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Synergistic and Competitive Behavior of Anisotropic Particles and Surfactant Molecules at Fluid Interfaces
Get PDFColloidal systems are a common feature in different application domains, from complex underground frameworks such as petroleum recovery and water remediation to industrialized consumer products such as cosmetics and paint. These systems usually consist of multiple components, such as nanoparticles, polymers, surfactants, and ions exhibiting unique properties that are different from those of simple fluids. In addition, in many applications involving the water-energy nexus, these complex fluids come in contact with surfaces and interfaces. For instance, an enhanced oil recovery operation usually consists of flooding a multicomponent fluid (e.g., aqueous solution of surfactants and polymers) through a reservoir rock that has heterogeneous porosity and anisotropic permeability. Another example is the treatment of produced water generated from the oil industry to separate the organic contaminants that are stabilized by surface-active species present in both phases. Given the range of phenomena that take place simultaneously, from the dynamic wetting of complex fluids on a solid surface to interfacial stresses and deformations at a fluid interface, advancing our understanding of the key factors that govern the interactions present in such systems and their resulting properties, will lay the foundation for the engineering of complex interfacial systems with a tailored set of properties.
To decode the behavior of multicomponent fluidic systems, it is crucial to understand the role of components present, both individually and synergistically, in the governing physics. For instance, surfactants, as amphiphilic molecules, populate the interface providing interfacial stability via a reduction in interfacial tension. In contrast, colloidal particles stabilize an interface by occupying the contact area between the two fluids. Hence, attributes such as particle size and surface chemistry can be used to control their position at the interface and the resulting reduction in the free energy. Moreover, particles encountered in real-world applications, such as those encountered in foam flotation for ore recovery, often diverge from the case of isotropic homogeneous particles; thus, it is crucial to understand the impact of heterogeneities present in such scenarios, on the resulting interactions, by studying model particles. These include particles that are rough and possess anisotropies either in shape or surface chemistry. A classic example of a chemically anisotropic particle is the Janus Particle (JP), named after the two-face Roman God. The Janus motif brings unique functionalities to a multicomponent system, augmenting the number of variables that can be tuned to achieve the desired outcomes. Although there is some literature with respect to the dynamics of particles and surfactants at interfaces, little information is available on mixed systems, especially for heterogenous and anisotropic, yet technologically relevant, particles.
The present work is dedicated to probing the roles different surface-active species (i.e., surfactants and nanoparticles) paly in resulting interfacial phenomena such as surface pressure, mechanical properties when subjected to dilational or shear stresses, and the stability of multicomponent fluidic systems. We investigate various features that can be employed in tuning the competitive vs. synergistic behavior of species that make up the system and determine the role non-idealities play in tuning the properties of the populated interface and stability of the interfacial systems. The knowledge base achieved from this PhD work, will equip us with a fundamental understanding of the key attributes in design and engineering of solutions to address real-life challenges in a broad range of applications from wastewater remediation to increasing the shelf-life of a product
Piano Instructors’ Experiences in and Perceptions Of Preparation, Success, and Confidence Teaching Students with Neurodevelopmental Disabilities
Get PDFThe purpose of this study was to investigate piano instructors’ perceived levels of confidence, success, and preparation in teaching students with neurodevelopmental disabilities. A secondary purpose of the study was to learn about the varied contexts in which piano instructors learn how to teach students with neurodevelopmental disabilities. Specifically, I wanted to examine (a) how confident and successful piano instructors believed themselves to be in teaching students with neurodevelopmental disabilities, (b) to what extent did piano instructors include students with various neurodevelopmental disabilities in their piano studios, (c) which neurodevelopmental disabilities did piano instructors have familiarity and experience with, and to what degree, and (d) in what contexts did piano instructors learn to teach students with neurodevelopmental disabilities.
Historically, these exceptional students have been marginalized in educational practice, and while movements in classroom education have sought to ameliorate many issues, research and practice in applied music instruction has not kept pace. To date, there are no requirements for special education coursework for piano pedagogy or applied piano majors at any level, according to the National Association of Schools of Music standards. With millions of people affected by neurodevelopmental disabilities, it is likely that piano teachers will teach students diagnosed with neurodevelopmental disabilities, whether they are prepared or not.
Non-collegiate piano instructors were recruited from the Music Teachers National Association and from social media sites to complete the survey and data were collected from N = 749 piano instructor respondents in the spring of 2023. Findings indicated that (a) respondents were willing to teach students with neurodevelopmental disabilities in at least some circumstances, (b) most teachers had taught or were currently teaching at least one student with a neurodevelopmental disability, and (c) that they felt underprepared to do so. Findings also showed that confidence was higher when the respondent had taken at least one course addressing neurodevelopmental disabilities. Implications for piano instructors, future or “pre-service” piano instructors, and piano pedagogy professors are discussed
Additive Manufacturing And Synthesis Of Advanced Antibacterial And Sensing Photocurable Polymer Nanocomposites
Get PDFThis work covers the synthesis, manufacturing techniques, and characterization of several novel nanocomposites produced with direct light processing-based additive manufacturing systems. Custom thermodynamic and UV control systems are implemented for photocurable resin systems to synthesize and manufacture up to 14 novel nanocomposites. These novel nanocomposites are synthesized to improve tensile strength, wear resistance, water contact angle, antibacterial resistance, and heat dispersion. Low-cost market-available printing systems are utilized with custom-designed enclosures and monitoring devices to allow printing with novel nanocomposites. This research discusses the advanced properties offered by each nanocomposite utilizing titanium dioxide and zinc oxide in weight concentrations as high as 7.5%. Carbon nanotubes (CNT) are also utilized in weight concentrations of up to 1%. A total of 5 different base resins are tested to demonstrate the effect of different oligomers and monomers on creating a matrix suited for nanocomposite addition. Secondary functions such as strain sensing are evaluated for human motion detection utilizing custom printed sensors, and ultimate tensile strength increases of over 200% are observed. The base matrix strength is improved with CNT concentrations as low as 0.5%. Wear resistance is documented to improve by over 15% in titanium dioxide and zinc oxide samples at 7.5% weight concentrations. Optimization studies are performed to determine the cure rate and in situ curing characteristics' effect on final part strength for CNT nanocomposites. Lastly, CNT alignment is explored through modification of printing parameters and resin viscosity curves to aid in part strength. This work demonstrates the ability to manufacture nanocomposite-reinforced parts that exhibit significantly improved physical, thermal, electrical, and antibacterial properties at high fidelity on low-cost hardware ideal for the medical, automotive, and aerospace sectors
Visibility Estimation from Camera Images Using Deep Learning
Get PDFAtmospheric visibility is an important and complex meteorological variable that directly affects safe and reliable transportation. Specifically, declining visibility can pose an increased risk to automotive, aviation, and maritime traffic and operations. Traditional visibility sensors, e.g., those of the Automated Surface Observing Systems (ASOS) network, are costly and designed for air traffic use, thus these visibility sensor networks have limited coverage state-wide. In contrast, camera footage is highly available, accessible, and fairly inexpensive. While it is possible to construct a model that detects a visibility measure for a single camera or location, this type of model is not generalizable to new locations with varying physical features or different fields of view. I propose a comparative visibility model that is generalizable solution to new locations. I train a convolutional neural network (CNN) that compares a query image and a reference image that originate from the same camera, and determines the degree to which the query image is less visible than the reference image. A query image from a new camera can then be compared to a set of reference images with known visibility distances from the same camera. These comparisons can then be used to infer the query image’s underlying visibility distance. In addition, a model can be trained using a set of locations that have different maximum visibility distances, fields of view, and physical characteristics. The resulting comparative model can generalize to novel sites. When combined with a small number of calibrated reference images for a given site, visibility distances can be accurately estimated from previously unseen query images. Results from a large combined NYSM/ASOS data set show that the models learned using the proposed method are able to generalize to new locations. The approach is successful in the comparative case and the numerical visibility prediction case. With these outcomes, the model is also able to effectively monitor visibility over time
Circadian rhythm variation in endocrine biomarker responses to high-intensity interval training in college aged males
Get PDFThe purposes of this study were to: 1) to examine the effects of time of day on salivary testosterone (sal-T) and salivary cortisol (sal-C) concentrations following acute bouts of high intensity interval training (HIIT) exercise performed in the morning and evening; and (2) to examine diurnal variations in the T:C ratio responses to HIIT exercise; and (3) to determine if there is a relationship between sal-T and sal-C responses to acute bouts of HIIT exercise and muscle mass. Ten men between the ages of 20-25 years who reside in the Norman or Oklahoma City Metro area were recruited for this study. Eligibility for participation was assessed using medical history and physical activity readiness questionnaire as well as completing the written informed consent and HIPAA agreement. Additionally, a dual energy x-ray absorptiometry (DXA) total body scan assessing bone-free lean body mass, fat mass, and percent body fat as well as a maximal graded exercise test to determine maximal oxygen uptake (VO2peak) was performed prior to participation in the intervention sessions. This randomized cross-over design assessment was aimed to determine whether or not HIIT exercise performed in the morning or evening effects sal-T and sal-C concentrations as well as determining if a relationship exists between bone-free lean body mass and the percent changes in the sal-T and sal-C responses. The findings from this study were: significant increases were observed for the sal-T exercise response (PRE to IP); significant decreases were observed for the sal-C time of day response (evening); and significant increases were observed for the T:C ratio time of day response (evening). In conclusion, sal-T and sal-C responses to acute bouts of HIIT exercise were not affected by time of day and no significant relationships existed between bone-free lean body mass variables and percent changes in sal-T and sal-C in this study
Recruiting, Hiring, & On-Boarding Non-MLS Liaison Librarians: A Case Study
Get PDFA case study of how the University of Oklahoma Libraries recruited, hired, and then on-boarded three Science Liaison Librarians who held advanced subject degrees but no Masters in Library Science. This study provides suggestions for modifying job postings, interview processes, and on-boarding to appeal to non-MLS subject experts and to fully inform them of the scope of liaison work. After providing a brief overview of our work, we will engage the audience in a facilitated conversation about the issue and potential impact for our profession.N
SynthesAIzing Discoveries: Emerging tools for next-level research instruction
Get PDFGenerative AI tools like ChatGPT are new and exciting, evoking buzz and triggering anxiety. What about less-known AI tools that synthesize information, tools with transformative potential for how we teach info and research skills? We approached an expert with our timely question: What do we need to know about how AI empowers research and sharpens information literacy skills? Our expert—an AI tool called Perplexity—responded by asserting that ChatGPT and other generative AI tools are “neither inherently good nor bad when it comes to finding and using information. Instead, they represent a new way in which we can interact with information.” Perplexity then centered the ACRL Framework for Information Literacy (2016), reminding us of the importance of understanding scholarship as an ongoing conversation; this, for us, begs a looming question: looming question: How do we meaningfully engage AI—and teach others to engage AI—as a conversation partner for next-level research? At the COIL Annual Conference, Michael Hanegan and Chris Rosser will introduce hearers to the potential of synthesizing AI tools like Perplexity for empowering learning and research, emerging tools that will profoundly inform new approaches to information literacy instruction. Our AIm is to equip instructors with tools and tactics for engaging synthesizing AI as a next-level partner for leveling up research.N
Minutes of a Regular Meeting, The University of Oklahoma Board of Regents, September 13, 2023
No full textExchange Rate and Agricultural Trade: Evidence from Iran
Get PDFIran has been consistently running a trade deficit in agricultural products. Conditional upon domestic and global output
as well as oil exports, we find that the effective real exchange rate plays a significant role in perpetuating this deficit.
While we find no evidence of any J-curve dynamics, our results suggest that the effects from currency appreciation is
of greater importance when compared to depreciation.YesThis article was originally published in Vol. 43 No. 1 of Economics Bulletin
