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Iron Based Metal-Organic Frameworks (Fe-MOFs) for Photocatalytic Energy and Environmental Applications
No full textThis thesis presents the synthesis and photocatalytic application of iron based MOF (MIL-53) and its heterostructure materials for the photocatalytic decontamination of environmental contaminants under visible light and the production of green and sustainable renewable energy by generating hydrogen from water splitting, thereby addressing the global environmental and energy crisis. Initially, a simple solvothermal technique was used to create MIL-53 (Fe-MOF) and MIL-53 based heterojunctions. The MIL-53 were then combined with metal carbonate, metal nanoparticles, graphitic carbon nitride (g-C3N4) and metal oxide to create novel heterostructure materials that have better optical absorbance and photoelectrochemical properties. The photocatalytic use of the synthesized heterostructure materials was tested for environmental purification process and hydrogen evolution reaction (HER) reactions. To comprehend the mechanism behind photocatalytic activity, a thorough mechanistic research and band position analysis of the composites were carried out. The photocatalytic activity of MIL-53 (Fe-MOF) was primarily checked by making a heterojunction with Ag2CO3 using a simple solvothermal method in which the Ag+ ion of Ag2CO3 self-reduces to Ag NPs, which function as a bridge in the heterojunction system. The Ag2CO3 was incorporated within an MIL-53 matrix to synthesize MAC-X hybrid p-n heterostructure that had remarkable photocatalytic activity towards RhB degradation and hydrogen evolution reaction. The optimal photocatalyst MAC-30 showed the highest photocatalytic efficiency towards RhB degradation with rate constant (0.025 min−1) which was 3.57 and 4.16 times more than pristine Ag2CO3 and MIL-53 respectively. The MAC- 30 photocatalyst also exhibited superior photocatalytic H2 evolution (1346 μmol.h−1.g−1) with apparent conversion efficiency of 8.59%. Self-reduced Ag nanoparticle in MAC-X composites acts as an electron mediator in the Z-scheme mechanism path to improve the photocatalytic performances. Ag nanoparticles adorned g-C3N4 modified with MIL-53 was synthesized using a simple solvothermal method. The finely distributed silver nanoparticles (Ag NPs) serve as an electron channelizing bridge for the photo-generated electrons in the composite systems. The hierarchical materials exhibited unique structural, compositional as well as opto-electrical properties, which include high crystallinity, surface exposed reactive site, nanosized interfacial contact, strong absorption in visible region, rapid migration of charge carriers and high resistance to recombination. The optimal 15% of ACN-20 modified wit MIL-53 (MACN-15) photocatalyst demonstrated outstanding photocatalytic activity for the degradation of rhodamine B (RhB) (98%), and generation of H2 (2.891 mmolg−1 h−1) from the water splitting with an apparent conversion efficiency of (14.8%). Further, to increase the ability to harness visible light and photoactivity, MIL-53 was constructed heterojunction with boron and sulfur co-doped hollow g-C3N4 which was implemented in photodegradation of Bisphenol A (BPA) and photocatalytic hydrogen production. The electrical and molecular structure of g-C3N4 can be altered by boron and sulfur elemental doping to enhance its photocatalytic capabilities. Large particular surface area and low interfacial resistance are provided by the boron sulfur co-doped g-C3N4 (CNBS)@MIL-53 (M-CNBS heterojunction) for quick electron transport. The M-CNBS heterostructure shows excellent photocatalytic activity towards BPA degradation with rate constant of 0.03873 min-1 and H2 evolution (2.80 mmol. g-1). The significant photoactivity of M-CNBS heterojunction attributes to the Z-scheme charge transfer dynamics. The degradation pathway and the molecular structures of the intermediates of BPA were analyzed by LCMS technique and a possible degradation pathway has also been predicted. Finally, the heterojunction between MIL-53 (Fe-MOF) and defect induced CeO2 was fabricated for the photodegradation of Bisphenol A (BPA) degradation and H2 evolution. A simple chemical redox etching methodology was adopted to narrow the band gap of pristine CeO2 through oxygen vacancy engineering. The optimal photocatalyst 30% of defect induced CeO2/ MIL-53(MCO-30) displayed the highest photocatalytic BPA degradation with rate constant (0.045 min−1) and H2 evolution (3286.2 μmol.h−1.g−1) respectively. The significantly improved photocatalytic application of MCO-X heterojunction could be attributed to the switching of charge dynamics mechanism from Type-1 to Type-II due to defect formation in the pristine CeO2. This study provides a comprehensive analysis on how defect in pristine CeO2 in MCO-X heterojunction can switch the charge transfer mechanism from Type-1 to Type-II to achieve remarkable visible light harnessing capacity and photocatalytic activity
Development of Different Radio Tomographic Imaging Techniques by Exploiting Sparsity
No full textIn contemporary research, target localization has become a prevalent challenge in wireless sensor networks (WSN). The localization of targets in WSN is facilitated by attaching sensors to the target. Such a localization technique is referred to as device-based target localization. The device-based localization encounters issues such as dif-ficulty with elderly monitoring, security, and inaccessibility by non-intended targets. This opens up the scope for device-free target localization. In device-free localization (DFL) techniques, no sensor is attached to the target that needs to be localized. Most importantly, the DFL system can localize targets in WSN without knowledge of the localization system, which thereby helps with privacy during the localization process. Therefore, a DFL system can be applied for rescue operations, intrusion detection, roadside surveillance, and health care systems. Radio tomographic imaging (RTI) is one such DFL technique that enables the localization of single or multiple targets by finding the attenuation information of radio waves caused by the targets. The radio wave attenuation information is provided through a radio map and is known as spatial loss fields (SLF). Hence, SLF provide the location and shape of the targets in the RTI system. The estimation of SLF can be done by acquiring the received signal strength (RSS) information of different sensor nodes in the network. A large change in RSS is observed when the targets lie in the line-of-sight (LOS) of the link. The change in RSS information, along with the weight of every pixel in the network, is used for the estimation of the SLF. Using the RSS value of every sensor node and the weight of each pixel, the underlying SLF can be found through a linear regression model. Generally, the number of observed data (RSS) is quite less than the number of predictors (pix-els) of the network, which indicates RTI is an ill-posed problem. Hence, the estimated SLF obtained from the linear least square algorithm suffers from poor localization and shape of the targets. Therefore, in the RTI system, appropriate prior information about the underlying SLF is incorporated through a regularized cost function that enables an accurate SLF estimation. The existing RTI models try to improve target localization by ensuring a low localization error. However, some major real-world RTI issues, such as estimation of SLF by removing surrounding noisy pixels (improving sparsity), esti-mation of SLF in the presence of data uncertainty, improving the convergence speed of RTI algorithms that enable fast and online estimation of SLF, and most importantly, an accurate estimation of SLF in the presence of node or link failure, need to be given insight and solved efficiently with a lower computational burden. This dissertation primarily seeks to give some robust estimators that can estimate the SLF by incorporating real-world RTI problems such as data uncertainty, slower convergence owing to batch processing of the data, and link or node failure issues in a centralized RTI framework. Measurement errors and external factors like wind and soil movement, which cause movement of the sensor nodes, are accountable for the data uncertainty. The stochastic robust approximation (SRA) and worst-case robust approx-imation (WCRA) algorithms offer exceptionally accurate vector estimates when there is uncertainty in the data. Therefore, the regularized SRA-based estimators can be used to handle the sensor location uncertainty that results in an uncertain weight matrix for the RTI system. Our main contribution is to develop regularized SRA-based esti-mators by taking into account the uncertain region surrounding the nominal position of the sensor nodes in both sparse and non-sparse circumstances. It is observed that the l2- norm-based SRA does not improve the sparsity in the estimated SLF and leads to a higher computational cost. A sparsity-promoting l1-norm based SRA (l1-SRA) that can localize the targets with sensor location uncertainty has been developed to re-duce the computational load. However, it is observed that l1-SRA does not promote the smoothness attribute of the SLF. In order to assure reliable SLF estimation, a structured sparsity-based least absolute shrinkage selection operator (lasso) is used. This estimator is otherwise termed a fused lasso (FL)-based estimator. Hence, the FL-SRA estimator is suggested. It concurrently enhances the sparsity and structural features of the under-lying SLF. Certain types of uncertainty in observed data occur due to the digitization of data, where the main source of uncertainty is the quantization of RSS data in the RTI system. Due to this quantized RSS (q-RSS), the SLF estimation becomes inaccurate. Consequently, support vector regression (SVR)-based estimators are proposed for the RTI system to handle the uncertainty caused by the quantization error. The epsilon SVR (ϵ-SVR) model is used, which eliminates the error inside the ϵ-band. The ν-SVR is used to deal with quantized data uncertainty by adaptively estimating the epsilon value to make the SLF estimation more robust against quantized data. Furthermore, a fused l2-norm based SVR (F-l2-SVR) approach is proposed that increases the correla-tion between the pixels. The proposed sparsity-improving l1-norm SVR, also referred to as linear programming-SVR (LP-SVR), is capable of eliminating the surrounding small noisy pixels by giving the noisy pixels the lowest power and overcomes the limitations of the F-l2-SVR technique. This sparse-based LP-SVR technique has a significant re-duction in computational expenses for the estimation of SLF due to the use of a lower number of support vectors. Additionally, the FL-based SVR (FL-l1-SVR) approach is used, which has a lower computational cost than F-l2-SVR but a slightly higher com-putational cost than LP-SVR. The main benefit of this sparse-based SLF estimation is that it can simultaneously retain the SLF’s sparsity as well as its structural details. Hence, such estimators can provide structured sparsity for the estimated SLF. It has been noticed that the majority of RTI systems use first-order algorithms. The first-order algorithms have a slower convergence rate as well as a higher mean square error (MSE) compared to the second-order algorithms. Again, the majority of RTI algorithms em-ploy batch processing. On-line data processing is therefore more in demand than batch data processing. Hence, a fast algorithm is proposed that can provide high target lo-calization accuracy as well as accurate estimation of the SLF. In addition to improving the structured sparsity-based SLF estimation, the proposed time-norm-weighted-fused least absolute shrinkage selection operator (lasso), i.e., the TNWFL technique in RTI, also offers a faster operation when compared to first-order FL algorithms. Although the suggested RTI algorithms offer reliable SLF estimation for various real-world RTI issues, their effectiveness is only compatible with fusion centre (FC)-based SLF estima-tion. The FC may get overburdened under a centralized RTI architecture. In centralized RTI, the SLF estimation accuracy is also affected due to node or link failure. A dis-tributed RTI system that is resilient to node or link failure is proposed to address the shortcomings of a centralized RTI system. The proposed distributed incremental RTI system is capable of sharing the image vector or SLF information with its immediate neighbour. After completing a cyclic path from the initial node to the end node, the global SLF is acquired. Due to the NP-hardness of this distributed incremental RTI, it cannot be regarded as a fully distributed technique. The alternating direction method of multipliers (ADMM)-based consensus technique thus offers a fully distributed frame-work for the RTI system. For distributed estimation of SLF, the proposed distributed consensus ADMM RTI (DCADMM-RTI) is used for non-sparse applications. However, the distributed sparse consensus ADMM RTI (DSCADMM-RTI) allows sparsity-based distributed estimation. The main contribution is to effectively share local SLF among neighbouring nodes in order to establish a global SLF of interest. In this consensus-based approach, all sensor nodes obtain SLF data comparable to the global SLF after a few convergence iterations. In the RTI system, there is a strong possibility that there will be fewer targets in a large monitored region. The SLF of interest is thus believed to be sparse. Sparsity in an estimated SLF means there are more zero pixels than non-zero pixels. The sparsity-based regularized objective function has applications in target lo-calization in RTI, spectrum cartography of radio frequency maps, and improved feature selection in 5G communication. The proposed robust algorithms are therefore simulated with their respective sparsity-based counterparts for the RTI system. The efficiency of these robust estimators for various real-world RTI circumstances is examined for both single and multiple targets in the monitored region. It has been observed that sparsity-based robust estimators perform almost similarly for single- and multi-target scenarios. However, the non-sparse robust estimator shows a little performance degradation for multi-target scenarios compared to single-target localization scenarios. The quantita-tive analysis of estimated SLF is examined through various performance metrics such as root mean square error (RMSE), pixel attenuation ratio (PAR), structural similarity (SSIM), and feature similarity (FSIM). The simulation findings indicate that under var-ious real-world RTI circumstances, the proposed FL-based robust estimators offer the most precise SLF estimation and lowest RMSE among all robust estimators
Diffusion Dynamics in Weak Polyelectrolyte Solutions
No full textDiffusion anomaly is accountable for molecular transport inside the living organism on which the transference of genetic materials, vital nutrients and ions around the cell membrane along with the movement of drug particles are dependent. This simple well-ordered dynamic process in reality is based on a complex mechanism. At a given time, numerous parameters control the diffusion environment of real-life system. The simultaneous presence of these parameters turns the system into a macromolecular crowding where the structural organization and other molecular interactions also affect the dynamical process. Apart from the biological system, the diffusion dynamical process has shown its importance in the field of many industrial applications; such as the pharmaceutical industry, food packaging and processing and water treatment or management processes. Hence, understanding the basis of diffusion behaviour and garnering knowledge on the various controllable parameters, their origin and aftereffects has become an interesting and challenging task among polymer physicists and soft material scientists. Normally, the controllable parameters affecting the diffusion process offer varieties of environmental constraints and when act together, it is difficult to distinguish the exact parameter responsible for the analogous changes in the diffusion dynamics. For this reason, the “model system” approach has been utilized as an alternative adaption to avoid the lack of understanding of the dynamics of the relevant systems. These model systems provide the freedom of choosing or rather selecting the individual parameters and tweaking them appropriately to analyze the consequences of the diffusion process. The amount of crowding, the existence of other molecules, their interactions and the structural organization should be taken care of while designing the model system. Hence the research on these model systems and their behavior will be of immense help in addressing the real-life complex interactions. The model systems comprise various categories and among these, the weak polyelectrolyte solution system is one of a kind where the polymer backbone charge can be regulated under the suitable conditions of pH and the type of the polar solvent. The unique characteristic of seizing a partial degree of dissociation at the intermediate pH range of the solvent has made the weak polyelectrolyte system more interesting and applicable to replicate the complicated real-life system interactions. The genetic materials of DNA, RNA and various proteins and polysaccharides are charged macromolecules, imprisoning vital cellular information and are regarded as natural polyelectrolytes. Synthetically prepared weak polyelectrolytes with advance properties act as an appropriate model for these natural polyelectrolytes and provide us with the advantage of controlling different parameters and allow us to carry out the sophisticated study which is otherwise impossible directly in the case of natural polyelectrolyte solution system. The proper information on the diffusion dynamics of these polyelectrolyte solutions assists the pharmaceutical industry in designing the drug loading capsules and tracing the target site at a controlled speed. The waste water treatment and other biomedical applications including cell encapsulation rely on the dynamical study of weak polyelectrolyte solutions. To achieve our research objectives, two types of weak polyelectrolyte solutions have been considered anionic and cationic weak polyelectrolyte. The two weak polyelectrolytes consist of oppositely charged polymer backbone and make use of three primary parameters as, pH of the solvent, polymer concentration and external electrolyte concentration, the systematic study on the diffusion dynamics has been carried out and presented in the current thesis. The experimental techniques of Dynamic light scattering (DLS) have been used to analyze the diffusion behaviour of polymer chains under set conditions whereas Fluorescence recovery after photobleaching (FRAP) is utilized to study the probe dynamics inside the weak polyelectrolyte solutions. The complementary techniques of rheology and microviscosity have been used in the required sections of the study. The light scattering study on anionic PAA solutions has revealed the simultaneous transition of ergodic to non-ergodicity and triple to bimodal relaxation on variation of pH of the solution and the reverse effect is achieved with the addition of electrolyte concentration to the PAA solutions in the respective pH of the solution. This transition in the dynamics of polyacrylic acid (PAA) solutions is the resultant influence of concentration, crowding and charge effect owing to pH variation. The screening of charge from the salt solutions helps to restore the ergodicity in PAA solutions which is also dependent on particular concentration and pH of the solution. The fast mode relaxation remains diffusive irrespective of pH and salt solutions whereas the non-diffusive slow mode relaxation and intermediate mode change to diffusive with the addition of salt solutions. The newly observed intermediate mode has been coined as non-ergodic mode due to the coincidence of appearance with non-ergodicity in PAA solutions. The rheology study on PAA solutions has confirmed the presence of network structure without the addition of crosslinking agent into PAA solutions and well explains the great storage property at higher pH of the solutions and the loss property dominance in the lower pH and lower concentrated solutions. The storage and loss behaviour of solutions is also dependent on the degree of ionization of the PAA at particular pH of the solvent and salt concentrations. The cationic polyallylamine hydrochloride (PAH) solutions have demonstrated a similar transition from ergodic to non-ergodic behaviour and bimodal relaxation to single relaxation at the lowest polymer concentrations in each pH value. With the salt solutions in PAH, the systematic complete restoration of ergodicity can be achieved only for lower polymer concentrations. The light scattering study on PAH solutions depicts that although the transition in dynamics is due to the combined effect of concentration, charge and crowding but the influence of concentration on crowding is dominant than the charge effect in PAH due to lack of charge variation. The fast and slow modes are diffusive and non-diffusive in nature at each pH value and at optimum salt solutions, both the modes turn out to be non-diffusive and the slow mode shows greater angle dependence with scattering wave vector. The probe dynamics in both the weak polyelectrolyte solutions are analyzed with two different sizes of FITC labeled dextran of 40kDa and 2000kDa molecules. Both the types of probe dynamics in PAA and PAH solutions experience the impact of microviscosity at all studied pH and salt solutions. This effect is more pronounced in PAA solutions than the PAH solutions. Moreover, the probe diffusion inside the weak polyelectrolyte solutions is dependent on various interactions among probe-polymer, the polymer background and the flexibility of polymer chains at particular pH and salt solution environments. We are sincerely hopeful that the research works presented in the thesis will contribute to the basic understanding of real-world systems and their functional performance in intricate surroundings. Altogether, these studies will provide opportunities for the conceptual design of matrices for appropriate applications comprising controlled release systems with separation of matrices and scaffoldings for enzyme immobilization
Study of Rashba Spin-orbit Coupling in Oxide Heterostructures
No full textSpintronic devices hold the promise of being resilient, energy-efficient, and non-volatile compared to conventional electronic devices. One of the fundamental aims of spintronics is to develop efficient methods for controlling electrons’ spin degrees of freedom. Interestingly, the Rashba spin-orbit (RSO) effect, which arises due to the interplay of intrinsic spin-orbit coupling and broken structural inversion symmetry offers exactly what is required for an efficient spintronics device. This is because the structural symmetry can be broken by an electric field that can be applied either externally or can be generated internally. Moreover, in recent years, a special branch of spintronics, named spin-orbitronics, has been garnering a lot of attention from researchers for its potential to revolutionize the landscape of electronic devices In this respect, transition metal-based oxide heterostructures have emerged as the front-runner for the implementation of these ideas for the following reasons: the possibility of stronger intrinsic spin-orbit coupling, higher spin-flip length, and ease of generating an internal electric field that breaks the structural inversion symmetry. However, the most well-known transition metal-based oxide heterostructures, which show a prominent RSO effect, are ill-suited for device applications due to the emergence of magnetism at the interface. In this thesis, we employ functional theory (DFT) based first-principles calculations to study a polar-nonpolar sandwich heterostructure made of LaAlO3 (LAO)/SrTiO3 (STO) and the polar-polar heterostructure made of LaAlO3/KTaO3 (KTO) and demonstrated that these structures are nonmagnetic while hosting the RSO effect prominently. Surprisingly, we have found a strong inter-orbital coupling between t2g and eg orbital, which contributes to the RSO effect, in the LAO2.5/STO5.5/LAO2.5 heterostructure. Moreover, we find that the strength of the cubic Rashba splitting in this system surpasses the linear Rashba splitting. On the other hand, we have found evidence of both cubic and linear Rashba interactions in the LAO/KTO heterostructure. It is noteworthy that the linear RSO coupling strength found in this system is the highest one for the non-magnetic oxide heterostructure. Interestingly, a potential photocurrent transition path has been discovered in this heterostructure, making it an excellent platform to study the circularly polarized photogalvanic effect
Isoclinism and Derivation of Lie Superalgebras and Lie Yamaguti Algebras
No full textThis dissertation delves into various assertions related to the structure and dimension of the derivation superalgebra Sder(G) of a Lie superalgebra G and other derivation subsuperalgebras of Sder(G). A derivation subsuperalgebra SderI J (G) is considered where I and J are two ideals of a finitedimensional Lie superalgera G. The subsuperalgebra containing superderivations of G whose images are in I and that map J to zero is denoted as SderI J (G). Similarly, Sdern c (G) is defined such that it contains the superderivations δ of G that satisfy δ(g) ∈ [g, Gn] for all g ∈ G. Then the notion of nisoclinism between two Lie superalgebras is defined. It is shown that for finitedimensional Lie superalgebras G and H with an nisoclinism between them established by the pair (φ, θ) there exists an isomorphism from SderGn+1 Zn(G)(G) to SderHn+1 Zn(H)(H). Also, some necessary and sufficient conditions under which Sdern c (G) is isomorphic to certain special subsuperalgebras of Sder(G) are proved. As an equivalence relation, isoclinism plays an important role in the classification of all groups. This dissertation establishes the introduction of the concept of isoclinism to the relative central extensions of a pair of regular HomLie superalgebras and analyses some of their important generalizations. Firstly, the notion of isoclinism, which is an equivalence relation, is defined on the relative central extensions of a pair of regular HomLie superalgebras. Then the relation among the relative central extensions in an isoclinism family of a particular relative central extension is determined. In particular, the conditions required for the two notions, isoclinism and isomorphism to coincide is studied for the relative central extensions of a pair of regular HomLie superalgebras which are finite dimensional. In recent times, LieYamaguti algebras have been studied widely with greater interest. A study on Gderivations is carried out in this dissertation in association to LieYamaguti algebras. LieYamaguti algebras are one type of special algebras that satisfy the properties of a Lie algebra and a Lie triple system simultaneously. A Gderivation of a LieYamaguti algebra G is a derivation with respect to both the bilinear and trilinear operations where G is taken as an automorphism group of G. Some profound results having vital importance in the study of Gderivations are incorporated in this dissertation. Moreover, the relationship between Gderivations and other generalized derivations of LieYamaguti algebras is also established. In this dissertation, the notions of isoclinism and Schur multipliers are introduced for LieYamaguti algebras. Schur multiplier of a LieYamaguti algebra G is the second homology group H2(G, Z) of G with coefficients from C∗. The structural aspects of the covers of LieYamaguti algebras are determined when their Schur multipliers are of finite dimension. Further, as one of the main results, it is shown that the maximal stem extensions of LieYamaguti algebras are precisely same as their stem covers
Comprehensive Analysis of PD Measurement, Condition Assessment, and Electrical Tree Growth Characteristics in Polymeric Cable Insulation
No full textThe presence of partial discharge (PD) activities deteriorates the high voltage cross-linked polyethylene (XLPE) cable, due to void, crack, and contamination of impurities. For this, the detection and classification of PD signal in XLPE cable is very important to find out the root cause of insulation failure. The existence of void amplifies the electric field stress caused by variations in the dielectric constant between air and the insulation that is all around it. PD activities on such XLPE insulation under high voltage stress are responsible for the growth of electrical- trees. The persistence of these discharges replicates several carbonyl channels which are in the form of an electrical tree. The growth rate of treeing phenomena implies a sudden and complete breakdown of in-service cable. Therefore, the detection of the various stages of electrical tree growths is essential for the safety and ensuring the reliable operation of the XLPE cable over a longer period. Deep learning techniques have given opportunities for automated feature extraction and classification of the severity level of discharge activity during tree growth. In this work, the XLPE sample specimens are aged at different applied AC voltages, and the respective PD signals are continuously saved for every cycle. PD signals are used for different stages of pattern classification during the growth of an electrical tree with the help of deep learning architectures. Also, this study presents a novel framework for identifying the evolution of electrical trees using image processing techniques. For this study, microscopic images of electrical trees are captured in the HV laboratory by variation of operating voltage and environment temperature. The growth characteristics are employed along with the fractal dimension to differentiate between different growth stages that occur after the initiation of tree development, including fast growth, stagnated growth, and the breakdown stage. To accomplish the automated identification of the severity level of XLPE in the form of tree growth is achieved with the help of ViT and transfer learning architecture. Along with this, the impact of thermal aging in terms of the electrical and physicochemical characteristics of XLPE insulation is emphasized. In order to accelerate the aging process, 33 kV high voltage XLPE cable samples are heated for 120 and 240 hours at 150°C, compared to an unaged sample. Due to thermal aging micro-voids, cracks are formed and the crystallization of polymeric material deteriorates, which affects the electrical tree growth characteristics. Also, during the aging formation of carbonyl index, molecular change, degree of crystallinity, enthalpy of fusion, and nature of hydrophobicity is observed by Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), X-ray diffraction (XRD), Differential scanning calorimetry (DSC), and Contact angle measurement respectively. The results show that the microstructure of the unaged sample is more resistant to thermal and oxidative stress than that of the aged sample. The molecular structure of the sample breaks down due to the thermo-oxidation reaction, and the crystal area is damaged as a result of long-term use, which decreases insulation performance
Design, Analysis and Coordinated Control of PV-Wind Energy fed LVDC Microgrid with Hybrid Energy Storage System
No full textDue to the escalating global population and accelerating technological advancements, worldwide energy demand is increasing at an unprecedented rate. It significantly strains the energy infrastructure, environment, economy, and society. To address these multifaceted issues, the adoption of renewable energy sources (RES) is prioritized in conjunction with improving energy efficiency and conservation practices. Government, industries, and individuals are working together to reduce the carbon footprint by transitioning to cleaner sources of energy (i.e. roof-top photovoltaic (PV)/wind energy-fed homes or buildings), adopting sustainable lifestyles (i.e. electric vehicles, more usage of public transportation) and supporting the development of low-carbon technologies. In this context, the development of microgrids can help in bringing a synergistic balance between energy generation and load demand. A microgrid is a small-scale, self-contained power network that can operate independently or in conjunction with the utility grid. It typically includes small-scale multiple power generation sources (i.e. PV systems, wind turbines, biomass, fuel cells etc.), energy storage systems (ESS), power converters and a variety of loads. DC microgrid (DCMG) offers the advantage of having relatively simpler control and power management, allowing them to overcome various challenges commonly encountered in AC microgrids such as reactive power compensation, phase synchronization, and high inrush current etc. The complementary characteristics of battery and supercapacitor (SC) in terms of energy and power density are combined to form hybrid energy storage systems (HESS). In recent times, low-voltage direct current (LVDC) microgrid has emerged as a new trend and smart solution for the seamless integration of RES and HESS. However, the energy output of RES is susceptible to fluctuations caused by weather patterns and diurnal variations. Consequently, the output power becomes unreliable and intermittent posing a challenge in providing a stable power to the load. This voltage fluctuation can be easily suppressed by interfacing a hybrid energy storage system comprising battery-SC as storage elements. Therefore, a hybrid RES along with appropriate HESS and proper control mechanism can be considered a reliable option for residential application/remote locations, future DC homes, commercial buildings, spacecraft and shipboard power systems due to its improved efficiency, reliability and controllability. This research work delineates a coordinated control and power management of an LVDC microgrid equipped with a parallel active configured HESS and its comprehensive design, development, and implementation. The system comprises a PV system, battery-SC with bidirectional interfacing converter forming the HESS, a wind power generation system (WPGS), and loads. All these distributed energy resources are connected to the DC bus through appropriate power electronic converters. The PV system consists of a PV array and a buck converter to extract maximum power integrated to the DC bus. The WPGS uses a DC motor coupled self-excited induction generator (SEIG)-based wind system interfaced with the same DC bus through a 3-phase rectifier and a buck converter. The intermittent nature of RES and the unpredictable variations in load demand necessitates the integration of both high-power and high-energy density storage systems in a DCMG. In this context, an actively configured battery and SC based HESS is linked to the 48 V LVDC bus via dedicated bidirectional DC-DC converters. The central idea of hybridization is to mitigate the instantaneous surge current demand and alleviate the charge/discharge stress of the battery during transients, enhancing the cycle life of the battery. By incorporating the battery-supercapacitor (SC) based HESS, the scheme effectively handles sudden and gradual power surges, leading to swift regulation of the DC bus voltage, effective power balance, and reduced current stress on the battery. The PV-wind fed 48 V LVDC microgrid integrated with HESS is designed and implemented with a proposed coordinated power management scheme (CPMS) operating in different modes. The CPMS enhances the dynamic response of the system, enabling it to quickly adapt to changing conditions such as fluctuations in RES generation and load demand. Furthermore, constraints like %SOC of both battery and SC are considered to safeguard the HESS while ensuring smooth transitions between different operating modes without disrupting the system stability. The individual systems are designed, developed, tested and finally, integrated forming the LVDC microgrid executed with proposed CPMS. The developed power management scheme is tested using a DS1103 digital platform, confirming effective mode transition and power sharing among all RES and loads. The experimental results demonstrate that the CPMS efficiently manages power among PV system, WPGS, HESS, and load under various environmental and load disturbances. The developed 48 V LVDC microgrid can be a promising solution for powering consumer electronics in homes/office spaces or other small-scale applications. However, it is important to carefully evaluate the specific needs and requirements of the loads being served
Impact of Social Isolation and Loneliness on Cognition and Psychological Well-being: An Intervention Study among Residents of Indian Old Age Homes
No full textThe population dynamics in India have been undergoing significant transformations characterized by reduced fertility and mortality rates, thereby resulting in an increased proportion of the aged population (60 years and above). Among multiple concerns associated with the process of ageing, increased levels of social isolation and loneliness are important concerns. The prevalence of social isolation and loneliness among older adults, and their co-existence is understudied in India. Further, the impact of social isolation and loneliness on the cognitive functioning and psychological well-being of older adults residing in old age homes is less explored in the Indian context. The study assesses the prevalence of social isolation and loneliness among the residents of old age homes. The study further examines the impact of social isolation and loneliness on the psychological well-being and cognitive functioning of older adults. Moreover, it explores the mediating impact of psychological well-being on the association of social isolation, loneliness, and cognitive functioning in older adults. Further, the efficacy of leisure-based intervention aimed to reduce social isolation, loneliness, enhance psychological well-being, and prevent faster cognitive decline among the older adults has been assessed. The study has been conducted in two phases. In the first phase, data has been collected from the residents of various old age homes of Odisha. The participants have been selected using purposive sampling, and 320 older adults aged 60 years or above participated in the study. Standardized measures like Lubben Social Network Scale-6 (LSNS-6), Revised UCLA Loneliness Scale, Ryff’s Psychological Well-Being Scale, and Hindi Mental State Examination (HMSE) have been used for data collection. After the analysis of pretest scores from the first phase, sixty participants categorised based on their social isolation and loneliness scores have been purposively selected for the second phase; and thereafter randomly assigned for the administration of leisure-based intervention framework. Further, various statistical methods like t-tests, multivariate analysis of variance, linear regression, mediation analysis, and bootstrapping have been employed for data analysis. The results reveal a high prevalence of social isolation (84.38%) and loneliness (86.88%) among the residents of old age homes. Further, a statistically significant MANOVA effect has been obtained for social isolation (F=3.836, p<.01), and loneliness (F=3.782, p<.01) on psychological well-being, suggesting the significant impact of reduced social interactions on the psychological well-being among older adults. Moreover, a significant regression equation was obtained (F=19.28, p<.01), with an R2 of 0.154, suggesting that loneliness significantly impacted the cognitive functioning of older adults. Furthermore, in bootstrapped samples (n=1000), loneliness predicted approximately ten percent of the variance in explaining cognitive health. Additionally, both social isolation (z= -4.71**) and loneliness (z= 4.03**) exhibited significant indirect effects on cognitive functioning of the participants with psychological well-being as a mediating factor. Additionally, leisure-based intervention has been proved effective across all experimental groups of participants in mitigating social isolation and loneliness, and enhancing psychological well-being and cognition among the participants. The findings of the study can be incorporated into measures aiming at alleviating feelings of social isolation and loneliness, improving their psychological well-being, and preventing faster cognitive decline among older adults. The intervention framework can be adapted for the residents of various old age homes to help them have a fulfilling senescence
Electrochemically Prepared Grain Boundaries Free Few-layer Graphene Sheets for Applications in Transport-based Devices
No full textGraphene is an excellent material due to its outstanding physical properties, applicable in high-speed electronic devices due to its higher mobility. However, experimentally obtained mobility is far lower than an expected value which depends on the quality of the graphene. Graphene prepared by the electrochemical (EC) method is better in quality in terms of its physical properties than graphene prepared by other methods. Though there are reports of good qualities of graphene prepared by the EC method, the quality in terms of grain boundaries (GBs) and its electrical properties have not been investigated in detail yet. Enhancement of electrical properties and modulation of mobility of graphene could be realized in field-effect transistors (FET) using different gate materials. Therefore, in this presentation, GBs and their electrical properties, intrinsic mobility using various substrates, such as conventional low-k and high-k dielectric, ferroelectric, and doped-ferroelectric as a gate based on the transport properties of graphene prepared by the EC method have been discussed. The research finding suggests the potential application of graphene prepared by the EC method in future high-speed electronics
Mechanical Behavior Studies of Interpenetrating Polymer Network Based Hierarchical Composites Incorporating Glass Fiber, Nano-Al2O3 and Al Alloy Reinforcements
No full textIn recent years, advanced fibrous/metal reinforced polymer laminated composites have been widely accepted in various aerospace, marine, automotive, energy, chemical, and civil industrial applications. Moreover, laminated composite materials have become competitive in structural industries, where weight saving is one of the highest priorities. The laminated composite materials replace the traditional metallic structures due to their high strength-to-weight ratio, low density, good corrosion resistance and ease of handling, which is essential for modern structural applications. However, the laminated composite material suffers from poor out of plane properties, generally due to usage of low toughened polymer matrix. Also, the weak interfacial adhesion between constituents affects the interface/interphase dominant critical mechanical properties of laminated composite materials. The modification of the matrix and pre-treatment of thin metal (aluminium) sheets could address both of these issues. In this study, following a systematic approach for developing a hierarchical composite, i.e., initially replacing the neat polymer matrix by an interpenetrating polymer network (IPN), namely epoxy-vinyl ester IPN (EVIPN) and subsequently reinforcing the same with a nanofiller (nano-Al2O3), is a novel approach to enhance the interfacial adhesion between constituents, thereby improve the laminated composites' out-of-plane mechanical performance. The present investigation starts with evaluating the mechanical behavior of glass fiber/epoxy (GE), glass fiber/vinyl ester (GVE), and glass fiber/epoxy-vinyl ester interpenetrating polymer network (GEVIPN) composites. The role of cure temperatures (140, 170, 200, and 230 °C) on the flexural behavior of all fabricated composites was examined. The results revealed that amongst various post-curing temperatures, 200 °C cure temperature resulted in optimal flexural properties for all experimented composites. The GEVIPN composite led to ~14%, ~22%, ~23%, ~32%, and ~22% improvements in flexural strength, tensile strength, interlaminar shear strength (ILSS), critical strain energy release rate during mode-I interlaminar fracture test (GIC), and mode-II ILFT (GIIC), respectively, over GE composite at optimal post cure temperature. Dynamic mechanical thermal analysis (DMTA) was conducted in the temperature range of 30 to 200 ºC to correlate all the composites' mechanical and thermo-mechanical behavior. The chemical restructuring of the GEVIPN composite was analyzed by Fourier transform infrared spectroscopy (FTIR). Fractography analysis was also performed to understand the possible failure modes of experimented composites. Further, the emphasis was given on comparing the elevated temperature flexural properties and long term creep behavior of the different composite materials (i.e., GE, GVE, and GEVIPN) has been carried out using the time-temperature superposition (TTSP) principle. At 30 °C, the GEVIPN composite showed the highest flexural properties over GE and GVE composites. The long term creep analysis test results revealed that the GEVIPN composite showed positive reinforcement efficiency for ~385 days with respect to GE and ~46.27 years with respect to GVE at 30 °C under constant stress of 40 MPa. It was observed that GEVIPN composite showed the highest creep resistance as compared to the other two composites at a lower temperature, whereas the opposite trend was observed at elevated test temperature (90 °C). Fractography analysis was done to identify the failure mechanisms and draw a comparison between the tested composites. The next objective is aimed to fabricate the multiscale glass fiber reinforced polymer (GFRP) composites via simultaneous implementation of two hybridization routes, namely, IPN formation and nanofiller (nano-Al2O3) addition, and elucidate their mechanical behavior. The results showed considerable increments in the mechanical performance of modified GEVIPN composites. Improvements of ~18%, ~8%, ~14%, ~19 %, and ~28% in flexural, tensile, ILSS, GIC, and GIIC values of nano-Al2O3 modified composites, respectively, were observed over composite without nano-Al2O3. Composite with 0.1 wt.% of nano-Al2O3 showed the highest mechanical properties in all the modes of testing except for mode-II ILFT testing, where composite with 0.4 wt.% of nano-Al2O3 showed the highest GIIC value. The synergy between the constituents and the thermal behavior of the composites were analyzed via FTIR spectroscopy and differential scanning calorimetry (DSC), respectively. Fractography was performed to understand the composites' failure modes and toughening mechanisms. Following, the effect of in-situ test temperature variation (30 °C, 60 °C, and 90 °C) on the flexural behavior of nano-Al2O3/GEVIPN composites, and the role of nano-Al2O3 content at each test temperature was also analyzed. The test results revealed that nano-Al2O3/GEVIPN composites significantly improved the mechanical degradation resistance at elevated temperatures. DMTA analysis was carried out to study the viscoelastic nature of all fabricated composites in the temperature range of 30 to 200 °C. Fractography analysis was performed to understand the underlying phenomena which dictate the mechanical performance at each test temperature. The last objective in the present study is aimed to fabricate and evaluate the mechanical performance of advanced fiber metal laminated (FMLs). It was previously learnt that simultaneous implementation of two hybridization routes namely, IPN formation and nanofiller addition is a promising route to improve the mechanical properties of polymer based composites. As 0.1 wt.% nano-Al2O3 reinforced EVIPN matrix based composite showed best amongst the experimented composites, further work was carried out to develop FML with this modified polymer as the matrix. Subsequently, to facilitate stronger interfacial adhesion between aluminium and matrix, aluminium surface was mechanically and chemically pre-treated before laminate fabrication. FML prepared by surface modified Al and 0.1 wt.% nano-Al2O3 modified IPN showed the best performance, i.e., ~23%, ~17%, ~24%, ~28 %, and ~37% in flexural, tensile, ILSS, GIC, and GIIC values over FML without any modification (i.e., no surface pre-treatment and without nano-Al2O3). Fractography validated the effect of modification methods on the micro scale phenomena dictating FML failure under various testing modes