Indian Institute of Science Bangalore
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Single/double atom catalysts for electrochemical energy conversion and storage applications
No full textIncreasing energy demands along with environmental crises have motivated the extensive investigation of alternative high efficiency energy conversion and storage devices such as metal-air batteries, fuel cells and water electrolysers. Oxygen reduction reaction (ORR) is a key reaction in fuel cells and metal-air batteries, etc., whereas oxygen evolution reaction (OER) is a key reaction in metal-air batteries and water electrolysers. For the easy facilitation of the aforementioned chemical reactions, the efficient electrocatalysts are required. Therefore, the rational design of the highly efficient, cost effective and environmentally benign catalysts is of utmost importance. To date, noble metal Pt based catalysts have been identified as benchmark catalyst for ORR, whereas Ir and Ru based oxides are the benchmark electrocatalysts for OER. However, due to the scarcity and high price of Pt, Ir and Ru, considerable efforts have been made for non-precious stable and efficient electrocatalysts.
Recently, transition metal based single atom catalysts (M-N-C) have evolved as efficient candidates for ORR because of its well-defined active sites with very high atom utilization efficiency. We have designed Co-N-C single atom catalysts (SACs) for ORR as the unique electronic and geometric structures of Co-N4 moieties are responsible for high intrinsic activity. We have extended our study further to non-3D transition metal i.e. W based SACs and elucidated its high intrinsic activity and stability due to the WN2C2 active moieties. Atempt has also been made to synthesize N coordinated Mg atoms which have optimal bonding strength with intermediate oxygen species for efficient ORR.
Generally, SACs exhibit well defined active sites with very high atom utilization efficiency but selective mainly towards ORR. To improve the functionality of a catalyst, we have designed the carbon-nitrogen-coordinated Fe-Mo double atom catalyst with high atom utilization efficiency which shows remarkable bifunctional ORR and OER. The importance of electronic modulation around Fe-Mo bimetallic moieties has been elucidated which is responsible for improved activity and stability for ORR and OER. We believe that the present work can provide basic understanding on the designing of efficient single and double atom catalysts for various electrochemical energy storage and conversion devices.Karuna Kar Nand
Compression for Distributed Optimization and Timely Updates
No full textThe goal of this thesis is to study the compression problems arising in distributed computing
systematically.
In the first part of the thesis, we study gradient compression for distributed first-order
optimization. We begin by establishing information theoretic lower bounds on optimization
accuracy when only finite precision gradients are used. Also, we develop fast quantizers for
gradient compression, which, when used with standard first-order optimization algorithms,
match the aforementioned lower bounds.
In the second part of the thesis, we study distributed mean estimation, an important
primitive for distributed optimization algorithms. We develop efficient estimators which
improve over state of the art by efficiently using the side-information present at the center.
We also revisit the Gaussian rate-distortion problem and develop efficient quantizers for
this problem in both the side-information and the no side-information setting.
Finally, we study the problem of entropic compression of the symbols transmitted by
the edge devices to the center, which commonly arise in cyber-physical systems. Our goal
is to design entropic compression schemes that allow the information to be transmitted in a
’timely’ manner, which, in turn, enables the center to have access to the latest information
for computation. We shed light on the structure of the optimal entropic compression
scheme and, using this structure, we develop efficient algorithms to compute this optimal
compression scheme
Development of 3D model and understanding translational regulation of breast cancer
No full textBreast cancer is the most common cancer among women in India and worldwide. It is a highly heterogeneous disease caused by several genetic and epigenetic alterations in mammary epithelial cells. Both inter- and intra-patient heterogeneity exists at various levels including genetic, cellular, and physiological. Due to this heterogeneity, each patient’s response to anti-neoplastic drugs varies. A major reason behind this is the use of essentially homogenous cancer cell lines in the initial drug screening process that does not adequately capture the heterogeneity. Therefore, it is important to develop an in vitro cell culture model that mimics breast tumour microenvironment. Our laboratory recently developed a 3D porous polycaprolactone (PCL) scaffold that better mimics breast tumour microenvironment and validated the same using metastatic breast cancer cell line MDA-MB-231. The current study aimed to further develop this scaffold-based culture system for the growth of patient-derived cancer cells. Multiple tissue processing strategies that perturb the tumour tissue microenvironment to varying levels, and deposition of extracellular matrix, was employed. These studies led to the development of a personalized 3D culture model for patient-derived breast cancer cells that is also amenable for screening drugs. Normal epithelial cells die by apoptosis upon detachment from the extra-cellular matrix, which is known as anoikis. However, cancer cells of solid tumours acquire anoikis resistance – one of the pre-requisite features for successful metastasis. Most of the cancer-related deaths are due to the metastasis of cancer cells to other organs via circulation. Our laboratory has identified AMP-activated protein kinase (AMPK) as a key player in the acquisition of anoikis resistance; yet, its downstream actions are largely unknown. Once active, AMPK brings about energy homeostasis by inhibiting energy-consuming pathways and promoting energy-producing pathways. Protein synthesis is a major energy-consuming pathway that is inhibited by AMPK. In a mass-spectrometric analysis of the phospho-proteome of matrix-deprived breast cancer cells, a novel AMPK-dependent phosphorylation of EIF4GII (Eukaryotic translation initiation factor 4 gamma II) on Serine 889 was identified, suggesting possible regulation of mRNA translation initiation. Immunohistochemical analysis revealed elevated EIF4GII protein expression in breast cancer tissue compared to adjacent normal tissue. Site-directed mutagenesis of EIF4GII on Serine 889 was conducted to create phospho-dead (S889A) and phospho-mimetic (S889E) mutants to study the effect on protein translation. Additionally, analysis of EIF4GII-interactome in matrix-deprived cells revealed reduced interaction with multiple cellular phosphatases. Specifically, DUSP6 (dual-specific phosphatase-6) interaction with EIF4GII was compromised. Inhibition of AMPK restored the interaction of DUSP6 with EIF4GII. Taken together, these data begin to suggest a novel role for AMPK in anoikis resistance via regulating the initiation of mRNA translation
Sustainable Urban Mobility: Urban Public Transport Systems Sustainability Assessment
No full textUrban public transport systems have become an inevitable choice to promote sustainable urban mobility in almost every city in the world. In India, buses are the most popular and convenient mode of transport in urban cities. However, the bus transport was unable to cater to the increasing urban travel demand and of late, unsustainable urban transport trends led to a wide range of economic, social, and environmental costs impacting quality of life. In this regard, along with rapid urbanisation, motorisation, and urban sprawl, it has become imperative to invest in mass rapid urban public transport systems as sustainable urban transport strategy. Sustainable urban transport systems are critical for tackling global climate, reducing local air pollution, improving road safety and well as quality of life. In this regard, it has become imperative for the decision makers to assess the sustainability of urban public transport systems from the perspective of social, economic, and environmental dimensions of sustainability. However, literature review suggests a lack of comprehensive approaches and empirical validations in assessing the sustainability of urban public transport systems. Assessment methods in literature mainly focussed on cost-benefit analysis, travel demand models, multi-criteria analysis to assess the sustainability dimensions. Further, there is a dearth of research studies and empirical validations exploring the influence of urban public transport systems’ transitions on sustainable urban mobility.
Towards bridging the above research gaps, we propose, develop, and validate (with applications) a set of analytical frameworks to assess the sustainability of urban public transport systems. The first part of the research aims to conceptualize sustainable urban public transport systems and we develop an analytical model to analyse the variations in modal split for different Indian cities through the Markovian model. The second part of the research focuses on developing set of evaluation models to assess the sustainability of urban public transport systems with Bangalore city as a case study city. We propose a spatial model to examine the effect of key factors (demographics, land use and public transport) on the urban public transport systems sustainability. We estimate the cost and benefits of alternative fuel technology for urban public transport systems over the lifetime based on total cost of ownership model. We examine the impact of modal shift from existing to new urban public transport system while considering the first and last mile connectivity options. Findings reveal that it is important to assess the sustainability of urban public transport systems while planning, implementation, and operation of urban public transport systems
A Multiscale Study of the Thermodynamics and Kinetics of Vacancy–grain-boundary Interactions
No full textGrain boundaries (GBs) influence many physical and mechanical properties of crystalline materials. This is primarily because of their interactions with other kinds of defects, such as point and line defects. Such interactions are known to depend on the structure and properties of individual GBs, i.e. the GB character. Although much research has been done in this regard, an understanding of the effect of GB character on such interactions is still incomplete.
The present study was undertaken to particularly understand the effect of GB character on vacancy--GB interactions in aluminium (Al). To achieve that, the study was done in five parts using a multiscale modelling approach which involved atomistic and continuum methods. First, using molecular statics (MS) simulations, Al bicrystals with symmetric [010]- and [101]-tilt GBs were constructed, and their stability was checked using molecular dynamics simulations. (A total of 32 boundaries were studied.) The GB structures were subsequently validated using the structural unit model (SUM). Second, vacancy formation and migration energies in the GBs were evaluated using MS simulations and the nudged elastic band method, respectively. These results were then used to obtain equilibrium vacancy concentrations and vacancy migration rates in the GBs. Third, using this data and the kinetic Monte Carlo (KMC) method, the anisotropic GB diffusivities were obtained by simulating random walks of a vacancy in the GBs with a focus on the role of the GB. Fourth, a multiple-site segregation equation was used to study the thermodynamics of vacancy segregation in cases where there is a supersaturation of vacancies, such as in-quenched or irradiated specimens. Segregation was studied as a function of GB character, grain size, temperature, and initial vacancy supersaturation. A phase-field method was then used to evaluate the kinetics of vacancy segregation at the GBs. Finally, quantities derived from the atomistic methods were used to evaluate differences in void nucleation and growth rates again in the presence of a vacancy supersaturation, and the role of GB character was explored.
The first part of the study reconfirms the result of earlier studies that a single parameter cannot be used to characterize GBs that crystallographically vary over a five-dimensional space. Furthermore, it was discovered that the structure of individual GBs could be modelled via a SUM. Some boundaries were identified as favoured GBs, and these could be described using a single repeating structural unit (SU). The structure of non-favoured GBs over the entire misorientation range about [101] tilt could be described by a linear combination of the SUs of favoured boundaries. Consistent mathematical descriptions of the equilibrium vacancy concentration and the overall vacancy jump rate in GBs are provided in the second part of the study. Using these descriptions, effective vacancy formation and migration energies were calculated for the GBs. In the third part of the study, KMC calculations were conducted to estimate GB diffusivities. The role of GB character on the activation energy for GB diffusion was also estimated.
The next two aspects of this study are related to a supersaturation of vacancies and effect of GB character on the segregation of excess vacancies and nucleation and growth rates of voids. In accordance with previous studies, it was observed that (compared to a more realistic multiple-site model) a single-site model overestimates the segregated vacancy concentration when the supersaturation was high and/or the grain size was large. Moreover, the difference between the models is pronounced at lower temperatures. Even though segregation of vacancies at GBs was observed to not drastically reduce the vacancy supersaturation in bulk, it substantially increased the probability of void nucleation at the GBs. The GB structure and, in particular, the presence of deep vacancy traps play a significant role in determining the void nucleation rate at GBs. Such deep traps were predominantly observed in low-angle GBs and GBs vicinal to the coherent twin boundary. As such, these were also observed to act as very good vacancy sinks and could better retard the nucleation of voids than bulk. Whereas, although the high-energy boundaries were observed to also act as good vacancy sinks, they were much more prone to void nucleation. On the other hand, the growth rate was found to be less sensitive to the GB character, with voids growing marginally faster along boundaries that have a higher diffusivity. Based on this, it was deduced that void-depleted zones that are experimentally observed in regions adjacent to the GBs are primarily due to the nucleation and growth of voids at GBs which consume the excess vacancies. Moreover, it was surmised that GBs with higher void nucleation rates are expected to have wider void-depleted zones
Multiple Discrete-Continuous Choice Models with Flexible Specification of Constraints, Utility Forms, and Stochastic Distributions: Applications in Travel Behavior Research
No full textEconometric choice models have been widely used in travel behaviour research to understand human activities, time-use, mobility choices, consumption, and related preferences. Most research in this area had focused on analysing consumer choice of a single discrete alternative from a set of alternatives that are perfect substitutes of each other. In the past two decades, however, a stream of research has emerged to analyse consumer choice of potentially multiple discrete alternatives from a set of alternatives that are imperfect substitutes, along with the continuous choice(s) of “how much to consume?” of the chosen alternative(s). Such choice situations, referred to as multiple discrete-continuous (MDC) choices, are pervasive in travel behaviour research. For example, the most widely analysed MDC choices arise in the context of individuals’ daily time-use, where an individual can potentially participate in multiple activities in a day and allocate the fixed time available in the day to perform those activities. Among the methods used to analyse such MDC choices, the random utility maximization (RUM)-based models have gained traction and resulted in numerous empirical applications in the context of time-use, vehicle ownership and usage, and recreational travel.
This dissertation advances the fields of RUM-based MDC choice modelling and travel behaviour research in the following directions: (a) formulation of new models to analyse and forecast MDC choices by introducing greater flexibility in the constraints faced by consumers and flexible stochastic specifications to represent consumers’ utility functions, (b) enhancing current understanding of the properties of state-of-the-art MDC choice models with flexible utility forms, and (c) application of the newly formulated models to understand time-use patterns of non-working adults in Los Angeles region of California, time-use patterns of commuters in major metropolitan cities of India, and tourism travel expenditures of domestic vacation travellers in India.
The specific methodological contributions of the dissertation are as follows:
(A) A new model formulation to analyse MDC choices at a disaggregate-level, including the number of instances (aka, episodes) different alternatives are chosen and the amount of consumption at each instance of choice, while also accommodating logical constraints across different instances of consumption of an alternative;
(B) A new model formulation to accommodate alternative-specific upper (and lower) bounds on consumptions, and the extension of this formulation to the above-mentioned analysis of disaggregate, episode-level consumption;
(C) Enhanced understanding of the properties of MDC choice models with alternative utility functional forms, which led to: (a) analytic derivations of the distributions of demand functions arising from a specific class of MDC choice models with linear utility functions, and (b) guidelines on what type of MDC choice formulations to use for modelling different types of consumption patterns; and
(D) A new model formulation to accommodate non-IID (not-independent and not-identically distributed) stochastic specifications in MDC choice models with flexible utility functional forms.
The substantive contributions of the dissertation are as follows:
(A) Application of the newly proposed MDC choice formulations to analyze individuals’ daily activity participation and time allocation decisions at an episode level, while considering episode-level upper and lower bounds on time allocation to different activities – for an empirical analysis of non-working individuals’ time-use in Los Angeles, California;
(B) Application of the proposed MDC choice formulations to understand the determinants of expenditure allocations of Indian domestic tourists on their leisure trips – toward identifying strategies for enhancing domestic tourism revenue in India; and
(C) Application of MDC choice models to understand the differences in time-use patterns between commuting women and men in major metropolitan cities of India, with a focus on gender differences in the impact of commute duration on their time-use patterns.
The above-mentioned empirical applications augmented the extensive simulation experiments conducted in the dissertation to evaluate the efficacy of the proposed MDC choice formulations. Specifically, the analysis of Californian non-workers’ time-use helped demonstrate the benefits of episode-level time allocation models and those that accommodate bounds on time allocation – in terms of improved understanding of time-use, statistical fit, and
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prediction performance – over the traditional MDC choice models. The analysis of Indian tourists’ expenditures (on their leisure trips) and Indian commuters’ daily time-use helped verify the properties of MDC choice models with linear utility functions.
The empirical analysis of Indian tourists’ expenditure patterns offered insights that can potentially be used to device strategies toward increasing revenue for the tourism and hospitality industry. The empirical analysis of Indian commuters’ time-use patterns brought to light notable differences in the time-use patterns of working women and men in India. Importantly, this analysis highlighted the need for policies aimed at addressing working women’s time poverty issues exacerbated by their commute
Augmenting Hyperspectral Image Unmixing Models Using Spatial Correlation, Spectral Variability, And Sparsity
No full textHyperspectral imaging sensors sample sunlight reflected from different targets on Earth's surface by utilising a series of contiguous narrow spectral channels. The higher spectral resolution of hyperspectral images (HSIs) comes at the cost of low spatial resolution; therefore, most pixels may consist of multiple targets. Spectral unmixing algorithms are essential in addressing the issue of low spatial resolution of HSIs by incorporating spatial correlation, spectral variability, and sparsity constraints. Moreover, unmixing methods can be used to measure the fractional abundance of pure materials (called endmembers) in a mixed pixel and are also helpful in enhancing the spatial resolution of HSIs.
In the first part of the thesis, sparse unmixing methods were improved by incorporating high adjacency effects and endmember spectral variability. Traditional total-variation-based sparse unmixing methods avoid high adjacency effects among the neighbouring pixels, which leads to over-smoothing and causes errors in the abundance estimation. A four-directional total-variation spatial regularisation approach is proposed to address these issues, which yields robust results when applied to low signal-to-noise-ratio images. Furthermore, spectral unmixing algorithms analyse the HSI by treating endmembers as independent entities in many remote sensing applications such as agriculture or mineral study. Therefore, traditional methods fail to estimate the fractional abundance of endmembers accurately. An endmember variability-based spectral-spatial weighted sparse regression unmixing method is proposed and demonstrated using a real airborne AVIRIS-NG HSI over the agriculture field, where fractional covers of red and black soil were estimated over sparsely vegetated areas. The experimental finding shows promising results as compared to other methods.
In the second part, the generalised bilinear mixing (GBM) model-based nonlinear unmixing methods were improved. Real HSIs are usually contaminated with complex mixed noises such as Gaussian noise, dead pixels, stripes, impulse noise, etc. The intensity of mixed noise may also vary band-to-band in HSIs, which reduces the accuracy of traditional GBM-based unmixing methods. A computationally efficient bandwise-GBM model is proposed to deal with these issues. The proposed technique reduces computation time while being comparable (and often better) to traditional GBM-based unmixing methods.
Furthermore, traditional GBM-based unmixing approaches also reduce unmixing performance by ignoring spatial correlation among the neighbouring pixels. A super-pixel-guided weighted low-rank representation for the robust GBM model is proposed to overcome the above issues. This model employs an entropy rate superpixel segmentation approach to extract homogenous patches in the HSI that underlie the low-rank property. A weighted nuclear norm minimisation approach is introduced for each homogenous patch to estimate the low-rank property, which allocates smaller weights to larger singular values and higher weights to smaller ones. The proposed method significantly improves the fractional abundance estimation by incorporating spatial correlation and sparse noise constraints in the unmixing model.
Finally, spectral unmixing methods are utilised to improve the spatial resolution of HSI by employing high spatial resolution multispectral images (MSIs). Traditional unmixing-based fusion methods avoid noise effects in the modelling, which reduces the accuracy of fusion products. A robust coupled non-negative matrix factorisation is developed for HSI and MSI fusion, incorporating sparse noise effects in the unmixing models of HSI and MSI. Both unmixing problems are coupled by using the sensors' relative spectral response and point spread function.
The above study indicates that the proposed methods achieve robust performance by comprising spatial correlation, spectral variability, and sparsity constraints in the unmixing process
Investigations on Polygonal Voltage Space Vector Structure generation with lower order harmonic suppression using switched capacitive filter throughout modulation range for Drive Applications
No full textMultilevel inverters (MLI) are widely used in a host of industrial applications ranging from renewable energy systems, to electric vehicles, to distributed generation. Due to the switching nature of the output voltage, MLI generate harmonics in output voltage at switching frequency. The harmonics in output voltage generate harmonic currents in the load, which may lead to losses in the system, and may also cause torque pulsations for motor drive applications. Hence, it is necessary to improve the harmonic performance (Total Harmonic Distortion-THD) of the output voltage. To improve the THD of the output voltage, passive filters may be incorporated to suppress the switching frequency harmonics. To optimize the component size in the filter, inverters are operated at high switching frequency. The high switching frequency in MLI generates electro-magnetic interference (EMI) and large dv/dt in the switching devices and motor load. Due to these drawbacks, the passive filtering solution is not very attractive.
To overcome the aforementioned drawbacks, polygonal space vector structures have been proposed. This solution leads to generation of polygonal voltage space vector structures with sides greater than 6, in the over-modulation region. By switching on the vertices of dense space vector structure, lower order harmonics in phase voltage are suppressed with increased utilization of DC link voltage. Polygonal space vector structures can be generated by using a secondary inverter fed with a capacitive supply. The polygon is generated by superposition of the primary and secondary inverter space vectors. Polygonal space vector generation offers many advantages over conventional solutions. Polygonal space vector structures offer increased linear modulation range, which leads to maximum utilization of the DC link supply. In this scheme the main power delivery inverter fed with the active DC link supply is switched at low switching frequency. The reduced switching frequency reduces switching losses and reduces dv/dt. The secondary inverter is fed with a capacitive supply which is balanced at a fraction of the DC link voltage supply. The capacitive supply is balanced at it's nominal voltage during motoring/braking operation by using a novel capacitor balancing scheme. The presence of a single active supply to provide power for motoring operation reduces system complexity and facilitates four quadrant operation. The secondary inverter fed with low voltage capacitive supply is switched at high frequency for suppression of harmonics generated by low switching frequency primary inverter. The secondary inverter can be realized using low voltage semiconductor devices as the blocking voltage requirements for the secondary inverter are considerably lower. The secondary inverter does not provide any active power for motoring operation and hence acts as a switched capacitive filter. Compared to the conventional bulky passive filtering solutions, the switched capacitive filter is cost effective
Smart textiles with Tuneable Architectures for Multifunctional Applications
No full textInnovations in electronics and the rapid developments in communication systems have been unprecedented and made life easier. One such advancement is wireless electronics, where gadgets operate in gigahertz frequencies – transmitting and receiving signals in the form of EM waves during their operation. The increased presence of EM waves in the atmosphere has led to electromagnetic (EM) pollution. With the miniaturization of devices, there is an increased
volume of complex circuitry in a limited space – causing interference between them during operation, termed “electromagnetic interference” (EMI). EMI concerns are rising as they are considered severe threats to devices and their functioning. Different shielding materials were developed to combat this issue, from metals and ferrites to polymer-based nanocomposites. As the filler loading in a polymer-based nanocomposite is limited by processing and the
accompanying stiffness, textiles have emerged as alternative materials with a broad design scope. This thesis entitled, “Smart textiles with tuneable architectures for multifunctional applications,” attempts to develop novel multilayer-like architectures based on coatings to target EMI shielding primarily. Different materials and processes were adopted to maximize EMI shielding effectiveness, UV blocking, and fire protection.
The thesis consists of 7 chapters. Chapter 1 is an introductory note on EMI shielding and textile-based EMI shielding materials. It discusses the terminologies used in EMI shielding, the fundamental shielding mechanisms, and the different phenomena causing attenuation. It presents a comprehensive overview of the evolution of textile-based EMI shields with time and explains the inherent advantages of using textiles as EMI shields over other materials. Chapter
2 is the roadmap of the thesis. It delves into the rationale behind selecting the materials and processes adopted. It explains the advancements in the different chapters, highlighting the critical aspects of each.
In Chapter 3, thermoplastic polyurethane (TPU)-based coatings containing iron titanate (FT) and multiwalled carbon nanotubes (CNT) were coated onto cotton fabrics by a dip coating process. The coated fabrics showed an EMI SE of -12 dB at a thickness of 1.1 mm, working on an absorption-driven mechanism amounting to around ca. 92% of the total attenuation. They also demonstrated a 99.9% UV blocking and a limiting oxygen index (LOI) of 20%. In Chapter 4, water-borne coatings were used on pretreated cotton fabrics. Here, water-borne polyurethane (WPU) was used as the matrix for dispersing chemically coupled CNT and FT. The coating was subsequently coated onto polyaniline-coated cotton fabric (PANi-CF) prepared by an in-situ polymerization route. The coated fabrics exhibited an EMI SE of -40 dB
at a thickness of 2.4 mm, with the absorption contribution being 83%. They also demonstrated a 99.99% UV blocking and an LOI of 23%.
Further, in Chapter 5, an attempt was made to study the effect of different conducting polymer pretreatments on cotton fabric on EMI shielding. Using a facile in-situ polymerization technique, two different conducting polymers, polyaniline and polypyrrole, were coated onto cotton fabrics to give PANi-CF and PPy-CF, respectively. A carbonaceous layer containing graphene nanoplatelets (GNP) and carbon nanofibers (CNF) dispersed in WPU was coated on both the pretreated cotton fabrics. PPy-CF showed better EMI SE (-22 dB), UV blocking (99.99%), and LOI (25%) than PANi-CF. The plausible reasons for the enhancement in properties are explained in this chapter.
Chapter 6 adopted a facile mussel-inspired electroless deposition to deposit metallic silver on cotton fabric (giving Ag-CF). The deposition process was optimized by varying the seeding time to enhance the silver loading on the fabric surface. The Ag-CF was coated with the same carbonaceous layer mentioned above (GNP and CNF dispersed in WPU) to give a ‘hybrid textile.’ The hybrid textile showed an EMI SE of -50 dB, the maximum obtained in this thesis, due to ‘absorption-reflection-absorption’ with absorption percentages going as high as 94%. The UV blocking and LOI values also reached 99.999% and 27%, respectively.
Chapter 7 presents a consolidated summary of the results obtained from the different chapters. It also suggests a possible extension of the work that could be done to enhance the multifunctional aspects of the coated fabric
Enhancement in corrosion resistance of selected high entropy alloys by incorporation of carbon nanotubes
No full textHigh entropy alloys (HEAs) have attracted considerable interest due to their remarkable structural and functional properties. HEAs generally contain 5–13 principal elements with the concentrations of each component lying in the range of 5-35 at. % and the mixing entropy greater than 1.5R. Though researchers have reported on HEAs as promising corrosion resistance material, one of the challenges limiting the application of HEA for corrosion inhibition is the phase inhomogeneity stemming primarily from the elemental segregation within the HEA matrix. Such microstructural inhomogeneity promotes undesirable galvanic coupling and accelerated corrosion. This work addresses the issue of phase heterogeneity in selected HEA systems through the incorporation of carbon nanotubes in the HEA matrix. Following systems were studied: FeCuCrNiCo-CNT, FeCuMnNiCo-CNT and FeCrMnCoNi-CNT composites. In all the cases, it was observed that the corrosive properties of HEAs were highly sensitive to the CNT volume fraction and at an “optimum” CNT volume fraction - high corrosion resistance was obtained. This in turn was intimately related to the phase constitution, coating morphology and surface oxide chemistry. Key findings of the work are: (a) In the case of electrodeposited FeCuCrNiCo-CNT composite coatings - the enhancement of the corrosion resistance at optimum CNT volume fraction was due to evolution of single phase BCC structure from two phase mixture of BCC and FCC structure, enhancement in the coating compactness, increase in the Cr content in the coatings and formation of stable protective oxides such as Cr2O3, NiO, Co3O4, FeO, (b) in the case of electrodeposited FeCuMnNi-CNT composite coatings - the enhancement in the corrosion resistance at optimum CNT volume fraction was due to formation of single phase BCC structure from a mixture of BCC and FCC phase structure, enhancement in the coating compactness, enhancement in the absorption of Fe in the coatings and formation of stabler protective oxide phases such as FeO, NiO, Co3O4, MnO, (c) in the case of FeCrMnNiCo-CNT electrodeposited coatings - the enhancement in the corrosion resistance at optimum CNT volume fraction was due to evolution of smooth and compact morphology, incorporation of increased Cr amount and formation of stabler oxide phases such as Cr2O3, NiO, MnO, Co3O4, FeO, (d) in the case of mechanically alloyed and spark plasma sintered FeCuCrNiCo-CNT composite ingots, the enhanced corrosion resistance at the optimum CNT incorporation was primarily attributed to the enhancement in chemical and phase homogeneit