Characterization of physical properties of polymers using AFM force-distance curves

A novel analysis method based on Hertz theory was used to determine the mechanical properties from force-distance curves obtained over a wide range of temperatures and frequencies on poly(n-butyl methacrylate) (PnBMA) and two polystyrene (PS) samples, having different molecular weight and hence different glass transition temperature Tg. The analysis technique extends the elastic continuum contact theories to the plastic deformations and permitted to calculate the stiffness in the plastic regime of deformation, the yielding force, the parameters of the WLF and Arrhenius equations, and the Young\u27s modulus. The Young\u27s modulus and the shift coefficients of the polymers determined through AFM measurements were in excellent agreement with the values from DMA measurements and/or the literature values. Force-distance curves were also acquired on a model polymer blend of PS/PnBMA at different temperatures. The analysis method was used to determine the Young\u27s modulus of PS and PnBMA away from the interface and close to the interface with a resolution of 800 nm. The differences in Tg of the two polymers resulted in different viscoelastic behavior. The modulus of PnBMA and PS was in excellent agreement with the DMA and AFM data from the measurements on individual films. The morphology of the PS/PnBMA blend was characterized using the Young\u27s modulus of the constituting polymers. A several µm long transition region was observed in the vicinity of the interface, where the modulus of PnBMA decreased from the value on PS to the value on PnBMA away from the interface. This experiment shows the capability of AFM of surveying local mechanical properties and studying heterogeneous samples. Such spatially resolved measurements cannot be achieved with any other technique.Eine neuartige, auf der Hertz Theorie basierende Analysemethode wurde benutzt um mechanische Eigenschaften anhand Kraft-Abstands Kurven zu bestimmen. Kraft-Abstands Kurven wurden auf Poly(n-butyl Methacrylat) (PnBMA) und auf zwei Sorten Polystyrol (PS) mit unterschiedlichem Molekulargewicht und unterschiedlicher Glasübergangstemperatur Tg in einem großen Temperatur- und Frequenzbereich aufgenommen. Diese Analysetechnik erweitert die elastischen Kontinuumstheorien um plastische Deformationen und erlaubt die Steifigkeit bei plastischen Deformationen, die Fließgrenze, die Parameter der WLF und Arrhenius Gleichungen, sowie den Elastizitätsmodul zu bestimmen. Der Elastizitätsmodul und die Verschiebungskoeffizienten der Polymere, bestimmt durch die AFM Messungen, stimmen mit den Ergebnissen der DMA Messungen und Literaturwerten überein. Kraft-Abstands Kurven wurden auch bei verschiedenen Temperaturen auf einem modellhaften PS/PnBMA-Polymerblend aufgenommen. Die Analysemethode wurde benutzt, um den Elastizitätsmodul von PS und PnBMA mit einer Auflösung von 800 nm nah und fern der Grenzfläche zu bestimmen. Die unterschiedlichen Tg der zwei Polymere zeigen sich im unterschiedlichen viskoelastischen Verhalten. Die Module von PnBMA und PS stimmen mit den Ergebnissen der DMA und AFM Messungen auf einzelnen Filmen überein. Die Morphologie des Blend wurde durch den Elastizitätsmodul der einzelnen Polymere charakterisiert. In der Nähe der Grenzfläche wurde eine mehrere µm lange Übergangsregion beobachtet, in der der Modul von PnBMA vom PS-Wert zum PnBMA-Wert bei zunehmendem Abstand von der Grenzfläche abfällt. Dieses Experiment zeigt die Möglichkeit des AFM, die lokalen mechanischen Eigenschaften von heterogenen Proben zu untersuchen. Solche ortsaufgelösten Messungen können mit anderen Techniken nicht durchgeführt werden

Policy-based power consumption management in smart energy community using single agent and multi agent Q learning algorithms

Power consumption in residential sector has increased due to growing population, economic growth, invention of many electrical appliances and therefore is becoming a growing concern in the power industry. Managing power consumption in residential sector without sacrificing user comfort has become one of the main research areas recently. The complexity of the power system keeps growing due to the penetration of alternative sources of electric energy such as solar plant, Hydro, Biomass, Geothermal and wind farm to meet the growing demand for electricity. To overcome the challenges due to complexity, the power grid needs to be intelligent in all aspects. As the grid gets smarter and smarter, considerable efforts are being undertaken to make the houses and businesses smarter in consuming the electrical energy to minimize and level the electricity demand which is also known as Demand Side Management (DSM). It also necessitates that the conventional way of modelling, control and energy management in all sectors needs to be enhanced or replaced by intelligent information processing techniques. In our research work, it has been done in several stages. (Purpose of Study and Results) We proposed a policy-based framework which allows intelligent and flexible energy management of home appliances in a smart home which is complex and dynamic in ways that saves energy automatically. We considered the challenges in formalizing the behaviour of the appliances using their states and managing the energy consumption using policies. Policies are rules which are created and edited by a house agent to deal with situations or power problems that are likely to occur. Each time the power problem arises the house agent will refer to policy and one or a set of rules will be executed to overcome that situation. Our policy-based smart home can manage energy efficiently and can significantly participate in reducing peak energy demand (thereby may reduce carbon emission). Our proposed policy-based framework achieves peak shaving so that power consumption adapts to available power, while ensuring the comfort level of the inhabitants and taking device characteristics in to account. Our simulation results on MATLAB indicate that the proposed Policy driven homes can effectively contribute to Demand side power management by decreasing the peak hour usage of the appliances and can efficiently manage energy in a smart home in a user-friendly way. We propounded and developed peak demand management algorithms for a Smart Energy Community using different types of coordination mechanisms for coordination of multiple house agents working in the same environment. These algorithms use centralized model, decentralized model, hybrid model and Pareto resource allocation model for resource allocation. We modelled user comfort for the appliance based on user preference, the power reduction capability and the important activities that run around the house associated with that appliance. Moreover, we compared these algorithms with respect to their peak reduction capability, overall comfort of the community, simplicity of the algorithm and community involvement and finally able to find the best performing algorithm among them. Our simulation results show that the proposed coordination algorithms can effectively reduce peak demand while maintaining user comfort. With the help of our proposed algorithms, the demand for electricity of a smart community can be managed intelligently and sustainably. This work is not only aiming for peak reduction management it aims for achieving it while keeping the comfort level of the inhabitants is minimum. It can learn user’s behaviour and establish the set of optimal rules dynamically. If the available power to a house is kept at a certain level the house agent will learn to use this notional power to operate all the appliances according to the requirements and comfort level of the household. This way the consumers are forced to use the power below the set level which can result in the over-all power consumption be maintained at a certain rate or level which means sustainability is possible or depletion of natural resources for electricity can be reduced. Temporal interactions of Energy Demand by local users and renewable energy sources can also be done more efficiently by having a set of new policy rules to switch between the utility and the renewable source of energy but it is beyond the scope of this thesis. We applied Q learning techniques to a home energy management agent where the agent learns to find the optimal sequence of turning off appliances so that the appliances with higher priority will not be switched off during peak demand period or power consumption management. The policy-based home energy management determines the optimal policy at every instant dynamically by learning through the interaction with the environment using one of the reinforcement learning approaches called Q-learning. The Q-learning home power consumption problem formulation consisting of state space, actions and reward function is presented. The implications of these simulation results are that the proposed Q- learning based power consumption management is very effective and enables the users to have minimum discomfort during participation in peak demand management or at the time when power consumption management is essential when the available power is rationale. This work is extended to a group of 10 houses and three multi agent Q- learning algorithms are proposed and developed for improving the individual and community comfort while at the same time keeping the power consumption below the available power level or electricity price below the set price. The proposed algorithms are weighted strategy sharing algorithm, concurrent Q learning algorithm and cooperative distributive learning algorithm. These proposed algorithms are coded and tested for managing power consumption of a group of 10 houses and the performance of all three algorithms with respect to power management and community comfort is studied and compared. Actual power consumption of a community and modified power consumption curves using Weighted Strategy Sharing algorithm, Concurrent learning and Distributive Q Learning and user comfort results are presented, and the results are analysed in this thesis

Facile synthesis and cytotoxic activity of 3,6-disubstituted 1,2,4-triazolo-[3,4-b]-1,3,4-thiadiazoles

Afacile synthesis of 3,6-disubstituted-1,2,4-triazolo-[3,4-b]-1,3,4-thiadiazoles (5a-j) has been achieved by condensing 3-aryl substituted 4-amino-5-mercapto (4H)-1,2,4-triazole (4a-b) with various aromatic acids. The structures of the synthesized compounds were supported by IR, 1H NMR, mass spectral data and elemental analysis The compounds were evaluated for in vitro cytotoxic activity against four human cancer cell lines: two ovarian (PA-1and OAW-42) and two breast (T47D and MCF-7) cell lines using the methyl thiazol tetrazolium assay method. It showed that some of the tested compounds 5a, 5b, 5g and 5f exhibited significant activity against PA-1 cell lines and the compounds 5a and 5e exhibited IC50 of 0.72 µM and 0.65 µM, respectively, against the cell lines of MCF-7, which are close to Doxorubin. After comparing the cytotoxic activity results of compounds 5a-j, it was concluded that the incorporation of triazolo-thiadiazole moiety in aryl propionic acid group gives rise to enhanced anticancer activity. Also, the substitution of chloro group in the aryl ring at the 3rd position was found to enhance their potency

Green Technology: A Must or a Need in TVET Education in Malaysia?

Green Technology is seen as the optimal solution in addressing most of the environmental issues affecting our society today. The movement towards green technology will undoubtedly change the labour market. Although, there were numerous studies been done in regards to green technology but the question remains unanswered when it comes to Vocational Colleges in Malaysia. Therefore, the main goal of this current study is to investigate and develop an in-depth exploration of the need of incorporating Green Technology in one of the most prominent TVET institutions in Malaysia which is the Vocational Colleges. The study uses exploratory sequential mixed method as the research design but only the qualitative approach is being selected to be discussed in this paper. Hence, we report the findings conducted through semi-structured interview by interviewing 7 industrial players in TVET, associated with Vocational Colleges in Malaysia. In relation to the main findings from this study, we found that all 7 participants believe that there is a need to incorporate green technology in the curriculum of Vocational Colleges as to upgrade and fulfill the industry’s needs. Taken together, this finding suggests that educational reform may be the most important tool in the future of green technology in Vocational Colleges and by all means it is defined as a need rather than a must

Antiproliferative effects of Vanilla planifolia leaf extract against breast cancer MCF-7 cells

Background: Breast cancer is the most common cancer disease among females in India and worldwide. This needs a critical research for finding the drugs to treat breast cancer with less side effects. The aim of the present study is to reveal the anti-proliferative effects of vanilla extract against MCF-7 cells.Methods: To reveal anti proliferative effects of vanilla leaf extract, MTT assay, cell cycle analysis and DNA fragmentation assay was performed as per standard protocols.Results: MTT assay showed decrease in cell viability with increase of dose of extract and revealed IC50 value at 31.2µg/ml. DNA fragmentation was seen in extract treated cells.Conclusions: The results of the present study confirm the antiproliferative property of vanilla leaf extract in MCF-7 cells. This study results conclude vanilla leaf extract as an effective plant source medicament for treating breast cancer

Ethanobotanical studies on Achyranthes aspera Linn. among the folk peoples of Tamilnadu, South India

The study documents indigenous Achyranthes aspera used for folk and tribal medicine in South Indian medicinal system.We have to take the survey among the village peoples and tribal peoples in concern district of Tamilnadu .The plant are commonly used for certain diseases and this is discussed with the literature

Decomposition of product graphs into sunlet graphs of order eight

For any integer $k\geq 3$ , we define sunlet graph of order $2k$, denoted by $L_{2k}$, as the graph consisting of a cycle of length $k$ together with $k$ pendant vertices, each adjacent to exactly one vertex of the cycle. In this paper, we give necessary and sufficient conditions for the existence of $L_{8}$-decomposition of tensor product and wreath product of complete graphs

Simulation of structural and electronic properties of amorphous tungsten oxycarbides

Electron beam induced deposition with tungsten hexacarbonyl W(CO)6 as precursors leads to granular deposits with varying compositions of tungsten, carbon and oxygen. Depending on the deposition conditions, the deposits are insulating or metallic. We employ an evolutionary algorithm to predict the crystal structures starting from a series of chemical compositions that were determined experimentally. We show that this method leads to better structures than structural relaxation based on guessed initial structures. We approximate the expected amorphous structures by reasonably large unit cells that can accommodate local structural environments that resemble the true amorphous structure. Our predicted structures show an insulator to metal transition close to the experimental composition at which this transition is actually observed. Our predicted structures also allow comparison to experimental electron diffraction patterns.Comment: 17 Pages, 11 figure