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Optoelectronic and Electrochemical Properties of Hybrid Transition Metal Dichalcogenide Heterostructures
No full textTransition metal dichalcogenides (TMDs) have attracted significant attention in recent years with their immense potential to revolutionize optoelectronics and electrochemical energy applications. However, several challenges have prevented their practical use, including fabrication difficulties, incompatibility with conventional doping techniques, and unwanted environmental effects. This thesis aims to address the issues by introducing novel strategies for transforming TMDs into organic-integrated hybrid structures. Furthermore, this study focuses on gaining a fundamental understanding and a tunability of the unique physical properties of TMDs. Finally, to unlock their full potential, this thesis explores synergetic effects among the hybrid components for the development of advanced optoelectronics and energy devices.By combining atomically thin TMDs with uniform organic layers, we have developed various two-dimensional (2D) hybrid junctions, including TMD/organic, TMD/TMD/organic, and TMD/organic/TMD. The TMD/organic hybrids are designed for type-II energy band alignments at the heterointerface and exhibit significantly improved (photo)conductivity and uniform photoresponse compared to pristine TMDs. The optoelectronic characteristics vary as a function of the layer number of TMDs, one of the unique features of ultrathin materials. We also find that integrating organic layers can tailor the charge density and polarity of TMD flakes, thus enabling controllable doping without damaging the crystallinity.The hybrid approach not only modulates the properties of individual TMD layers but also offers an opportunity to study unique phenomena of 2D heterostructures such as interlayer excitons (XIs). XIs are spatially separated bound states of an electron and a hole in TMD/TMD heterolayers. We prepared various TMD/TMD/organic hybrid heterostructures with distinct energy band alignments and demonstrated a selective modulation of XI emission. The photoluminescence from the radiative recombination of XIs can be preserved, quenched, or modulated based on the band alignments. Furthermore, we fabricated organic-layer-inserted heterolayers (TMD/organic/TMD) and investigated the environmental effects on XIs. The organic layers tailor the dielectric screening within XIs and the dipolar interaction among XIs, thus regulating the energy states of XIs. In addition to the rich potential in optoelectronics, the hybrid strategies are advantageous to improve electrochemical energy storage. We constructed hybrid composites from core carbon nanotubes, intermediate metal-organic frameworks (MOFs), and outer TMD layers for supercapacitor electrodes. The 3D hierarchical composites aim to achieve synergetic effects from the components and offer high energy density while maintaining excellent power density and durability. Percolated nanotube networks are highly conductive, MOFs ensure a fast ion diffusivity, and TMD offers a large ion capacity. We engineered the TMD morphologies via topochemical synthesis and determined the optimal structure maximizing faradaic-reactive surface areas for improved ion accumulation and redox energy storage. We found that the hybrid composite of a flower-like TMD structure interwoven with carbon networks exhibits an unprecedentedly high energy density of over 80 Wh/kg, superior to conventional supercapacitors.In summary, this thesis presents powerful strategies for engineering atomically thin TMDs and critical insights on relevant physics which may not be accessible otherwise. Given the extensive library of organic molecules, the hybrid approach may provide a versatile platform to study 2D materials and open new opportunities. The findings could serve as the foundation for the development of novel optoelectronic and energy storage applications
Writing Tutor Alumni Takeaways: Pros and Cons of Contingency
Get PDFThis essay aims to build upon the Peer Writing Tutor Alumni Research Project (PWTARP), designed by Bradley Hughes, Paula Gillespie, and Harvey Kail (2010), which focuses on what tutors learn about themselves as writers and students. However, the PWTARP survey, like much of writing center scholarship, focuses on student workers attending PWIs (Predominately White Institutions). To help fill the diversity gap in the existing literature, the current study uses the PWTARP survey as a frame of reference to investigate what tutors learned about themselves as writers and students at a Hispanic-Serving Institution (HSI). Based on feedback from a team of current and former tutors, we added questions that addressed demographics, multilingualism, and worker conditions. We conducted a mixed methods case study and collected data via surveys and focus group interviews with tutor alumni before and during the COVID-19 pandemic (2019–2022). Our findings connect with many results of the original PWTARP and other responses about economic vulnerability and the emotional labor of tutoring. Also, our survey produced many useful findings about issues related to being a contingent worker, including economic pressures, emotional labor, and professional development
“I’m Not a Writer!” Graduate Writers’ Self-Assessment of Writing Ability and Confidence
Get PDFThis presentation shares survey data on graduate writers\u27 self-assessment of their writing ability and confidence. Typical survey respondents had varied confidence in their writing abilities, were concerned about being respected and understood by scholars in their fields, wrote alone and only when they had to, and were anxious and possibly worried about writing
Describing socially sustainable tourist behaviour -- development and validation of a measurement scale
No full textThis research considers the concept of socially sustainable tourist behaviour. It explains the critical role of this behaviour in two parts (themes and behaviours), highlighting the lack of adequate research to date on this concept and the role of the social dimensions in achieving the UN’s Sustainable Development Goals (SDGs). The dimensions from previous works (academic and non-academic) attempting to articulate sustainable tourist behaviour from a social perspective are summarised and classified. A measurement framework of 19 dimensions of socially sustainable tourist behaviour is proposed. It is argued that the extant literature fails to comprehensively conceptualise and measure socially sustainable tourist behaviour, and that a new framework with an associated measurement scale is needed
GRASP metaheuristic for the design of Tourist Trips
No full textPersonalised electronic tourist guides play a central role the tourist satisfaction at destinations. They include points of interest recommendation and the planning of trips to visit them. The design of tourist trips at destination are based on efficient optimization procedures that require low computational effort. GRASP is a metaheuristic that provide efficient solution procedures suited for solving tourist trip design problems. We show the main features of GRASP algorithms in the application of the main versions of these problems
A systematic literature review on the responsible travel behavior of tourists in destination tourism
Get PDFResponsible travel can be referenced in a variety of ways in academic literature due to similar terms and synonyms. Responsible travel does not have a formal definition although, it is being researched in a wide range of contexts. This study is a systematic literature review on the responsible travel behavior of tourisms in destination tourism. This study aims to identify what literature exists currently that investigates or aims to promote responsible travel behavior of tourists at a destination. This study is also interested in identifying if there is any literature that measures the responsible travel behavior of tourists. This will specifically address whether scales, measurements, tests, assessments, or instruments have been developed to measure responsible travel behavior. This study will be one of the first studies to consolidate the vast information regarding responsible travel, while recognizing that responsible travel can be referenced in a variety of ways. This study may also identify current gaps in responsible travel research and identify whether there is a lack of instruments to measure responsible travel behavior while traveling to a destination
TECHNOLOGICAL APPLICATIONS IN SMALL ACCOMMODATION BUSINESSES
Get PDFThe technological evolution in economic activities determines rapid changes and the need for permanent adaptation. In tourism and, especially in the field of hospitality, these transformations have essential influences on the organizational aspects, managerial decisions and, finally, on the companies\u27 competitiveness. The study aims to assess the current state of use of technological applications in the small accommodation businesses, as well as the perceived advantages and barriers in managers’ views. This study presents the results of a qualitative research conducted on a sample of 20 managers of small hospitality businesses in Brasov County, Romania. The findings point out that the limited financial possibilities confine the access of small units to the implementation of new technological solutions, and the lack of specialized training and support from authorised institutions postpones the decision to use these applications
Determining Macroscopic Transport Parameters and Microbiota Response Using Machine Learning Techniques
No full textDetermining the macroscopic properties such as diffusivity, concentration, and viscosity is of paramount importance to many engineering applications. The determination of macroscopic properties from experimental or numerical data is a challenging task due to the inverse nature of these problems. Data analytic techniques with recent advances in machine learning as well as optimization techniques have enabled tackling problems that were once considered impossible to solve. In the current proposal, we focus on using Bayesian and the state of the art machine learning techniques to solve three problems that involve calculations of the macroscopic transport properties.i) We developed a Bayesian approach to estimate the diffusion coefficient of rhodamine 6G in breast cancer spheroids. Determination of the diffusivity values of drugs in tumors is crucial to understanding drug resistivity, particularly in breast cancer tumors. To this end, we invoked Bayesian inference to solve the problem of determining the light attenuation coefficient and diffusion coefficient in breast cancer spheroids for Rhodamine 6G (R6G) as a mock drug for the tyrosine kinase inhibitor, Neratinib. We noticed that the diffusion coefficient values do not noticeably vary across a HER2+ breast cancer cell line as a function of transglutaminase 2 levels, even in the presence of fibroblast cells.ii) We developed a multi-fidelity model to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes. Determining the rheological properties of fiber suspensions is of indispensable to many industrial applications. To this end, multi-fidelity Gaussian processes and neural networks were utilized to predict the apparent viscosity. Results indicated that with tuned hyperparameters, both the multi-fidelity Gaussian processes and neural networks lead to predictions with a high level of accuracy, where neural networks demonstrate marginally better performance.iii) We developed machine learning models to analyze measles, mumps, rubella, and varicella (MMRV) vaccines using Raman and absorption spectra. Monitoring the concentration of viral particles is indispensable to producing vaccines or anti-viral medications. To this end, we designed and optimized a convolutional neural network and random forest models to map spectroscopic signals to concentration values. Results indicated that when the joint Raman-absorption signals are used for training, prediction accuracies are higher, with the random forest model demonstrating marginally better performance.iv) We developed four machine learning models, including random forest, support vector machine, artificial neural networks, and convolutional neural networks to classify diseases using gut microbiota data. We distinguished between Parkinson’s disease, Crohn’s disease (CD), ulcerative colitis (UC), human immune deficiency virus (HIV), and healthy control (HC) subjects in the presence and absence of fiber treatments. Our analysis demonstrated that it would be possible to use machine learning to distinguish between healthy and non-healthy cases in addition to predicting four different types of diseases with very high accuracy
Convergence Basin Analysis in Perturbed Trajectory Targeting Problems
No full textIncreasingly, space flight missions are planned to traverse regions of space with complex dynamical environments influenced by multiple gravitational bodies. The nature of these systems produces motion and regions of sensitivity that are, at times, unintuitive, and the accumulation of trajectory dispersions from a variety of sources guarantees that spacecraft will deviate from their pre-planned trajectories in this complex environment, necessitating the use of a targeting process to generate a new feasible reference path. To ensure mission success and a robust path planning process, trajectory designers require insight into the interaction between the targeting process, the baseline trajectory, and the dynamical environment. In this investigation, the convergence behavior of these targeting processes is examined. This work summarizes a framework for characterizing and predicting the convergence behavior of perturbed targeting problems, consisting of a set of constraints, design variables, perturbation variables, and a reference solution within a dynamical system. First, this work identifies the typical features of a convergence basin and identifies a measure of worst-case performance. In the absence of an analytical method, efficient numerical discretization procedures are proposed based on the evaluation of partial derivatives at the reference solution to the perturbed targeting problem. A method is also proposed for approximating the tradespace of position and velocity perturbations that achieve reliable convergence toward the baseline solution. Additionally, evaluated scalar quantities are introduced to serve as predictors of the simulation-measured worst-case convergence behavior based on the local rate of growth in the constraints as well as the local relative change in the targeting-employed partial derivatives with respect to perturbations.A variety of applications in different dynamical regions and force models are introduced to evaluate the improved discretization techniques and their correlation to the predictive metrics of convergence behavior. Segments of periodic orbits and transfer trajectories from past and planned missions are employed to evaluate the relative convergence performance across sets of candidate solutions. In the circular restricted three-body problem (CRTBP), perturbed targeting problems are formulated along a distant retrograde orbit and a nearrectilinear halo orbit (NRHO) in the Earth-Moon system. To investigate the persistence of results from the CRTBP in an ephemeris force model, a targeting problem applied to an NRHO is analyzed in both force models. Next, an L1-to-L2 transit trajectory in the Sun-Earth system is studied to explore the effect of moving a maneuver downstream along a trajectory and altering the orientations of the gravitational bodies. Finally, a trans-lunar return trajectory is explored, and the convergence behavior is analyzed as the final maneuver time is varied