Development of Graphene and Graphene-Nanoparticle Composites for Sensor Applications

The goal of this research was the synthesis of graphene and graphene nanocomposite for use as sensor materials. This dissertation describes the optimization of a novel approach to the synthesis of few layer graphene films on SiC, the modification of the graphene surface by wet chemical methods, the nucleation of nanoparticles to form graphene-nanoparticle composites, the fabrication of chemoresistive sensor structures from these materials, and the characterization of these surfaces and films.;In this work, the basic graphene synthesis method which uses halogen based plasma etching and ultra-high vacuum annealing (UHVA), has been optimized to reliably produce one, two, and three layer graphene on SiC films. The process has also been extended by replacing the UHVA step with rapid thermal annealing (RTA) in atmospheric pressure argon. Graphene films produced by both methods have been characterized using x-ray photoelectron spectroscopy (XPS), Raman microscopy, and atomic force microscopy (AFM). The UHVA process produces films with halogen-based and possibly some oxygen-based defects, whereas the RTA processes produces exclusively oxygen-based defects which include epoxide, hydroxyl, and carbonyl groups similar to, but at much lower levels, than that observed for graphene oxide (GO). As in the case for GO, the defect density was further reduced by wet chemical surface modification.;Nanoparticles (Ag, Au, Pt, Ir) were attached to these surfaces using solution based methods. The particle diameter and height distributions along with surface coverage were characterized using AFM methods. Key parameters in these studies included solution composition and incubation time. For electrical characterization and sensor testing, two structures were then fabricated using lithography free methods and electron beam evaporation. The first of these structures, referred to as the transmission line method (TLM) structure, was used in the present work for electrical characterization. Using the TLM structure, the electrical properties were characterized using two and four point probe methods. The films exhibited semiconducting behavior which is believed to be due to the opening of a band gap by the halogen- and oxygen-based defects. Using the two and four pint methods, the Schottky barrier height, the carrier density, electrical resistivity, and the carrier mobility were determined. The electrical resistivity was found to have an inverse relationship with number of graphene layers for one, two, and three layer films. The second device structure was a simple interdigitated sensor structure which was passed on to other researchers for sensor studies. Overall, reliable and reproducible synthesis and fabrication methods for graphene and graphene-nanoparticle composites have been developed for the next stage of testing and sensor development

Predicting the Type and Target of Offensive Social Media Posts in Marathi

The presence of offensive language on social media is very common motivating platforms to invest in strategies to make communities safer. This includes developing robust machine learning systems capable of recognizing offensive content online. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English and a few other high resource languages such as French, German, and Spanish. In this paper we address this gap by tackling offensive language identification in Marathi, a low-resource Indo-Aryan language spoken in India. We introduce the Marathi Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded annotation to the levels B (type) and C (target) of the popular OLID taxonomy. MOLD 2.0 is the first hierarchical offensive language dataset compiled for Marathi, thus opening new avenues for research in low-resource Indo-Aryan languages. Finally, we also introduce SeMOLD, a larger dataset annotated following the semi-supervised methods presented in SOLID.Comment: This is a preprint of an article published in the Journal of Intelligent Information Systems, Springer. The final authenticated version is available online at https://link.springer.com/article/10.1007/s13278-022-00906-

An Insight on Analytical Profile on Bisoprolol Fumarate – A Selective Beta-1 Adrenoreceptor Blocker

BF is Beta-adreno receptor antagonist and used as an AntiHypertensive Drug. BF gives the blocking action on β1-adrenergic receptors in the heart and vascular smooth muscle. The present review compiles the various approaches implemented for quantification of BF in bulk drug, pharmaceutical matrix and biological fluid. This review represents more than 50 analytical methods which include capillary electrophoresis, HPLC, HPTLC, UV-Spectroscopy, UPLC, impurity profiling and electrochemical methods implemented for estimation of BF as a single component as well as in multicomponent

A Comparative Analysis on Factors Affecting Functioning of Airports in India

Airport is one of the most complex facilities which link various business units to achieve their roles within the air transport industry. Individuals travel frequently as they get wealthier. That means economic fortune causes more demand for travel. In the case of aviation industry, this pattern is enhanced by propel innovation, cost efficiencies and solid rivalry inside the business, which have conveyed air travel inside the compass of numerous individuals. The chief objective of this study is to examine the factors affecting functioning of Mumbai, Pune, Lucknow, Kanpur, Ranchi and Rourkela airports in India and recognize opportunities for improving airport performance. The multiple regression model is applied to find the relationship between demands i.e. number of passengers and its determinants. As the coefficients of factors such as Economic condition, occupation and Income of passenger, Population of the place, Connectivity to other cities, Accessibility to the airport and Safety and security at the airport gets higher values, they affects more in functioning of the airport. In similar way the coefficients of factors such as Place of Residency, Air fares, No. of Airlines in function, Frequency of flights, Airport Infrastructure, psychological parameters (fear of flying, willingness to pay, attitude, etc) gets low values, they affects less in functioning of the airport. The findings of this study suggest that air transport demand can be enhanced significantly when the price of competing service, population and income increases and airfare decreases. Thus the administrative associations (Airport Authority of India) ought to have a consolidated arrangement to upgrade the benefit acquired through the advancement of the national economy and the population development

Temporal Personalization of a Digital Intervention for Physical Activity

Personalized interventions using wearables and mobile apps to improve users’ physical activity (PA) show promising results. The appropriate timing of such digital interventions is vital to achieve the target behaviors. Yet, there is a lack of understanding and theory-driven research on how to personalize the timing of such PA interventions. Motivated thus, we draw on circadian rhythm theory for our intervention design, which proposes that individuals prefer activities during specific time periods of the day. We built an app that personalizes PA intervention timing according to this preference (called chronotype). To examine its effect, we designed a 3-arm randomized control trial (RCT) with matched timing, unmatched timing, and fixed timing intervention groups. The results of the pilot RCT show significant improvement in app usage and walking time for treatment groups as compared to the fixed timing control group. We outline the contributions and future research plans for our study

A Composite Indicator of Realty Sector Activity in India

The realty sector plays a crucial role in India in terms of employment generation, access to housing and as a major source of saving in physical form, besides direct contribution to the country’s Gross Value Added (GVA). In this study, we estimate a dynamic factor for housing (DFH) from a range of available high-frequency indicators of real estate activity. Our empirical findings indicate unidirectional causality from DFH to GVA with evidence of the first and second-round impact of revival in DFH driving recovery in GVA. Our empirical results also indicate the critical role of counter-cyclical fiscal and monetary policy measures, housing sector reforms and other policy incentives for the housing sector in reviving construction sector activity and GVA growth in the post- COVID period

Links demystified: Periodontitis and cancer

Cancer is marked by the uncontrolled growth of cells, tissue invasion and metastasis to various organs via the circulatory and lymphatic systems. Recent data have expanded the concept that inflammation is a critical component of tumor progression. Many cancers arise from sites of infection, chronic irritation, and inflammation. The tumor microenvironment, which is largely orchestrated by inflammatory cells, is an indispensable participant in the neoplastic process, fostering proliferation, survival, and migration. Periodontal disease, a chronic inflammatory condition is characterized by an oral bacterial infection leading to inflammation within the supporting tissues of the teeth, which often leads to the destruction of the periodontal tissues and alveolar bone that support the teeth. This oral inflammation often has systemic effects leading to an increased concentration of circulating inflammatory markers with the severity of disease being correlated directly with levels of serum inflammatory markers. Periodontal infection has been linked to organ and systemic diseases. There is documented evidence of significant associations between cancer of the lung, kidney, pancreas, hematological and oral cancers, and periodontal disease. This articles reviews and summarizes the possible biological mechanisms involved between periodontal infection and cancer

Predicting the type and target of offensive social media posts in Marathi

The presence of offensive language on social media is very common motivating platforms to invest in strategies to make communities safer. This includes developing robust machine learning systems capable of recognizing offensive content online. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English and a few other high-resource languages such as French, German, and Spanish. In this paper, we address this gap by tackling offensive language identification in Marathi, a low-resource Indo-Aryan language spoken in India. We introduce the Marathi Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded annotation to the levels B (type) and C (target) of the popular OLID taxonomy. MOLD 2.0 is the first hierarchical offensive language dataset compiled for Marathi, thus opening new avenues for research in low-resource Indo-Aryan languages. Finally, we also introduce SeMOLD, a larger dataset annotated following the semi-supervised methods presented in SOLID (Rosenthal et al. in SOLID: a large-scale semi-supervised dataset for offensive language identification. In: Findings of ACL, 2021)