broadly to the development of robust nanocarriers for chronic disease treatment, supporting future clinical translation
Application of the AI-Enabled Unmanned Aerial Systems in Resolving Highway Construction Claims
No full textClaims are among the most concerning disturbances in the construction sector universally. Simply speaking, they arise from delayed procedures in answering inquiries, solving conflicts, and clarifying vague data to owners. Their development and delayed solution could pose many kinds of threats, like complex court disputes from simple arguments and controversies, contributing to arduous obstacles to complete the whole project progress flexibly. They consume much energy, time, financial resources, and focus from project managers, contractors, seniors, and consultants. Swift response to solve and handle such issues is crucial as their lengthy prevalence would create further problems, resulting in more complexity and common enterprise concerns of cost overruns and delay of the submission schedule. Even the latest building information modeling (BIM), project management (PM) techniques, risk management (RM), and other innovative PM advancements may face different hurdles to provide comprehensive handling. But trials and research and development (R&D) are still conducted, aiming to configure holistic strategies of such challenging affairs. Unmanned aerial vehicles (UAVs) have been innovated and limitedly practiced in specific military uses. Nonetheless, with the rapid boom of digitalization and its involvement in every field, UAV applications have changed into more beneficial, safe, cost effective, and practical uses. Despite their common limitations of public privacy and safety penetration, the scale of positive awareness, interest, and knowledge in many fields, like construction, are overcoming these restrictions. Additional proof is provided, day after day, of their relevance and contributions to conduct many construction activities efficiently and profitably through several UAV-supervised piloting and operating trials in construction and other disciplines.
In this context, this study aims to supply sufficient evidence-based practices necessitated for many construction stakeholders, on UAV feasibility, practicality, and reliability to take a part in managing construction claims actively. Strictly speaking, their importance in this domain is reflected in conducting high-performance, accurate, swift, flexible, and massive data collection since of their high maneuverability capability to cover and inspect large surface areas, offering an alternative option of routine, risky, technically complex, and lengthy physical inspection processes (PIPs) implemented broadly nowadays. Thus, they can minimize much cost, time, risks, and challenges to perform such activities, especially if some acute weather conditions or natural disasters exist. Also, their contribution can be remarkably realized for performing active, broad inspection and coverage of massive surface areas of vertical construction (VC) projects and utility-scale infrastructure city enterprises. For claim consultants and many project stakeholders, this capability is a significant advantage since it helps accelerate and facilitate the management of time-consuming, complex, and energy-exhausting claims and disputes. “UAVs to manage construction claims” is a very rarely discussed subject. It is considered a significant knowledge gap, which needs to be bridged. The current study relies on one secondary data collection (systematic review and meta-analysis of the current knowledge body). Besides, it utilizes four primary data collection approaches, namely cross-sectional quantitative data collection and analysis by online survey questionnaires, covering expert construction engineers in Illinois Chicago, cracking severity analysis by artificial intelligence (AI) algorithms, certainly high-performance deep learning (DL) frameworks, and holistic examination of different local, public, and federal legislative frameworks to facilitate and enable active UAV application in many construction activities.
A framework containing recommended amended acts is formulated to allow these regulations to support UAVs in recording helpful data for swift and flexible court claim or complex dispute management. The overall outcomes from this research revealed a collection of important statistical facts and imperative data. Firstly, the secondary data collection, from the systematic review and meta-analysis, uncovered rapid growth of UAV involvement in diverse promising applications and contributory utilizations, helping supply enhanced accuracy, high-quality data, and savings of energy, time, labor effort, and cost resources. The literature reported that UAVs can make beneficial photogrammetry tasks, massive and rapid aerial inspection of various types of construction sites, which are difficult to reach. UAVs can develop daily progress reports, take precise visual image or video records of critical data, share in many influential tasks of PM and RM in normal conditions or at natural disasters. They can provide critical risk estimation data for risk decision makers and responsible authorities to save cost, equipment, and human resources. The estimated Cronbach's Alpha coefficient of the survey dimensions was 0.885, indicating higher internal consistency among the items, exceeding the recommended threshold of 0.7. Approximately, through all survey articles, more than 80% of the overall surveyed engineers (135 individuals) have affirmed the relevance, contributions, advantages, and profitability of UAVs in construction to manage construction claims flexibly, efficiently, and functionally. In-depth legislation analysis of UAV-related laws, notably UAV 620 ILCS 5/42.1, UAV 725 ILCS 167, Federal Aviation Administration (FAA)/ Part 107 Law, Public Act 100-0735, and Public Act 725 ILCS 167 has identified many significant gaps, application voids, and affairs that need to be carefully taken into consideration by UAV supervisors, construction stakeholders, and aviation law makers to ease UAV integration in construction for many advantageous applications, specifically for practical construction claim and dispute management (CC&DM) and alternative dispute resolution (ADR). Concerning the critical AI classification outcomes, it was found that convolutional neural networks (CNN) DL framework has enabled a fully automated process of high-way bridge cracking severity prediction existing in different walls and structures, which is considered technically very complex, costly, and time-consuming. CNN provided an accuracy, reliability, and effectiveness ratios of over 95%, identifying ‘critical’ from ‘noncritical’ cracks. The CNN DL recognition process is aligned with the American Association of State Highway and Transportation Officials (AASHTO) code
The Experience of Korean Immigrant Parents of Children with Autism: Partnership and Inclusion
No full textThis dissertation explored the collaborative experiences of Korean immigrant parents raising children with autism as they partner with teachers in inclusive educational settings. Despite the growing population of Asian immigrant families facing developmental disabilities, research remains limited on how Korean immigrant parents navigate teacher collaboration for special education services, particularly in inclusive education contexts. This study aimed to understand Korean immigrant parents' perspectives on effective partnerships with educators, including the benefits, challenges, and recommendations for improving inclusive education outcomes. Using transcendental phenomenological methodology (Moustakas, 1994), three in-depth interviews were conducted with Korean immigrant parents of children with autism who had experience collaborating with teachers in inclusive settings in the United States. Findings revealed that Korean immigrant parents (n = 11) emphasized the critical importance of culturally sensitive teaching practices that acknowledge Korean cultural values and communication styles. The study identified key facilitators of successful collaboration, including bilingual support, culturally competent educators, and family-centered approaches to inclusive education. Parents highlighted the need for teachers to understand Korean cultural perspectives on disability, family dynamics, and educational expectations. The research demonstrates that effective parent-teacher partnerships in inclusive education require intentional cultural responsiveness and systematic efforts to bridge cultural differences. These findings contribute to the literature on culturally responsive inclusive education and provide practical implications for preparing educators to work effectively with culturally and linguistically diverse families. The study recommends comprehensive cultural competency training and the development of culturally adapted collaboration frameworks to enhance inclusive education outcomes for future Korean immigrant families raising children with autism
Electrochemical Pathways to Decarbonizing Urea and Ammonia Production
No full textClimate change presents one of the most urgent challenges of the 21st century, and chemical engineers have a critical role to play in addressing it by designing processes that not only minimize carbon emissions but also have the potential to reverse them. This thesis focuses on decarbonizing the production of ammonia, a chemical indispensable to global food security, yet responsible for a substantial share of global CO₂ emissions due to its synthesis via the highly energy-intensive Haber-Bosch process.
Ammonia is traditionally synthesized at high temperatures and pressures to activate molecular nitrogen (N₂) and achieve acceptable reaction rates. This work explores the possibility of driving the reaction electrochemically using voltage instead of thermal energy, thereby enabling nitrogen activation under ambient conditions. The long-term vision is to develop a decentralized “black box” system that utilizes air, water, and renewable electricity to continuously generate ammonia on-site at the point of use, such as agricultural fields.
As an initial step toward this goal, lithium-mediated ammonia synthesis (LiMAS) was investigated. Lithium metal spontaneously reacts with N₂ to form lithium nitride (Li₃N), which can subsequently be protonated to yield NH₃. A non-aqueous electrochemical system was developed and optimized by varying the lithium salt (e.g., LiClO₄ vs. LiBF₄), current density, nitrogen pressure, proton donor type, and concentration. Under optimized conditions, employing 3 M LiBF₄ in tetrahydrofuran with 0.065 M ethanol as the proton source and 20 bar N₂, Faradaic efficiencies of up to 70% were achieved at −100 mA/cm². Despite these promising results, lithium remains neither cost-effective nor earth-abundant, making it unsuitable for large-scale or distributed applications.
To address this limitation, calcium (Ca) and magnesium (Mg) were evaluated as alternative mediators. Theoretical screening based on nitrogen binding energetics and nitride stability indicated that both metals could support comparable nitrogen activation mechanisms. Electrolyte systems were developed to facilitate reversible Ca and Mg plating in non-aqueous media. Using dimethoxyethane (DME) as the solvent and operating at 6 bar N₂, calcium-mediated systems achieved ammonia Faradaic efficiencies of 50% ± 0.2%, while magnesium achieved 27% ± 2%. Post-reaction characterization, including ¹⁵N₂ isotope labeling and in-situ Raman spectroscopy, confirmed that the observed ammonia originated from molecular nitrogen and that metal nitrides were formed during the process.
In support of decentralized ammonia generation, a reliable and accessible ammonia detection platform was also developed. A miniaturized paper-based colorimetric sensor was created by adapting the Berthelot reaction into a drop-test format. This sensor reduced the sample volume requirement by over 100-fold and decreased the analysis time by a factor of three compared to conventional techniques. With a detection limit of 35 μM and strong correlation with NMR results, the sensor proved effective in both laboratory and field settings, including challenging matrices such as wastewater and post-electrolysis solutions.
Further progress in ammonia synthesis was hindered by the reliance of batch-mode systems on sacrificial proton donors (e.g., alcohols), which limited their long-term sustainability. To address this, a continuous-flow electrochemical system was designed to mimic the Haber-Bosch process using H₂ and N₂ as feed gases. This approach decouples hydrogen oxidation at the anode and nitrogen reduction at the cathode, eliminating the need for sacrificial donors. The system incorporated nickel-based anodes, optimized electrolytes for reversible metal plating, and in-situ Raman spectroscopy for tracking nitride formation. Magnesium, in particular, exhibited moderate but stable nitride formation kinetics suited for continuous operation, and the system demonstrated consistent ammonia generation over time.
Overall, this thesis demonstrates significant progress toward sustainable ammonia synthesis by advancing the use of earth-abundant mediators, improving reaction selectivity, enabling on-site ammonia detection, and transitioning from batch to continuous operation. Together, these contributions move the field closer to realizing decentralized, low-carbon ammonia production powered by nitrogen, hydrogen, and renewable electricity
Baryon-to-Meson Ratios in Jets from Au+Au and p+p Collisions at 200 GeV
No full textExperimental results from RHIC and the LHC reveal a significant enhancement in baryon-to-meson yield ratios at intermediate in high-energy nuclear collisions relative to + collisions. Strong hydrodynamic expansion and parton recombination within the QGP are believed to be responsible for this enhancement. Jets have served as powerful probes of QGP properties, with numerous measurements revealing significant modifications to jet yields and internal structure. LHC measurements reveal that, despite observable medium effects on jet fragmentation, the baryon-to-meson ratios within jets remain consistent with + values and are distinct from those of the surrounding QGP. We probe this behavior at RHIC by applying time-of-flight and TPC -based particle identification in conjunction with jet-track correlation analysis to extract in-jet particle ratios for GeV/. This work reports the first observation of in-cone baryon-to-meson yield ratios for fully reconstructed jets in both + and Au+Au collisions at GeV, based on STAR data at RHIC. The in-jet ratios show little variation between Au+Au and + collisions in the examined kinematic regime, contrasting with the pronounced differences seen in inclusive particle ratios
Leveraging Digital Phenotyping to Predict Outcomes in Youth at Clinical High-Risk for Psychosis
No full textBackground: The majority of individuals who experience a psychotic episode describe subacute symptoms in the months or years prior to their conversion to psychosis. Social functioning impairments are common in individuals with psychosis and predict the transition to psychosis in young people at clinical high-risk for psychosis (CHR-p). Our understanding of the relationship between offline and online social functioning, as well as digital social functioning on illness progression and clinical symptoms, is limited by current assessment methods. Digital phenotyping (the use of mobile devices to collect data) may address these limitations and holds promise given the ubiquity of smartphones. The current study combined passive and active digital phenotyping data to examine: (1) group differences in smartphone and social media use across CHR-p and healthy control (HC) participants, (2) concordance of offline and digital social behavior measures, (3) associations between clinical symptoms (risk for conversion to psychosis, anxiety, and depression), offline social behavior and interest, and social media use in CHR-p participants, and (4) the relationship between clinical symptoms (risk for conversion to psychosis, anxiety, and depression), offline social behavior and interest, and the reciprocity of text messages and phone calls in CHR-p participants.
Methods: CHR-p participants (n=132) and HC participants (n=61) completed clinical interviews and 6 days of digital phenotyping data collection. Frequency of offline social interactions and momentary social interest was collected through ecological momentary assessment surveys. Social media application usage was collected for Instagram and Facebook. The number of outgoing and income Snapchats, text messages, and phone calls was collected, and ratios were calculated. Time spent per day in active and passive social media use was self-reported at study clinical interview. Aims were examined using independent samples t-tests, Spearman correlations, multiple regressions, and multiple mixed-effects regression models.
Results: CHR-p participants reported significantly less daily time passively using social media compared to HC peers, but both groups demonstrated comparable daily active social media, and overall screen, time. CHR-p participants did not demonstrate a significant relationship between their social media activity (passive use, active use, Instagram time, Snapchat ratio) and their offline social behavior and social interest. No social media activity significantly predicted risk for conversion to psychosis, anxiety, nor depression, over and above demographic variables, within the CHR-p group. Finally, fewer outgoing texts (relative to incoming) uniquely predicted one- and two-year risk for conversion to psychosis, but not anxiety or depression, within the CHR-p group.
Conclusion: Results point to a nuanced digital social landscape with divergent relationships from offline social behavior and unique, clinically meaningful features for CHR-p youth. By updating our understanding of the digital social landscape, assessment tools to measure it, and clinical consequences for young people at CHR-p, we become closer to high precision identification and intervention strategies
3D Bioprinting of High Cell Density Bioink for Chondrogenic Differentiation in Replacement Tracheal Rings
No full textTracheal stenosis represents a complex medical challenge due to its debilitating effects on respiratory function. This condition is characterised by the narrowing or obstruction of the tracheal lumen, often resulting from trauma, prolonged intubation, infection, or congenital abnormalities. Severe cases can be life-threatening, significantly impacting the quality of life and leading to respiratory distress. The limitations of existing treatment methods, especially for long-segment tracheal stenosis, mean that novel solutions are required to improve long-term outcomes in such cases.
In this study, tissue engineered constructs aiming to mimic the native tracheal tissue's mechanical, structural, and biological properties were fabricated using a high cell density 3D bioprinting system into a support bath of oxidised, methacrylated alginate (OMA) hydrogel. A comprehensive characterisation of various formulations of the hydrogel was conducted to tabulate rheological and mechanical properties, and identify compositions providing a broad range of stiffness supports. Further analysis was then conducted on the constructs to identify the quality of differentiation, maintenance of geometry, and viability of cells when cultured in the different compositions. Future work involving the incorporation of growth factor loaded microparticles within the bioink was also examined in brief in the form of cell aggregates.
In order to treat a difficult-to-manage disease state, this study demonstrated the practical application of a novel 3D bioprinting technique, and characterised in greater detail the biomaterials used. These findings help further the application of this high cell density 3D bioprinting technique towards the treatment of tracheal stenosis, and as part of the broader tissue engineering toolkit
Analysis and Chemical Applications of Metal–Metal Bonds and Large Language Models
No full textAdvances in catalysis, energy storage, and chemical education increasingly rely on integrating molecular design, predictive modeling, and emerging technologies. Yet challenges remain in tuning metal–metal interactions, stabilizing reactive battery interfaces, and improving access to data interpretation in chemical research and education.
This work addresses these challenges through a combination of synthetic chemistry, computational modeling, electrochemical analysis, and artificial intelligence (AI). Redox–active dimolybdenum paddlewheel complexes were developed to probe second–sphere charge effects, revealing a quantifiable relationship between ligand charge and Mo≣Mo redox behavior. Building on this platform, a quadruply bonded dimolybdenum–based organometallic additive was introduced into a NaPF₆/dimethyl carbonate electrolyte to improve sodium metal battery performance. This strategy addressed persistent issues with interfacial instability and dendritic growth, promoting the formation of a more uniform and stable solid electrolyte interphase and enabling improved cycling efficiency. To expand the application of data–driven molecular design, bond dissociation energies and reactivity trends in aluminum–metal complexes were modeled using density functional theory and multivariate regression. These models enabled predictive catalyst development using earth–abundant metals, offering new insight into cooperative bond activation mechanisms.
Beyond molecular systems, this study also explores how artificial intelligence can support chemical practice. Large language models (LLMs) were evaluated for their effectiveness in supporting chemical safety and education. One study assessed their ability to generate accurate and relevant lab safety guidance. Another demonstrated their potential to assist students with quantitative data analysis and graphical interpretation. These findings highlight the promise of LLMs as supplemental tools when used with appropriate oversight.
Together, this dissertation integrates experimental design, theoretical modeling, and emerging digital tools to tackle pressing challenges in modern chemistry. The findings provide new strategies for redox tuning, battery optimization, catalyst development, and responsible AI integration in chemical practice
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇