Indian Institute of Science Bangalore
etd@IISc Electronic Theses and Dissertations at Indian Institute of ScienceNot a member yet
6204 research outputs found
Sort by
From Particles to People: Vicsek-Inspired Behavioural Modelling Frameworks
No full textIn this thesis, we develop different modelling frameworks to capture the processes of opinion formation and disease spread. The common theme binding them is a nearest-neighbour-based interaction rule; while the closeness of opinions fosters inter-personal interactions, physical proximity facilitates disease spread. Since the nearest-neighbour rules with dynamic local interactions have been shown to enable consensus about the heading of self-propelled particles in a non-equilibrium system, we adapt the Vicsek model.
In the first part of the thesis, we discuss two modelling frameworks that are rendered suitable for the study of opinion formation and influence diffusion. We work on the presumption that the agents' opinions are analogous to directions in the opinion space. We assume that the agents with vectorial opinions related to a common subject form groups. We characterise these groups by the distribution of the initial opinions of the agents, and accordingly, they are either Conservative or Liberal. This modelling aspect exclusive to our work is intended to capture the impact of opinion bias of the group on the eventual behaviour. We also account for the heterogeneity among agents and broadly classify them into rigid and flexible. Although this classification does not seem unique, their characterisation differs from the conventional ones. It is based upon the inclination of agents to update one's opinion and susceptibility to the influence of peers with contrasting opinions. Since the interactions among agents of a group on virtual social platforms are oblivious to the physical distances separating them, we assume them to be arranged on a time-varying and directed influence network. In the first model, the agents are placed on directed influence networks based on opinions, individual tolerance and familiarity. In contrast, the network is generated using Watt-Strogatz's model in the second. This arrangement of agents is unlike the uniform random distribution of particles inside the square box. Additionally, not all interactions are equal; some are more important than others and is quantified using inter-personal weights. The two processes, opinion formation and evolution of the network, in tandem, give rise to several behavioural patterns. We evaluate trends in the behaviour of groups upon varying several model-specific parameters through extensive simulations.
In the second part of this thesis, we discuss two other modelling frameworks proposed to capture trends in disease spread due to human mobility. While the opinion models borrow the directional attributes of particles from the original formulation of the Vicsek model, the motion of the self-propelled particles is of interest in the context of the spread of infectious diseases. However, the rules governing the movement of particles cannot describe the human movement straight away, thereby necessitating suitable modifications. We propose an agent-based framework equipped with a population mixing algorithm and stochastic disease transmission and evolutionary dynamics. The population mixing algorithm incorporates the simple rules governing the movement of particles in the Vicsek model, together with collision avoidance and goal following to mimic human motion. This algorithm generates human motion patterns ranging from short-distance and long-distance movement to activity-driven mobility. On the other hand, the disease models characterise the health condition of agents using three crucial traits of the disease, (1) infection status, (2) severity and (3) awareness, endowed with age-dependent probabilities for transmission, progress and recovery of the disease. The representative population, motion model and stochastic age-specific disease transmission dynamics are used to evaluate different scenarios. The scenarios are combinations of different motion patterns of the agents; we have chosen them to reflect restricted human mobility during phases of the COVID-19 outbreak from the past
Opto-electronic Properties of a Few Dimensionally Controlled Hybrid Halides and Related Systems
No full textTo mitigate the adverse environmental effects of burning fossil fuels, it became necessary to explore alternative ‘clean’ renewable energy sources to meet the ever-increasing energy demands. While silicon-based solar cell devices have been at the forefront for decades, recently organic-inorganic hybrid halide perovskites APbX3 [A = methylammonium (MA+), formamidinium (FA+); X = halides] have transpired as a new family of materials as the alternatives, owing to their exceptional optoelectronic properties such as tuneable bandgap, low exciton binding energy, high carrier mobility, high defect tolerance etc. Remarkably, the efficiency of these solar cells with hybrid perovskites as the active layer has shot up from 3.8% in 2009 to exceed 25% at present. However, the environmental stability of the given materials remains elusive, placing a considerable hurdle on the way to its commercialization. Compositional engineering by partially substituting ‘A-site’ (MA+ with FA+) and/or ‘X-site’ (I- with Br-) ions of the perovskite have proven to be one of the successful approaches to enhance the stability of these materials. More recently, reasonable success in increasing environmental stability is achieved by incorporating bulkier and hydrophobic organic cations at the ‘A-site’, resulting in 2D layered counterparts with enhanced bandgap and exciton binding energy. In this thesis work, we have explored the opto-electronic and thermal properties of dimensionally controlled 2D as well as compositionally engineered 3D hybrid halide systems. In addition to the solar energy, hydrogen evolution reaction (HER) has a great significance in promoting electrochemical energy conversion in fuel cells. Being one of the most efficient catalysts for HER, MoS2 – the flagship member of the 2D layered transition metal dichalcogenides family, has gained much attention recently. We have also discussed the electronic structure of MoS2, responsible for such novel applications
Graphene derivatives sandwiched with porous polyvinylidene fluoride based thin film composite membranes for effective water remediation
No full textSafe drinking water for all is perhaps one of the prime nexus of the 21st century and one of the prime sustainable development goals. Around 1.2 billion people still lack access to safe potable water, while around 2.6 billion people still lack access to proper sanitation. Due to the rapid contamination of conventional freshwater aquifers and a decrease in the groundwater table, there is an urgent need to re-use the unconventional sources and remediate the contaminated aquifers. In this context, re-using desalinated water for practical applications seems a feasible solution since nearly 71 % of the earth is water, and 96.5 % of that water is occupied in oceans. Among techniques like thermal distillation, electro-dialysis, evaporation, membrane-based desalination is the most economical and cost-effective process. In this thesis a classical UCST system (PVDF/PMMA; polyvinylidene fluoride/poly (methyl methacrylate) is chosen to design a porous membrane for water remediation using crystallization induced phase separation. Membranes with varying pore sizes were obtained by varying the composition in the blend and etching the PMMA phase. A unique hierarchical architecture was developed by stitching different membranes using polyacrylic acid, as an adhesive, to achieve a gradient in pore size. The first working chapter (Chapter 3) illustrates the in-situ assembly of polyamide (PA) and PA-graphene oxide quantum dot (GQDs) framework supported on the templated hierarchical porous architecture to improve the rejection and fouling resistance. This strategy resulted in efficient salt rejection (more than 94% and 98% for monovalent salt and divalent salt respectively) studied through pressure enhanced osmosis process using 1000 ppm as draw solutions, and dye rejection (more than 90% and 85% for Methylene blue (MB) and Congo red (CR) respectively) studied through cross-flow experimental set up using 10 ppm as feed solution @ 60 psi pressure. Moreover, the antifouling properties of the PA-GQD modified membranes were superior (80%) as compared to the control PVDF membranes. In the next working chapter (Chapter 4), in order to further improve the antifouling and chlorine tolerance performance, a free-standing GO membrane was positioned in tandem with the PA layer formed in-situ on the surface of hierarchical porous membrane. This strategy resulted in rejection of more than 95% for monovalent ion and more than 97% for divalent ion using 1000 ppm draw solutions; fouling resistance was more than 85%; dye rejection was more than 96% and 90% for a model cationic dye (MB) and anionic dye (CR) @10 ppm feed. This particular membrane showed excellent chlorine tolerance performance @ 2000 ppm NaOCl solution as compared to the membranes described in Chapter 3. In the next chapter (Chapter 5), to arrest the swelling of GO, chemically crosslinked freestanding GO was sandwiched along with the porous membranes, followed by creating an active surface through the layer-by-layer assembly of Poly dopamine (PDA) and Poly styrene sulfonate (PSS) alternately. This strategy resulted in rejection more than 95% for monovalent ion and more than 97% for divalent ion using 2000 ppm draw solutions; fouling resistance was more than 90%; dye rejection was more than 99% and 98% for a model cationic dye (MB) and anionic dye (CR) @100 ppm feed. This particular membrane showed excellent chlorine tolerance performance @ 6000 ppm NaOCl solution. In the final working chapter (Chapter 6), a novel dense covalent organic framework (COFs) embedded PA layer was deposited on a highly stable crosslinked GO@COF membrane to enhance sieving efficacy. This modified membrane showed excellent rejection performance (more than 94%, 98% for monovalent ion, divalent ion, respectively using 2000 ppm draw solution, and near 100% for dyes @ 100 ppm feed) and resistance to fouling attack (more than 93%). Moreover, outstanding chlorine tolerance performance (@ 8000 ppm NaOCl solution) was obtained by this membrane as compared to all previous membranes described in the earlier chapters. The result presented in this thesis suggests the various modifications on PVDF membrane for the fabrication of thin film composite membranes as well as the approaches to maximize the rejection performance with excellent fouling resistance and stability. This study will further help guide the researchers working in the field from both academia and industry.DST and SER
Wireless Content Centric Networks: Cross Layer Designs for Queueing, Caching, Power Control and Beamforming
No full textEver increasing user base of social media platforms such as Facebook, Youtube and the Over-the-Top platforms such as Netflix, Prime Video etc., has increased the demand for High Definition Videos/Contents over the Mobile Networks. This has triggered a new area of research named the Content Centric Networks (CCNs), where the designs of network are based on Contents and their features such as popularity, frequency of request, size of the content etc. Since, the social media and the OTT platforms are here to stay, the Next Generation wireless networks such as 5G, 6G etc. are inherently designed to be content centric.
We study the effect of different components of CCNs, such as Queueing, Caching, Scheduling, Power Control and Beamforming, on the content delivery performance. We propose several cross-layer designs that improve the Quality of Service (QoS) to the users. We present the study systematically in three parts.
In the first part of the thesis, we study the interplay between Queueing and Caching and the effect of fading in wireless CCNs. We consider a CCN with a server connected to several users over a shared finite capacity link. Each user is equipped with a cache. File requests at the users are generated as independent Poisson processes according to a popularity profile from a fixed finite library of files. The server has access to all the files in the library. Users can store parts of the files or full files from the library in their local caches. The server should send missing parts of the files requested by the users. The server attempts to fulfill the pending requests with minimal transmissions exploiting multicasting and coding opportunities among the pending requests. We consider a queue in which requests for the same file from different users are merged and transmitted simultaneously to all the requested users. We study and compare the performance of this novel queueing system in terms of queuing delays when Least Recently Used (LRU) caches are used and when coded caching schemes proposed in the literature are used. We provide approximate expressions for the mean queuing delay for these models and establish their accuracies via simulations. We extend the analysis to the case when transmission errors are also taken into account.
In the second part, we improve over the systems proposed in the first part and show that power control and adaptive scheduling can significantly improve the wireless CCNs performance under fading. We use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learnt for reasonably large systems via this approach. Further we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queueing strategy along with power control. We demonstrate scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.
In the third part, we study cross-layer designs for Multiuser, Multiple Input, Single Output (MU-MISO) CCNs which are proving to be indispensable in the next generation wireless networks such as 5G and 6G. Several recent studies have utilised redundancies in the content request along with the spatial diversity of a MISO system to improve the capacity of wireless networks. It is shown that Max-Min Fair (MMF) Beamforming schemes for MISO based on SDMA, NOMA, OMA and Rate-Splitting could be used to improve the content delivery rates. However, in most of these studies the key aspects such as the queueing delays in the downlink and the user dynamics have generally been ignored. In this work, we study how the interplay between queueing, beamforming and the user dynamics affects the Quality-of-Service (user experienced delay) of downlink in MU-MISO content centric networks (CCNs). We propose queueing theoretic models that are simple in nature and can be directly adapted to MU-MISO CCNs to perform optimal multi-group multicast downlink transmissions. We show that the Simple Multicast Queue (SMQ) developed in the first part for SISO systems can be directly used for MU-MISO systems and that it provides superior performance due to its always-stable nature. Further, we observe that MMF Beamforming schemes coupled with SMQ can be quite unfair to users with good channels. Thus, we propose an improvement to SMQ called Dual SMQ which addresses this issue. We also provide theoretical analysis of the mean delay experienced by the users in such MU-MISO CCNs.Centre for Airborne Systems, DRD
Microwave-Assisted Growth of Laccase Mimetic Copper Oxide Nanozyme for Biosensing Applications
No full textNatural enzymes are highly efficient macromolecular biocatalysts that can selectively catalyze biological reactions with
high activity and substrate specificity under optimum conditions. However, natural enzymes suffer from several inherent drawbacks, such as susceptibility to denaturation, laborious preparation, difficulties in recycling, and high cost,
significantly constraining their practical applications. Among the natural enzymes, laccases are an important class of
oxidative enzymes belonging to the family of multicopper oxidases, which couple the monoelectronic oxidation of its
substrates with the reduction of dioxygen into water. This enzyme exhibits great potential in several applications,
including dye bleaching, anticancer treatment, wastewater treatment, soil bioremediation, and biocatalysts for organic
synthesis. Nevertheless, the poor stability under harsh environmental conditions, high cost, and non-recyclability of the
native laccase enzyme seriously restrict its practical applications.
In this thesis work, we have explored the laccase like activity of Cu2O nanosphere, fabricated using one pot polyol-based microwave-assisted method. The as-synthesized Cu2O nanosphere exhibited outstanding laccase-like activity with a Michaelis−Menten rate constant (Km) value of 0.2 mM for 2,4-dichlorophenol as a substrate, which is noticeably smaller than previously reported nanozymes as well as natural laccase. The laccase-like oxidase property of the nanozyme was exploited in the effective and sensitive detection of biorelevant catechol-bearing molecules such as epinephrine and dopamine. Furthermore, a platform has been developed for the sensitive detection of Acetylcholinesterase using the Cu2O nanozyme as a probe. In general, this robust and recyclable laccase mimetic nanozyme holds great potential for biosensing, sustainable environmental protection, and biotechnology applications
High-Throughput Computational Techniques for Discovery of Application-Specific Two-Dimensional Materials
No full textTwo-dimensional (2D) materials have revolutionized the field of materials science since the successful exfoliation of graphene in 2004. Consequently, the advances in computational science have resulted in massive generic databases for 2D materials, where the structure and the basic properties are predicted using density functional theory (DFT). However, discovering material for a given application from these vast databases is a challenging feat.
In this thesis, we have developed various automated high-throughput computational pipelines combining DFT and machine learning (ML) to assess the suitability of 2D materials for specific applications. Methods have also been developed to draw valuable insights into what makes these materials suitable for these applications. The assessed properties include suitability for energy storage in the form of Li-ion battery (LIB) and supercapacitor electrodes, along with high-temperature ferromagnetism and the presence of exotic charge density waves (CDW).
The ultra-large surface-to-mass ratio of 2D materials has made them an ideal choice for electrodes of compact LIBs and supercapacitors. We combine explicit-ion and implicit-solvent formalisms to develop high-throughput pipelines and define four descriptors to map “computationally soft” single-Li-ion adsorption to “computationally hard” multiple-Li-ion-adsorbed configuration located at global minima for insight finding and rapid screening. Leveraging this large dataset, we also develop crystal-graph-based ML models for the accelerated discovery of potential candidates. A reactivity test with commercial electrolytes is further performed for wet experiments. Our unique approach, which predicts both Li-ion storage and supercapacitive properties and hence identifies various important electrode materials common to both devices, may pave the way for next-generation energy storage systems.
Although there are numerous studies computationally exploring 2D materials as Li-ion battery electrodes, these studies are mostly material-specific, i.e., only a few materials are explored in each of these studies. In our work, however, using the novel descriptor-based technique, we explore thousands of 2D materials for LIB electrode applications. Moreover, to the best of our knowledge, no study has explored these thousands of 2D materials for supercapacitor electrodes yet, which we also achieve.
The discovery of 2D ferromagnets with high Curie temperature is challenging since its calculation involves a manually intensive complex process. We develop a Metropolis Monte-Carlo-based pipeline and conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end ML model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model.
CDW materials are an important subclass of two-dimensional materials exhibiting significant resistivity switching with the application of external energy. We combine a first-principles-based structure-searching technique and unsupervised machine learning to develop a high-throughput pipeline, which identifies CDW phases from a unit cell with an inherited Kohn anomaly. The proposed methodology not only rediscovers the known CDW phases but also predicts a host of easily exfoliable CDW materials (30 materials and 114 phases) along with associated electronic structures.
Apart from these, we have also investigated Li-ion storage in distorted rhenium disulfide crystal, polymorphism-driven Li-ion storage of monoelemental 2D materials, and cation intercalation-driven reversible magnetism in ferrous dioxide using global-energy-minima search technique. Our findings could provide useful guidelines for future experimental efforts. All the data, ML models, and computer codes are available freely for community usage.
We stress that the automated methodologies/workflows developed in this thesis are as important as the results obtained and generalized enough to be applicable to any 2D materials. The available 2D materials databases are ever-growing, and the workflows introduced by us can aid in the discovery of even better application-specific 2D materials in the future.Indian Institute of Science and Ministry of Education, Government of Indi
Quantized heat flow probing thermal equilibration and edge structures of quantum Hall phases in graphene
No full textIn condensed matter physics, usually, phases are described using Landau’s approach, which characterizes the phases in terms of underlying symmetries that are spontaneously broken. Over the past few decades, a different classification paradigm has been used based on the topological order. In a topological phase, certain quantized physical quantities whose values are insensitive to the detail of the system are characterized by topological invariants (TI). The first experimentally realized topological phase in condensed matter systems is the quantum Hall (QH) phase. Since the topological order of a QH phase is a bulk property, the best way to determine the topological order of these phases will involve experiments that can directly probe the bulk. But unfortunately, such an experimental probe was found to be tough to realize. However, thanks to the remarkable validity of the bulk-edge correspondence, which allows us to determine the topological order of these phases by measuring the TI quantity, like electrical and thermal conductance, which are sensitive to the edge modes structures.
The quantization of the electrical and thermal Hall conductance in QH states was established long back. Although electrical Hall conductance has been widely used to understand the topological order of a QH state, it turns out to be insufficient in the hierarchical fractional quantum Hall (FQH) states, where the edge structure is complicated, and transport may occur via both the downstream (Nd) and upstream (Nu) modes. The electrical Hall conductance only reveals the downstream charged chiral edge modes and remains insensitive to the total number of the edge modes, their chirality, and character. By contrast, the quantized thermal Hall conductance is not only sensitive to the downstream charged modes, but it can also detect the other upstream modes, including the chargeless neutral modes and the celebrated Majorana modes, which are not detectable in electrical Hall conductance measurement.
In the major part of this thesis, we utilize the Jhonson-Nyquist noise thermometry to measure the quantized thermal conductance to probe the topological edge structure of the various QH states in single layer and bilayer of graphene. We first measure the thermal conductance of integer and particle-like FQH states. The measured value matches very well with the theoretically predicted values, establishing the universality of the quantization of thermal conductance in graphene. Next, we measure the thermal conductance of the hole-like states (5/3 and 8/3, which are closely related to the paradigmatic hole-conjugate ν = 2/3 phase) for both electron and hole-doped sides with different valley and orbital symmetries realized in bilayer graphene. The measured quantized thermal conductance values are markedly consistent with the thermally non-equilibrated values instead of the thermally equilibrated ones. The non-equilibrated values indicate the divergence of the thermal equilibration length as supported by the theoretical calculations.
Next, we target to achieve the crossover from a thermally non-equilibrated heat transport to fully-equilibrated heat transport. This crossover is pivotal to resolving the dichotomy between different models of edge structure or, in general, to determine the topological edge quantum numbers of FQH states hosting counter-propagating downstream and upstream edge modes. To achieve this crossover, we perform the temperature dependence measurement of the quantized thermal conductance of the FQH states emerging in the lowest Landau level of the single-layer graphene. For particle-like states, we didn't observe any temperature dependence suggesting only the downstream edge modes. Surprisingly, for the hole-like states, we observe a crossover between the two asymptotic limits of the thermal equilibration. Achieving such crossover opens a new route to finding the exact ground state of more complex even denominator FQH states.
In the last parts of the thesis, we study two different problems which are not related to the thermal conductance measurement. In one of the works, we utilize the nonlocal resistance measurement to probe the presence of the dispersive edge modes in bernal stacked trilayer (ABA) graphene. The scaling exponent relating the nonlocal and local resistance was found to be unity over a wide range of the temperature and displacement field, suggesting the edge-mediated nonlocal charge transport. In another work, we investigate the electrical and magnetotransport properties in bilayer graphene encapsulated between two hexagonal boron nitride (hBN) crystals, where the top and bottom hBN are rotationally aligned with the bilayer graphene with a twist angle θt ∼ 0◦ and θb < 1◦, respectively. This results in the formation of two moir´e superlattices, with the appearance of satellite resistivity peaks together with the resistivity peak at zero carrier density. Furthermore, we measure the temperature (T) dependence of the resistivity (ρ). The resistivity shows a linear increment with temperature within the range 10 to 50 K for the density regime bounded by two satellites peaks with a large slope dρ/dT ∼ 8.5 Ω/K. The large slope of dρ/dT is attributed to the enhanced electron-phonon coupling arising due to the suppression of Fermi velocity in the reconstructed minibands
Medium Index Contrast Guided Mode Resonant Structures for Photonic Applications in the Visible-Near Infrared Wavelength Regime
No full textGuided mode resonant (GMR) structures are interesting from the point of view of enhanced light-matter interaction and find applications in sensing, filtering, and miniaturized photonic components. The Guided mode resonance (GMR) arises due to the coupling of the incident light into the guiding medium, followed by interference of the leaky modes with the reflected/transmitted light, resulting in high Q resonance features with field enhancement in and around the structure. GMR structures using high refractive index materials like silicon, germanium, gallium arsenide are explored widely due to the high scattering capability of the material. However, such materials are lossy and are not suitable for visible frequency applications. On the other hand, GMR structures based on materials like silicon dioxide, titanium dioxide, polymer, silicon nitride are transparent in the visible frequency regime yet are not widely studied due to its weak refractive index and low scattering capability. In this thesis, silicon nitride and gallium nitride-based GMR with refractive index contrast of ~0.4-1.5 with respect to the substrate are studied for fluorescence and nonlinear enhancement studies.
In this first part of the work, the high refractive index contrast gratings are compared with the weak refractive index contrast gratings and the techniques are studied to widen the narrow design space encountered in weak refractive index gratings by altering the design parameters of the gratings. Further, fully etched silicon nitride gratings with 80% duty cycle are studied for resonantly enhancing the absorption and emission of the Rhodamine B ITC dye using TE and TM polarized resonances, respectively. The fluorescence enhancement of 10.8 times obtained experimentally using TE polarized excitation and TM polarized collection is then corroborated with simulations by representing the fluorophores to be an array of dipoles in presence of gratings to model the effect of polarization-sensitive resonances on the enhancement of absorption and fluorescence. The GMR structure discussed in this study proves to be a promising approach to realize highly sensitive fluorescence assays and to probe polarization-sensitive information from 2D materials transferred onto these structures.
Next, layered GMR structures consisting of silicon dioxide gratings conformally coated with silicon nitride are studied for resonantly enhancing the third harmonic generation from the 10 nm amorphous silicon layer. The GMR structure is designed to have maximum field interaction with the amorphous silicon layer. In addition, it also behaves as a passive medium with low inherent absorption and nonlinearity, enabling the integration of these structures with other highly nonlinear medium of interest. The GMR structure being sensitive to the incidence angle, the contrast of the resonances and THG enhancement is learned to decrease with an increase in the divergence angle of the incident beam. Experimentally, the backward THG enhancement measured as the ratio of backward THG power in presence of grating and in absence of grating increases from 18 times to 1120 times when the angular spread is reduced from 11 degree to 2.3 degree. This is corroborated with Gaussian beam-based simulations which show good agreement with the experimental results. The structure discussed here enables THG enhancement from the thinnest amorphous silicon layer to the best of our knowledge with minimal absorption at the THG wavelength and provides a scalable platform for nonlinear enhancement and sensing based applications.
Furthermore, the gratings being a one-dimensional periodic structure, the angular sensitivity of the resonances reduces in case of full-conical illumination with the projected wave vector parallel to the grating lines as compared to the full-classical illumination with the projected wave vector perpendicular to the grating lines. Leveraging this concept, four orders of forward THG enhancement is reported using a partial conical illumination setup with a rectangular pupil mask placed at the back focal plane of the objective to limit the angles perpendicular to the gratings, while allowing full angular spread supported by the objective along the grating lines. With this setup, improvement in THG enhancement from 2860 to 1.7x10^4 is observed when the angular spread perpendicular to the grating lines is reduced from 2.3 degree to 0.43 degree. The THG enhancement obtained in this work is the best reported so far from 10 nm silicon to the best of our knowledge and the technique discussed here paves way for enhanced nonlinear generation from any angle-sensitive structure.
Finally, a simulation and experimental based study of amorphous silicon-Gallium Nitride based heterogeneous structures on sapphire substrate is performed for resonant enhancement of second order harmonic generation (SHG) in 5 m thick c-Gallium Nitride. Due to the restrictions imposed by the lattice symmetry in c-Gallium Nitride material, it is necessary to have enhanced longitudinal and transverse fundamental field components to ensure the efficient excitation of nonlinear polarization terms and to efficiently radiate the in-plane polarized SHG, thereby enabling enhanced excitation and collection of SHG using low NA optics. With 45 degree polarized fundamental incidence, TE and TM polarized resonances are simultaneously excited in these structures which leads to efficient SHG generation with maximum in-plane polarized component radiated along the optic axis. Experimentally, 1300 times SHG enhancement is demonstrated at 1460 nm, with the measured SHG efficiency of 1.54 x 10^-3 %/W at a smaller peak intensity of 0.11 GW/cm^2. The measured efficiency is comparable to the reported SHG efficiencies in x-cut Lithium Niobate based structures with d33 axis in-plane allowing efficient excitation and collection of SHG and higher as compared to the z-cut Lithium Niobate structures with maximum susceptibility component d33 oriented out-of-plane similar to the c-Gallium Nitride material studied in this work. This work reports the best SHG efficiency value from Gallium Nitride when compared to previous work on GaN based muti-quantum well based metasurfaces
HBS Constrained Antiparallel β-Sheets and Flat Toroid Turns; Homoserine, Thioamide and 1,3-Thiazine Containing Peptidomimetics: Design, Synthesis, Conformational Analyses and applications
No full textIn chemical biology, short peptidomimetic biological active models have been widely used for anti-microbial activity, drug development, catalysis, and amyloidogenesis. In this thesis, I have synthesized several unnaturally constrained peptidomimetics, studied their conformations using spectrometric methods including NMR, FT-IR and CD and used them in chemical applications: In the H-bond surrogate (HBS) constrained convex anti-parallel β-sheet peptidomimetics two propyl linkers (N-CH2-CH2-CH2-N) HBS have been used to stabilize two shortest possible tripeptide strands into convex anti-parallel sheet conformations. The stereochemical control elements for their structure are elucidated. Their remarkably high thermal stabilities render them applicable in supramolecular chemistry and chemical biology. In another part, to mimic the functional principle of enzyme active sites in the small molecules, we have designed and synthesized flat rigid toroid cyclic peptides constrained by novel HBS analogs. Their residue side chains are oriented uniformly on one face of the cyclic surface, which is desirable for catalysis. The burial of these ionic groups in the desolvated environment of self-aggregated oligomers leads to Born-effect, where the pKas of the buried carboxylates get raised by 2-3 pH units compared to random coil values. In another application, small peptidomimetics containing the N-acyl-homoserine lactone (AHL) have been used to determine the structure-activity relationship in AHLs towards bacterial quorum sensing. In a related strategy, these thioamide homoserine lactone has been used as synthons for incorporating the 1,3-thiazine in the middle of the peptide chain and for the site-selective chemical cleavage, through competing 6-exo-tet and 5-exo-tet cyclization pathways respectively. These thioamide homoserine derivatives and 1,3-thiazine-containing peptides have been tested as potential modulators of amyloidogenesis with promising success
Dynamics and Transport Properties in Polymer Nanocomposites: Role of Interfacial Entropic and Enthalpic Effects
No full textPolymers embedded with nanoparticles polymer nanocomposites (PNCs) have emerged as a new class of hybrid materials, which combine the unique electronic, mechanical, magnetic, catalytic, and optical properties of nanoparticles with the flexibility and processability of polymers, resulting in materials with novel and much improved properties. Experimental investigations accompanied by various theoretical and computational efforts have contributed to the fundamental understanding of the thermal, mechanical, and rheological properties of such PNCs, especially at lower NP loadings. Given the diverse properties of such materials, several potential applications have also emerged. Apart from their varied applications, the soft nanoparticle-polymer composites are a platform of rich physics involving subtle entropic and enthalpic effects which eventually determine their thermo-mechanical properties. In this thesis, we studied complex interplay of interfacial entropic-enthalpic effects and nanoparticles on temperature and time-dependent dynamical changes in PNCs. A short description of the works presented in this thesis is given below.
In Chapter 1, we discuss various types of interactions present in polymer nanocomposite mixtures. The theoretical background of entropic and enthalpic interactions in a binary mixture is discussed in this chapter. A brief discussion on polymer dynamics and confinement-induced finite-size effects have been introduced. Chapter 2 deals with the materials and methods used in this thesis.
In Chapter 3, we discuss a systematic study of segmental dynamics in polymer nanocomposites of polystyrene (PS) and 5 nm diameter polymer grafted nanoparticles (PGNPs) using quasi-elastic neutron scattering (QENS). It provides spatial and temporal information about small length scales (~1 nm) and fast time scales (~1 ns) and, therefore, at temperatures above the glass transition. For athermal PNCs, consisting of PGNPs embedded in chemically identical polymers, interface wettability and matrix chain penetration into the grafted chain layer (or thickness of interface layer, IL) is enhanced with increasing entropic compatibility between the graft and matrix chain. The IL properties are altered by changing the grafted to matrix polymer
size ratio, f which in turn changes the extent of matrix chain penetration into the grafted layer. So, the interfacial length between matrix polymer on these length PGNPs are playing a crucial role for the dynamics and time scales polymer segmental motion in bulk PNCs.
In Chapter 4, we extend the discussions into a confined PNC system. Since it is widely known that various properties of these thin films, especially their thermo-mechanical behavior, can be considerably different from the bulk depending on the thickness and interaction with surrounding media, it is imperative to study these properties directly on the films. However, quite often, it becomes difficult to perform these measurements reliably due to a dearth of techniques, especially to measure mechanical or transport properties like the viscosity of thin polymer or PNC films. Here we explore the complex interplay of two interfacial widths -film/substrate interface and graft/matrix chain interface - on the viscosity of confined PNC thin films through careful atomic force microscopy (AFM). We demonstrate a new method to study the viscosity of PNC thin films using atomic force microscopy-based force-distance spectroscopy. Using this method, we investigated viscosity and the glass transition, Tg, of PNC thin films consisting of polymer grafted nanoparticles (PGNPs) embedded in un-entangled homopolymer melt films. The PGNP–polymer interfacial entropic interaction parameter, f, operationally controlled through the ratio of grafted and matrix molecular weight, was systematically tuned while maintaining good dispersion even at very high PGNP loadings, ϕ. We observed a significant reduction (low f) and giant enhancement (high f) in the viscosity of the PNC thin films, with the effect becoming more prominent with increasing ϕ. This work thus not only demonstrates the tunability of the interfacial entropic effect to facilitate a dramatic change in the viscosity of PNC coatings, which could be of great utility in various applications of these materials but also suggests a new regime of viscosity variation in athermal PNC films indicating the possible need for a new theoretical model. In Chapter 5, we discuss a facile method to prepare PGNPs-based high-density functional polymer nanocomposites using thermal activation of a high-density PGNPs monolayer to overcome entropic or enthalpic barriers to the insertion of PGNPs into the underlying polymer films. The key challenge is to attain a high loading while maintaining reasonable dispersion to attain the maximum possible benefits from the functional nanoparticle additives. We monitor the temperature-dependent kinetics of penetration of a high-density PGNP layer and correlate the penetration time to the effective enthalpic/entropic barriers. Repeated application of the methodology to insert nanoparticles by appropriate control over temperature, time and graft-chain
properties can lead to enhanced densities of loading in the PNC. This method can be engineered to produce a wide range of high-density polymer nanocomposite membranes for various possible applications, including gas separation and water desalination.
In Chapter 6, we discuss the potential application of such nanostructured polymer thin film membranes for water desalination. Membranes with high water flux and large salt rejection are necessary to desalinate water at scale. While polyamide composite (PA-TFC) membranes are the benchmark, there is a continuing need to improve performance systematically. Here, we discuss a novel, transformative paradigm using thin films of pure PGNPs with fixed grafting density but varying chain lengths assembled on PA-TFC membranes through the venerable Langmuir-Blodgett method. The water permeance (A), and flux (J), show a non-monotonic dependence on graft chain molecular weight, likely driven by the modification of osmotic compressibility, which goes through a minimum at 88 kDa. In contrast to most separations, the membrane transport of components of saline solutions is driven by different physics, thus providing us with two distinctly different control handles to optimize these important phenomena opening a new direction in affordable water desalination technologies. Finally, Chapter 7 summarizes the results obtained in this thesis and expresses the future scope of this work