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Publication Design and Development of Disposable Microsystems for Sensing Applications(Indian Institute of Technology, Jodhpur, 2025-04-28) Gupta, AnkurSensing and diagnostics have become essential parts for our healthcare monitoring. Disposable microsystems can be potential solution for the same which are designed for single-use while eliminating the risk of cross-contamination between patients. These devices are typically aimed to design for straightforward, one-time use, simplifying the testing process and hence, are of low initial cost. Along with this, it addresses environmental concerns associated with single-use plastics. In this context, Paper-based analytical devices (PADs) are desgined and developed which are economical, recyclable, biocompatible, and robust. They consist of a network of hydrophobic and hydrophilic micro-channels which are capable to handle and quantitatively analyze the target analyte. There is no requirement for the clean-room facility to fabricate such device, and it does not require any external pump for the movement of target analytes. Paper strips have been used for several decades for biomedical assays because they provide a low-cost platform for colorimetric testing. Previously, Müller and Clegg reported the first kind of paper- based microfluidic device in 1949. However, the Whitesides group later explored paper-based microfluidics, and it opened new pathways in this field for the use of paper to develop portable, on-site detection in bio-sensing applications. This research investigates simple fabrication strategies for cost-effective mass manufacturing of disposable microsystems for sensing applications, while utilizing a leak-proof paper-based analytical device (PAD) by creating a hydrophobic zone through the proper penetration of ink into the pores of the paper. An inexpensive disposable colorimetric sensor developed through the assistance of chemometric reaction and tested for the optimization of samples and the determination of glucose for different concentrations (0.5-20 mM and 0.1-0.5 M) with a LOD of 2.92 mML−1. To increase platform's resistance to variations in illumination and camera optics, images taken with several cellphones in various lighting conditions were used to train classifiers, with an accuracy of 72.7%. To enhance the analysis platform’s robustness and make it user-friendly, the image data set was taken at various angles of incident light with an overall accuracy of~ 93%. Further, for the multiplexed detection of the analyte, a unique trident-shaped µPAD has been fabricated on a chemically modified A4 paper, followed by the theoretical and experimental fluid flow. Three different biomarkers, glucose, lactate, and uric acid, were detected through colorimetric enzymatic reaction on the novel trident-shaped disposable PAD, with LOD and coefficient of determination of 0.28 mM, and 0.98, 0.40 mM and 0.97, and 0.22 mM and 0.99) respectively. This platform achieves a high accuracy rate of analyte detection (~ 97%) in predicting three different colorimetric findings, both quantitatively and qualitatively through an Android application. The application's integrated image processing tools autonomously identify the region of interest (ROI) and minimize human error, enhancing the platform’s user- friendliness and precision. Proceeding the work, for the exploration of electrochemical sensing, a non-enzymatic electrochemical sensor was developed for the detection of lactic acid with a sensitivity of 0.00176 mA*µM-1cm-2 and LOD of 0.76 mM, followed by computation studies of reaction molecules.Publication Remediation of saline wastewater for organic pollutants using halophilic bacteria and Dunaliella salina(Indian Institute of Technology, Jodhpur, 2025-04-15) Chhabra, MeenuIndustries such as leather, textile, food processing, and petroleum contribute to the rising salinity of freshwater supplies, producing significant quantities of saline wastewater. Conventional water treatment plants face difficulties in treating these effluents for organics removal because the high salinity causes membrane fouling and inhibits the growth of microorganisms. In order to tackle this problem, it is necessary to utilize a specific category of organisms capable of tolerating or thriving in high salt concentrations, known as halotolerant organisms, as biocatalysts. Physicochemical methods can remove salt, but the repeated buildup of organic compounds leads to increased energy consumption and decreased quality of the recovered salts. The Microbial Fuel Cell (MFC) is a suitable method for effectively utilizing saline wastewater. The study describes the cultivation of Dunaliella salina at the saline cathode in a photosynthetic microbial fuel cell (PMFC). The alga was isolated from Salt Lake Sambhar, Rajasthan, India, and identified using 18S rDNA sequencing analysis. The alga growth in PMFC was tested at 0.5 M to 1.5 M sodium chloride. The highest power and current density were obtained at 0.5 M NaCl with 213.38 mW/m2 and 1020.5 mA/m2, respectively. The specific growth rate of algae was 0.4 day−1 with 566 mg/l lipids and 348.9 ± 25.6 μg/ml of glycerol content at 0.5 M PMFC. The PMFC operating at 1.0 M NaCl led to high β-carotene production (24.42 ± 1.8 μg/100 mg). The salinity in natural water resources makes it challenging to apply them in bioprocesses. This study establishes the utility of D. salina in saline water- based PMFC for generating power and high-value Dunaliella biomass. A scaleup PMFC system with a 15-litre cathode and 2-litre anode was developed for the second study, utilizing Dunaliella lipid-extracted algae (LEA) biomass as an electron donor substrate at the anode. The operating system with LEA biomass generates a power density of 621.43 ± 129.47 mW/m2 and a net 0.496 kWh/m3 energy at 0.5 M salinity under outdoor conditions, reducing reliance on external substrates. Therefore, low-cost PMFCs can potentially treat saline wastewater with concomitant energy and high-value product generation.Publication Explainable and Generalized Deep Learning Framework: Study on Atypical Brain Network Development A(Indian Institute of Technology, Jodhpur, 2024-04-11) Banerjee, Romi; Roy, DipanjanSocial cognition refers to the ability to understand, process, and respond to social interactions and the behaviors of others. The Theory-of-Mind (ToM) brain network, associated with social cognition, is responsible for understanding another person’s intentions and ideas, comprising regions such as the medial prefrontal cortex, temporoparietal junction, and superior temporal sulcus. These areas are crucial for recognizing and interpreting others’ thoughts, intentions, and emotions. Recent studies highlight significant interactions between the Fronto-Parietal Network (FPN) and the Temporo-Parietal Junction (TPJ), which are critical for processing social information and predicting social evaluations. Using functional magnetic resonance imaging (fMRI) data, recent research has focused on the early development of ToM in children, extending into middle childhood and adolescence. By age five, children typically develop the ability to understand others’ aims and predict actions based on false-belief paradigms, marking a critical stage in ToM development. Deficits in ToM are evident in neurodevelopmental disorders like autism spectrum disorder (ASD), where individuals show significant impairments in social cognition and communication. Despite extensive research, the heterogeneity within the ASD population and incomplete understanding of its neurobiology pose challenges. Our research focuses on quantifying the temporal stability of ToM and Pain brain networks from early childhood (3 years) to adulthood, using fMRI-based dynamic functional connectivity analyses. We investigate whether temporal stability patterns are associated with performance in false-belief reasoning tasks, particularly in children aged 3–12 years. To decode cognitive states during a naturalistic movie-watching task, we developed an explainable spatiotemporal connectivity-based graph convolutional neural network (Ex-stGCNN). We further employ an explainable convolutional variational autoencoder (Ex-Convolutional VAE) to predict individual false-belief task performance, identifying key brain regions contributing to these predictions as subject-specific neural fingerprints. In the context of ASD, which affects social behavior and communication ability, we utilize large-scale fMRI datasets to develop an explainable deep learning framework aimed at identifying both shared and ASD-specific variability in ToM brain networks. Our models classify typically developing (TD) and ASD individuals using functional connectivity and meta-connectivity features derived from ToM, Default-Mode Network (DMN), Central Executive Network (CEN), and Salience Network (SN). Furthermore, we use these features to predict clinical scores related to ASD symptoms, including ADOS-Total, ADOS-Social, DSM-IV, and Full-Scale IQ (FIQ), through connectome-based predictive modeling (CPMM). The model evaluation demonstrated robust classification performance (AUC 0.86) and accurate symptom severity prediction with minimal error across test sets. Finally, we also identify ASD sub-groups through edge-centric meta-connectivity features using a Rough Fuzzy C-means approach. Overall, this work demonstrates the power of explainable deep learning models in bridging brain network dynamics and symptom heterogeneity in ASD, contributing to more individualized insights into atypical brain development.Publication Light Harvesting Complex and Chlorophyll Aggregation in Membranes using Molecular Dynamics Simulations(Indian Institute of Technology, Jodhpur, 2025-04-09) Debnath, AnanyaThe light-harvesting complex (LHCII), a pigmented protein trimer embedded in thylakoid membranes of plants, captures energy from sunlight through its pigments primarily chlorophyll, and transfers it to the photosynthetic reaction center during photosynthesis. Chlorophyll molecules reside around the LHCII trimer in a belt-like configuration. The conserved composition of the thylakoid membrane which hosts protein complexes and cofactors in plants is found to be essential for light harvesting and pivotal in non-photochemical quenching (NPQ), the process by which excess energy from sunlight is dissipated as heat to protect photosynthetic organisms from photodamage. The chlorophyll derivatives bound in the membrane are important in designing artificial photosynthetic materials. The properties of chlorophylls, such as energy conversion and electron transfer depend on how the chlorophylls are assembled in thylakoid membranes. The membrane comprises different types of lipids with different degrees of unsaturation based on the number and their positions along the alkyl chains. Tail unsaturation plays an important role in photosynthesis, forming different cellular structures, adaptation to environmental stresses, and so on. However, the relation between the tail unsaturation to the stability of chlorophyll aggregate is unexplored till now. The role of the lipidome of thylakoid in regulating the harvesting is unclear. The molecular origin of the stability of the trimeric complex in the membrane remains elusive to date. Thus, the current thesis proceeds to investigate the origin of the structural integrity of LHCII in lipid membranes, thermodynamics, dynamics of chlorophyll aggregation, and the role of lipids on the aggregation using all-atom (AA) and coarse-grained (CG) molecular dynamics simulations. AA molecular dynamics (MD) simulations of LHCII are carried out in a dipalmitoylphosphatidylcholine (DPPC) membrane at 323 K. Central associations of chlorophyll a (CLA) pigment molecules near the LHCII are attributed to conserved coordination between the CLA and specific residues of the first helix of a chain. The residue forms a salt-bridge with the fourth helix of the same chain of the trimer, not of the monomer. In an earlier experiment, three residues (WYR) at each chain of the trimer have been found indispensable for the trimerization and referred to as the trimerization motif. Our simulations show that the residues of the trimerization motif are connected to the lipids or pigments by a chain of interactions rather than direct contact. Synergistic effects of sequentially located hydrogen bonds and salt bridges within monomers of the trimer keep the trimer conformation stable in association with the pigments or the lipids. CG molecular dynamics simulations of CLA are carried out in plant thylakoid membranes at 293 K by varying the total lipid-to-CLA ratio using our previously derived CG model of CLA and MARTINI force fields for lipids. Our simulations show that CLA molecules dynamically form aggregates that break and reform, corroborated by earlier fluorescence quenching experiments. The number of aggregates increases with an increasing concentration of CLA. Selective lipids promote the formation of CLA aggregates governed by van der Waals interactions in plant thylakoid membranes. Less unsaturated lipids reside near the aggregate, promoting increased order and efficient packing. Conversely, higher unsaturated lipids are depleted from the aggregate, imparting flexibility to the membrane. Such preferential locations of lipids around the aggregates result in increasing lateral heterogeneity in the order parameter and density with increasing CLA concentrations. This induces more undulation in membranes, resulting in a lower bending modulus and area compressibility. To understand the role of lipid compositions in the stability of CLA aggregates, the potential of mean force of a CLA dimer is calculated in the presence of the thylakoid and the bilayers comprising either the least or the highest unsaturated lipids by using CG MD simulations. The thylakoid membrane enhances the stability of the CLA dimer compared to the least and highest unsaturated membranes. Lipid mixing, rather than lipid unsaturation, plays a critical role in facilitating CLA dimerization by modulating the membrane microenvironment through stronger lipid-lipid interactions. The microenvironment of the selective lipids in the vicinity of the aggregated CLA remains conserved as in the LHCII trimer which suggests CLA as the origin of the lipid fingerprints in thylakoid. The contact lifetime and waiting time distributions of CLA dimers exhibit the existence of multiple time scales, including most populated fast time scales and less populated slow time scales. The survival probability of CLA dimers follows a non-exponential decay with multiple residence time scales which leads to a time-dependent rate, unlike conventional rate theory. Such non-exponential decay of dimer survival is a manifestation of dynamic disorder resulting from coupling between time scales of dimer formation and higher-order aggregates. In summary, the study indicates that the conformation of the LHCII trimer, along with the CLA association and the microenvironment of the selective lipids in its vicinity, as a whole, can be instrumental for the stability and functions of the photosynthetic machinery. The results from the study can be extended and useful in understanding the role of the CLA binding LHCII or their aggregation hosted by thylakoid on the optimal photosynthetic function by enhancing light absorption and by dissipating excess energy. Our research provides the foundation for a better understanding of more complex biophysical phenomena, such as NPQ, in the futurePublication Smart Engineered Soft Biomaterials as Advanced Healthcare Therapeutics(Indian Institute of Technology, Jodhpur, 2025-03-25) Ghosh, SurajitSoft biomaterials play a pivotal role in various biomedical application domains. Materials with dynamic properties, such as the ability to expand and contract, change stiffness, self- heal, or dissolve in response to environmental changes, are highly sought after for various applications including biosensing, drug delivery, soft robotics, and tissue engineering. In the last thirty years, the creation and use of biomaterials has emerged as a highly active and promising field of study, situated at the crossroads of chemistry, materials science, bioengineering, and medicine. Utilizing self-assembly in the development of functional biomaterials is a highly promising and stimulating field of study, offering significant potential for the treatment of injury or disease. Peptide self-assembly is becoming a promising method for creating sophisticated biomaterials that possess exceptional physicochemical and biological characteristics. In this regard peptide sequences with inherent self-assembly capabilities have been intentionally engineered to give rise to diverse structural aggregates, including nanofibers, nanovesicles, nanobelts, and nanotubes. Over the course of the past few decades, a number of methodologies have been devised with the purpose of designing self- assembling peptides. Various supramolecular nanostructures have been created using synthetic peptides that possess beta sheet, alpha helix, triple helix, ELP-like, or amphiphile structures. These nanostructures were specifically designed to undergo self-assembly, resulting in the acquisition of specific properties and functionalities. This study investigates the use of bioinspired self-assembly to create a variety of supramolecular healthcare materials that can be used in drug delivery as well as antibacterial regenerative medicine purposes. First of all, we fabricated peptide based nanovesicles crafted from the “Hotspot” region of alpha beta tubulin heterodimer interface for the targeted delivery of a tubulin targeted drug in combination with an mTOR inhibitor. By in vitro assessment we found that such a tubulin targeted peptide nanovesicle mediated targeted delivery can increase the therapeutic potential of both docetaxel and everolimus significantly and can be employed as anticancer nanotherapeutic. In our next research work, we have constructed a Substance P mimicking supramolecular hydrogelator octapeptide by fusing truncated C-terminus peptide sequence “FFGLM” derived from Substance P along with an integrin-binding “RGD” motif. This designed octapeptide under the influence of its N-terminus aromatic capping group together with its uniquely balanced hydrophobic and hydrophilic residues undergoes quick self- assembly at biological pH (pH 7.4) to give rise to a soft yet thixotropic hydrogel matrix with superior thermal and pH responsive property. From experimental analysis, we envisioned this designed hydrogel as a futuristic cytocompatible matrix having both wound healing and pH responsive drug releasing therapeutic effectivity. Next in an endeavour to find new synthetic AMP, we have designed and constructed a few amyloid-inspired multi-domain peptide amphiphiles comprising N-terminus lipid chain succeeded by a core hydrophobic zone in combination with a C-terminus cationic heparin-binding motif. These synthesized peptide amphiphiles differ only by their aggregation propensity of the core hydrophobic zone. Interestingly, all the synthesized peptide amphiphiles were found to exert their antibacterial effectivity above their critical aggregation constant. However, our study reveals that the extent of antibacterial effectivity is guided by both hydrophobic core zone in combination with C-terminus positively charged heparin-binding motifs derived and modified from the Aβ42 peptide core. The lead peptide by its more aggregation-prone core region undergoes rapid self-assembly at lower CAC and shows higher LPS binding affinity with superior antibacterial effectivity against multi-drug resistant staphylococcus aureus in comparison with other analogues. The lead multidomain LVK-PA peptide under its self- assembly propensity gives rise to the formation of the thixotropic hydrogel with significant antibacterial wound healing property. Experimental data including wound closure ratio, % collagen deposition, and expression of various pro-inflammatory cytokines like IL-6, TGF-β along with CD-31/α-SMA revealed that the designed hydrogel promotes the healing of both gram-negative P. aeruginosa and gram-positive MRSA-infected diabetic wounds through reduced nflammatory repercussions and enhanced angiogenesis. In our final study, we have developed an extracellular matrix mimicking, wound-microenvironment responsive multi- component hybrid hydrogel scaffold composed of lysozyme-derived amyloid fibril (LZ) co- assembled with heparin Sulfate (HS) by mere utilization of their inherent favourable non- covalent interactions. Heparin Sulfate was employed to construct this amyloid-sugar co- assembled extracellular matrix (ECM)-mimetic scaffold due to its striking resemblance to heparan sulfate. Further to increase its conductivity at physiological pH and simultaneous antibacterial efficacy in an acidic chronic wound microenvironment we have incorporated tannic acid-functionalized silver nanoparticle into the hydrogel composite. The enhanced antimicrobial efficacy of this composite hydrogel is likely linked to the pH responsive disassembly of the supramolecular hydrogel matrix when exposed to chronic diabetic wound conditions. Apart from its antibacterial effectivity the at pH~5.5 the designed hydrogel can potentially eradicate bacterial biofilm formation providing a promising strategy for the management of clinical chronic wounds. These multifaceted synergistic effects significantly expedite the process of wound healing and enhance the overall quality of wound repair.Publication An All-Fiber Multimode Interference Device For Power Splitting In Single Mode And Few Mode Fiber Based Passive Optical Networks(Indian Institute of Technology, Jodhpur, 2025-04-28)The demand for high-speed internet connectivity is rapidly increasing, driven by the growing number of applications that require low latency. As more devices and services depend on real time data transfer, the need for faster and more reliable connections becomes crucial. One promising approach to addressing the capacity limitations of conventional single mode fiber (SMF) networks is mode division multiplexing (MDM) in few mode fibers (FMFs). This technique leverages multiple guided modes within a multimode fiber to enable distinct transmission channels. Successful deployment of MDM systems necessitates the development of various mode transparent optical components. Multimode interference (MMI) devices play a vital role in modern photonics systems, finding applications in telecommunications, sensing, etc. These devices utilize the phenomenon of multimode interference, where light propagating through a multimode waveguide undergoes self-imaging due to the interference of different modes. MMI devices offer multiple advantages such as compactness, low insertion loss, and compatibility with standard fabrication processes. Optical power splitters play a crucial role in passive optical networks (PONs) by enabling the distribution of optical signals from a single source to multiple endpoints. This functionality is vital for efficient bandwidth utilization, allowing service providers to connect numerous users without the need for active components. By facilitating the sharing of optical signals, these splitters help reduce infrastructure costs and minimize maintenance requirements, as fewer active elements are needed in the network. Additionally, they contribute to the overall reliability of PONs, as passive components are less prone to failure compared to their active counterparts. The ability to seamlessly integrate splitters into the network architecture also enhances scalability, making it easier to expand services as demand grows. Most existing optical devices are based on planar optical waveguide technology (i.e., planar lightwave or photonics integrated circuit technology). While this technology is well-established, its compatibility with optical fiber components is limited due to the significant size difference between optical fibers and planar waveguides. Various coupling strategies have been explored to enhance energy transfer, but these mechanisms often introduce loss and complexity. A promising solution to these challenges lies in the development of all-fiber based devices. Such devices would reduce coupling losses and facilitate seamless integration into existing optical fiber networks. In this work, an all-fiber MMI-based mode-transparent 1 × 4 power splitter is experimentally demonstrated using square core multimode fiber. The proposed device is capable of splitting the input into four equal parts, regardless of the launch field. Initially, the power splitter design is focused on single mode fiber networks. By building upon the principles established in the single mode configuration, the extended design incorporates additional considerations necessary for effectively managing multiple modes within few mode fibers network. Comprehensive simulations and proof-of-concept experiments support the feasibility of the proposed device for practical applications. The presented device is highly compact, measuring less than 1 cm. Although the power splitter design investigated in this work is specifically for 1 × 4 power splitting, the design concept and methodology can be applied to any 1 × N configuration. Additionally, an all-fiber designs for a 4×4 higher order switch and a mode sorter are provii posed. The optical switch is based on a splitter-combiner architecture, where the input is first split into four equal parts and then recombined, by adding appropriate phase values, to one of the four output ports. The proposed device design is scalable to N × N configurations. In Mode Division Multiplexing (MDM) systems, optical switches are essential for managing and routing multiple signal modes (or spatial channels) within a single optical fiber. MDMenhances the system’s capacity by utilizing different propagation modes, and optical switches facilitate the selective routing of these modes to various destinations. A key component in MDM systems is the mode sorter, which is responsible for separating the spatial modes. In order to design the aforementioned devices, the scope of the one -imensional analysis has been extended to two dimensions. This expansion provides a clear understanding of the conditions under which the proposed method can be effectively applied to the design of all fiber devices. For this, a computationally efficient method is proposed. This method involves bifurcating a 2D waveguide into two 1D waveguides. The mode propagation analysis of the 1D waveguides is utilized to calculate the propagating fields of the bifurcated 1D structures. These fields are then used to compute the fields of the 2D waveguide. This approach eliminates the necessity of calculating the propagation constants for the 2D modes. The proposed method operates in a reduced d imensionality, allowing for significant computational efficiency. By utilizing this method, up to 75% of computation time can be saved. The all-fiber devices proposed in this work could offer a viable solution for power splitting and switching in future few mode fiber (FMF) networks that utilize mode division multiplexing techniques.Publication Experimental Investigations on Dissimilar Metal Welds Fabricated Using Nickel Based Welding Consumables(Indian Institute of Technology, Jodhpur, 2025-04-28) Chhibber, RahulIn the current context of industrial advancement, there is a rapid surge in electricity demands. Meeting this escalating need involves either enhancing the efficiency of existing power plants or establishing new ones. Pulverized coal stands out as the cost-effective fuel choice predominantly utilized in power industries. However, the expansion of power plants contributes to increased emissions of gases like CH4 and CO2. The efficiency of power plants hinges largely on their operational steam parameters, particularly temperature and pressure. Supercritical power plants offer a solution by boosting efficiency through elevated operating conditions, resulting in reduced coal consumption and lower CO2 emissions. Fabricating and maintaining power plants pose significant challenges due to the demanding working conditions. Utilizing cutting-edge high-temperature materials has become imperative in the power plant sector. Different components and structural elements necessitate dissimilar metal welds to enhance structural integrity and performance. This research endeavors to develop Shielded Metal Arc Welding (SMAW) electrode coatings aimed at enhancing performance and mitigating issues associated with high-temperature failures. SMAW is widely employed for welding, repairs, and maintenance in this context. However, detailed information on the welding consumables currently in use remains classified and limited in the public domain. This research aims to develop coated electrode for producing Inconel 617/SS304H dissimilar joints, particularly for power plant applications. Utilizing the extreme vertices methodology, the coating compositions were formulated from mineral systems like CaO-TiO2- SiO2-Na3AlF6 and CaO-CaF2-SiO2-SrO. The experimental characterization focused on thermophysical, physicochemical, and wettability roperties of the coatings. A regression model was employed to analyze the impact of coating constituents. Subsequent multipass bead-on-plate experiments using these coatings were used to evaluate their performance. Qualitative and quantitative assessments of the resulting beads determined electrode suitability. The most effective coating was then applied to fabricate dissimilar IN617/SS304H welds using extruded IN-617 core wire. Mechanical, microstructural, and hot corrosion properties of the elds were thoroughly examined. Additionally, a comparative analysis between our aboratory-developed electrodes and commercially available ones was conducted. Overall, the study demonstrates successful development of SMAW electrode coatings tailored for welding IN617/SS304H, specifically for A-USC power plant applications. These developed electrodes exhibit superior mechanical properties, high-temperature corrosion resistance, and satisfactory microstructural stability compared to their commercially available alternatives.Item Construction of Carbon-Carbon and Carbon-Heteroatom Bonds using Diaryliodonium Salts as Aryl Transfer Reagents(Indian Institute of Technology, Jodhpur, 2025-04-24) Murarka, SandipArenes represent a fundamental structural motif in a diverse array of organic molecules, encompassing pharmaceuticals, clinical drug candidates, agrochemicals, and nctional materials. Hence, the development of novel and sustainable arylation methodologies are of gnificant importance in organic synthesis. Diaryliodonium salts being bench stable and readily available, renowned for their exceptional reactivity and facile preparation, serve as versatile aryl precursors in a wide range of organic ransformations. In this regard, we have developed a metal-free, one-pot, three-component coupling reaction incorporating diaryliodonium triflates, carbon disulfide, and both cyclic and acyclic aliphatic amines, resulting in the synthesis of biologically active S-aryl dithiocarbamates. Notably, the synthesized S-aryl dithiocarbamates are found to exhibit drug-like properties, as confirmed by in-silico analysis and cellular studies. Employing the same reaction methodology, we have also synthesized S-diarylmethane dithiocarbamates through coupling between cyclic and acyclic primary and secondary amines, carbon disulfide, and p-quinone methides. The key features of this method include its mild reaction conditions, high functional group tolerance, and scalability. Moreover, we have also devised a copper-catalyzed annulation of α,β-alkynic N-tosyl hydrazones with diaryliodonium salts to synthesize N-aryl pyrazoles. As established by control experiments, mechanistic investigations revealed that the copper catalyst is essential for this process, which involves sequential cyclization, deprotection, and arylation steps. Furthermore, we have demonstrated an organophotoredox-catalyzed allylic arylation and aryl sulfonylation of Morita-Baylis-Hillman (MBH) acetates utilizing diaryliodonium triflates. This methodology exhibits broad substrate scope, scalability, and high tolerance to diverse functional groups.Item Effects of Processing Methods on Microstructure-Flow Property Relationships at Ambient and Elevated Temperatures in Binary Al-Si Alloys(Indian Institute of Technology, Jodhpur, 2025-05-16) Kashyap, B.P.Al-Si alloys constitute a widely used group of light metals and alloys. In spite of it traditionally being a well-studied system, no systematic attempts have been made to overcome the strength-ductility limitations existing in these alloys. It becomes even more important to investigate when numerous developments have been witnessed in playing with microstructure control by newer processing methods to enrich our capability of broadening the process-structure-property optimization regimes. This research investigates the relationship etween composition, processing, structure, and properties of binary Al-2Si to Al-30Si alloys at room and high temperatures. The Al-Si alloys were produced by melting and casting in the induction furnace. Microstructure and mechanical property studies were conducted on as-cast, hot extrusion, friction stir processing (FSP), and high-pressure torsion (HPT) processed Al-Si alloys. The minimum grain sizes obtained are 1-2μm on HPT, 2-3μm upon FSP, and 5-8μm on extrusion, which are much smaller than the as-cast grain sizes of 15-115μm. Irrespective of processing methods, strength was found to increase with decreasing grain size according to the all-Petch type relationship, but the strengthening caused by the grain interior and the grain boundary zone was found to be sensitive to the processing method. In contrast to this, at elevated temperatures (450-840K), the strength decreased, whereas elongation ncreased with decreasing grain size. Superplastic behaviour investigated in FSP material showed elongations of 191-341%, depending on Al-Si composition, with the aximum elongation of 341% corresponding to the Al-12Si eutectic alloy. The values of parameters of the constitutive relationship for high-temperature deformation were obtained to be as follows. (i) Strain rate sensitivity index (m) was found to be a maximum of ~0.4 for FSPed l-12Si alloy, whereas it was a minimum of 0.04 for the hypereutectic Al-30Si alloy. The magnitude of m increased with test temperature and generally decreased with increasing Si content for all the processes involved. (ii) The apparent activation energy for deformation (Qa) was obtained to range from 79.4±11.8kJ/mol to 572±148kJ/mol. The FSP material shows m≥0.4 and true activation energy t~59.4kJ/mol over the temperature range of 530-650K and 221.5kJ/mol over 650-780K. The echanism for superplastic deformation is accordingly suggested to be grain boundary sliding and its accommodation by grain boundary diffusion at the lower end of the temperature range, and the same by lattice diffusion at the upper end of the temperature range. Qt generally increased whereas m decreased with increasing Si content, for which the deformation mechanisms were identified to be dislocation climb creep for low Si-containing Al-Si alloys, but power law breakdown appeared for hypereutectic Al-Si alloys in as-cast and extruded Al-Si alloys. According to the present results on FSPed Al-8Si to Al-30Si, both dislocation-based creep mechanisms (m≤0.3) and superplasticity-type (m≥0.3) mechanisms occur over different ranges of temperatures, strain rates, and compositions. In conclusion, it is possible to make Al-Si alloy stronger by selecting higher Si content, but irrespective of composition, the same can be made to exhibit superplasticity, thus providing an opportunity to extend the applications of Al-Si alloys by proper processing and meeting the service conditions.Item From Words to Structures: Enhancing Document Image Analysis using Handcrafted and Machine Learning Techniques(Indian Institute of Technology, Jodhpur, 2025-04-30) Harit, GauravOur thesis explores the field of document image analysis, outlining its significance and the challenges it faces. We progress from basic text extraction (word spotting) to understanding complex structures (cell extraction and article segmentation, addressing various challenges found in this progression. Our first work (Srivastava and Harit [2020b]) navigates the complexity of Word Spotting in Cluttered Environments where a word is cluttered by a strike-through with a line stroke. We present a comprehensive approach to word spotting in cluttered environments, focusing on the use of Vertical Projection Profile (VPP) feature and its modified version, the combinatorics Vertical Projection Profile (cVPP). We compare our method with (Rath and Manmatha [2003]) and PHOCNET (Sudholt and Fink [2016]) for handwritten word spotting in the presence of strike-through, achieving better results. We then explore structural insights of document images, focusing on cell extraction and horizontal-scale correction in handwritten form images (Srivastava and Harit [2020a]). Our focus laid on structured documents like forms and cheques, where there is a predefined space called frame field/cell for the user to fill the entry. We address the non-uniformity of inter-character spacing while writing by extracting cells using the modified Region growing method (Gonzalez [2009]) and applying horizontal scale correction on the extracted form fields. This system results in reduced error rates when applied as a preprocessing step in a recognition system (Almazán et al. [2014]). In continuation to handwritten form images, we propose a graph-based deep network forpredicting the associations pertaining to field labels and field values in heterogeneous handwritten form images (Divya and Gaurav [2019]). We consider forms in which the field label comprises printed text and field value can be the handwritten text. We have used a Graph Autoencoder (Kipf and Welling [2016a]) to perform the intended field label to field value association in a given form image. It was the first attempt to perform label-value association in a handwritten form image using a machine learning approach. Simultaneous super resolution and denoising in document image (Divya and Gaurav [2023]). The approach is a one shot unpaired technique where a single unpaired example is used as reference for training a SinGAN model (Shaham et al. [2019]), where first, a clean reference image is used to train a SinGAN to learn the characteristics of the clean image. Then, super resolution and denoising of the given test image is carried out using another SinGAN. The formulation of the loss function helps in this task by prompting the generated images to have characteristics similar to the reference clean image. Selective Image denoising (Divya and Gaurav [2024]) deals with removal of unwanted noise from images. The existing methods process an image in its entirety, assuming that the noise uniformly affects the entire image. For inputs where the noise affects a localised part of the image, applying methods that attempt to denoise the entire image can adversely affect the clean portions. To address this issue, the authors propose a deep reinforcement learning-based framework for selective image denoising. The framework uses a two-step procedure that first identifies the noisy patch and then denoises the extracted patch. The authors use reinforcement learning for noise localization and PixelRL (Furuta et al. [2019]) for noise removal. Next, we perform an article segmentation model for noisy degraded old newspaper images as a downstream task. These are Bangla newspaper images taken from the era of 1937-1980. We propose a deep model based on convolution, dilated convolution, and skip connections to handle noise elements in these images. In this work we faced two major challenges: 1. noise was getting super resolved while enhancing document, 2. there were noise patches denoising which lead to text removal from clean areas. The focus of the work was to demonstrate the benefits of the preprocessing steps of denoising and super resolution on a downstream task.Item Corona Product in Signed Graph Theory: Modelling, Structural Balance, and Control(Indian Institute of Technology, Jodhpur, 2025-05-03) Yadav, Sandeep KumarSigned networks help in studying complex relationships and interactions in various domains, offering insights into the dynamics of positive and negative influences within interconnected systems. I extend the concept of the corona product of two graphs to signed graphs, building on the marked graph framework introduced by Beineke and Harary in 1978. This approach provides a visual method for understanding the process of network generation, aiding in the analytical determination of the resulting signed graph properties. I explore both the structural and spectral properties of the corona product of signed graphs. Additionally, I introduce the concept of signed corona graphs, which are formed by iteratively applying the corona product to a fixed small signed graph, known as the seed graph. These signed corona graphs serve as a model for generating large, evolving signed networks. This study includes an examination of the structural properties of corona graphs, such as the statistics of signed edges, various types of signed triangles, and degree distribution. I also investigate the algebraic conflict within signed corona graphs produced by specially structured seed graphs. By carefully selecting a seed graph, it is possible to generate corona graphs that mirror the properties of real-world signed networks. I have expanded the corona product of signed graphs into a generalized corona product of signed graphs motivated by the generalized corona product of unsigned graphs. I study the structural balance and spectral properties of these graphs. By utilizing the notion of coronal of a graph, I determine the formulas of characteristic, Laplacian, and signless Laplacian polynomials of eneralized corona product of signed graphs. I have also identified sufficient conditions for the generalized corona product of some special collections of signed graphs to be co-spectral. Following this, I analyze the controllability of the corona product of signed graphs and derive sufficient conditions based on the spectra of corona product of signed graphs.Item Modified 2D MoS2 and SnS2 Flakes for Sensing Applications(Indian Institute of Technology, Jodhpur, 2025-05-12) Kumar, MaheshThe emergence of two-dimensional (2D) transition metal dichalcogenides (TMDCs), particularly SnS₂ and MoS₂, has sparked significant interest in the field of advanced materials due to their unique layered architecture and atomic-scale thickness. These materials exhibit exceptional electrical, optical, and chemical properties, rendering them highly suitable for a wide range of applications, including sensors, hotodetectors, and various optoelectronic devices. However, the full potential of SnS₂ and MoS₂ is often hindered by common challenges such as large-area controlled growth, limited optical absorption in pristine materials, and slow response and recovery times attributed to inefficient charge transfer dynamics. To address these challenges, we employed a thermal chemical vapor deposition (CVD) technique to optimize the growth conditions for high-quality SnS₂ and MoS₂ films. By systematically adjusting parameters, we achieved vertically aligned structures and uniform layers, laying the groundwork for enhanced device performance. Comprehensive characterization techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and photoluminescence (PL) were utilized to analyze and confirm the structural, morphological, and optical properties of the synthesized materials. Based on the results from these analyses, we propose a detailed growth mechanism in which in-plane SnS2 and mixed MoS₂ flakes acts as a seed layer for the initial development of vertically aligned SnS2 and MoS₂, ultimately leading to the formation of an interconnected three-dimensional network of these flakes. We developed a highly sensitive gas sensor based on vertically aligned SnS₂ flakes, which exhibited exceptional selectivity toward NO₂ over various other toxic and combustible gases[Kumar et al., 2022]. The sensor demonstrated stable performance and strong selectivity, supported by electronic structure calculations. To further enhance gas sensing capabilities, we implemented defect and interface engineering techniques, specifically inducing optimal sulfur vacancies in the SnS₂ lattice through nitrogen plasma treatment[Kumar et al., 2023a]. This vacancy engineering significantly improved electronic conductivity and increased the active surface area for gas adsorption, resulting in a remarkable sensitivity to NO₂ and complete recovery at 120°C. In parallel, we explored the functionalization of mixed MoS₂ flakes for NO₂ gas sensing. Recognizing the limitations of pristine MoS₂, which suffers from low response and slow recovery times, we modified the material through nitrogen doping and decoration with silver nanoparticles. The resulting nitrogen-doped MoS₂ (N-MoS₂) and silver-decorated nitrogen-doped MoS₂ (Ag-N-MoS₂) sensors exhibited substantial improvements in NO₂ sensing performance. Notably, the Ag-N-MoS₂ sensor demonstrated nearly double the response of pristine MoS₂ at 100°C, illustrating the synergistic effects of nitrogen doping and silver decoration. Finally, we investigated the application of MoS₂ in optoelectronic systems through the development of a self-powered broadband photodetector utilizing a van der Waals heterostructure composed of MoS₂ and WS₂. The fabricated eterostructures exhibited enhanced responsivity, detectivity, and response times ompared to pristine MoS₂, showcasing the potential of these materials in next-generation optoelectronic applications. Furthermore, the anticipated challenges and future perspectives in the rapidly evolving research on SnS2 and MoS2 for sensing applications are similarly exploredItem Advanced Security Solutions for Large-Scale and Highly Dynamic Internet of Vehicles (IoV)(Indian Institute of Technology, Jodhpur, 2025-05-13) Das, DebasisAbstract The Internet of Vehicles (IoV) is a large-scale, highly dynamic network of heterogeneous communications.Maintaining a balance between authentication and privacy is crucial in such types of networks. Moreover, ensuring a high level of security through heterogeneous communication can result in significant computational overhead due to the numerous cryptographic operations required, which have higher execution times. Such trade-off between security and efficiency, are impractical in IoV networks, where the IoV devices have limited processing power and storage capacity. Furthermore, diverse communications generate substantial data that requires secure storage. Therefore, it is essential to minimize data storage and retrieval times to facilitate real-time communication in rapidly changing resource-constrained IoV network. These problems can be solved using lightweight security solutions that make sure users are authenticated without compromising their privacy. These solutions can also improve the trade-off between security and performance and make it easier to store and retrieve the large amounts of data that are created by different types of communication in the IoV network. The research is conducted in two phases. The first phase proposes advanced security solutions for the aforementioned issues using Tamper-Proof Devices (TPD). The TPD, a type of Hardware Security Module (HSM), stores cryptographic keys along with secret parameters and performs cryptographic operations such as authentication, encryption, decryption, and so on within the device. Initially, a lightweight conditionalprivacy preservation-based authentication mechanism is proposed, enhancing security through innovative key management mechanisms, such as Hard Key Update (HKU) and Soft Key Update (SKU). These mechanisms safeguard against key disclosure and ensure continuous protection even in the case of key compromise. The proposed solution significantly reduces computational and communication overhead using cryptographic techniques such as Message Authentication Codes (MACs), hash functions, and XOR operations. The research is further extended to mitigate the security-performance trade-off by implementing the idea of static and dynamic batch verification of multiple messages, leveraging lightweight cryptographic operations. The proposed method reduces computational overhead and ensures data integrity and confidentiality across the network. Additionally, it provides a solution for efficient and secure storage-retrieval of information while enhancing the responsiveness and efficiency of IoV services having dynamic and largescale environment. The proposed approach reduces latency and prevents bottleneck issues in a centralized system by integrating edge computing with the Vehicular Cloud (V-Cloud). Despite their robust design, TPDs can still be vulnerable to advanced physical attacks because of potential hardware flaws or inadequate protection against complex tampering techniques. Attackers can exploit these weaknesses to bypass security measures, potentially compromising the integrity of the device. This issue is resolved by introducing alternative approaches using Physical Unclonable Functions (PUF) devices. PUFs use hardware’s inherent physical irregularities to generate cryptographic keys on demand, making them nearly impossible to clone and highly resistant to tampering, unlike TPDs, which require secure key storage. In the second phase, PUF devices are used as secure hardware modules to design a highly secure and lightweight authentication scheme using a Challenge-Response Pair (CRP) mechanism. Furthermore, the research provides an effective solution for enhancing security on resource-constrained devices, where efficient and secure data handling is crucial. The framework uses PUFs to generate dynamic nonce and keys to address nonce reuse issue, without imposing significant computational or energy demands. Additionally, implementing this framework on low-power hardware platforms demonstrates its suitability for systems that require high security with efficient power usage, making it an ideal choice for securing resource constrained IoV devices. Comprehensive security analysis using BAN logic, Random OracleModel (ROM), and the ProVerif tool validates the technical depth of the proposed solutions, making them highly effective in countering a wide variety of security threats prevalent in IoV scenarios. The proposed methods are also evaluated against existing techniques, demonstrating improvements in terms of computational overhead, communication overhead, energy consumption, latency, and scalability.Item Non-Self-adjoint Eigenvalue Problem for Optical Bent Waveguides(2025-05-15) Hiremath, Kirankumar R.Optics has emerged as a significant contributor to technological advancements, especially in optical communication and optical signal processing. Nowadays, with optical waveguides, it is possible to have all the facilities for communication and transmitting high-speed data. Due to its importance, researchers worldwide emphasize exploring optical waveguides and their different aspects, including design, properties, applications, and integration with other technologies. The most prominent waveguides are straight waveguides and bent waveguides. These waveguides are well-studied experimentally, numerically, and semi-analytically. The mathematical aspects of the optical straight waveguides are explored very well. In this thesis, we presented the mathematical aspects of the dielectric optical bent waveguides. The analysis was done by constructing an eigenvalue problem governing the bent waveguide model using Maxwell’s equations in the appropriate space setting. We restrict our-self to the 1 − D straight and bent waveguide model. The main difficulty in studying the mathematical aspects of the bent waveguide model was the non-self-adjointness of the bent waveguide problem. Unlike Sturm-Liouville’s theory for self-adjoint eigenvalue problems, there is no general theory for non-self-adjoint eigenvalue problems. It makes extracting information directly about the model associated with these problems challenging without conducting a separate analysis. In the Chap.1, we discussed the basics to understand how the optical waves propagate in the planner straight and bent waveguides. The rigorous mathematical model of the straight and bent slab waveguides with constant step-index profiles is discussed. The main objective of the model set-up is to understand the optical wave propagation analytically in the 1−D model. The literature survey for the optical waveguides is also a part of this chapter, where one can find the work based on experimental, numerical, and semi-analytic studies. For the mathematical study of the bent waveguide model, in Chap.2 we discussed some primary results on the spectral theory of self-adjoint and non-self-adjoint operators. We conducted a literature survey focused on the spectral theory of various non-self-adjoint operators. This survey aimed to provide insight into methodologies for analyzing different non-self-adjoint problems. The analytic study of the straight waveguides showed that the corresponding eigenvalues problem is self-adjoint. It has real eigenvalues. Also, the eigenfunctions corresponding to distinct eigenvalues are orthogonal. The analytical study of the bent waveguide was still missing due to the non-self-adjointness of the corresponding eigenvalue problem. Our study has addressed this gap, a part of Chap. 3. The analytic work presented in this chapter showed the non-self-adjointness of an eigenvalue problem based on an operator theoretic setting. Here, the non-self-adjointness of the bent waveguides problem is discussed by finding the adjoint operator of the problem. The non-self-adjoint problem has non-real eigenvalues, which indicate the lossy nature of the bent waveguide modes. This problem contains a bent radius parameter. The other studies show that when this parameter is large,this problem transforms into a straight waveguide problem. In terms of the underlying mathematics, we proved this using a transformation, showing that a non-self-adjoint problem transforms into a self-adjoint problem. Moreover, the non-real eigenvalues of the bent waveguide problem change into real eigenvalues of the corresponding equivalent straight waveguide problem. An explicit relation between the real and imaginary parts of the non-real-valued propagation constants is constructed on a detailed analysis. Based on this relation in Chap. 4, the boundedness of both real and imaginary parts of the propagation constants is proved, meaning they are confined within certain region in the complex plane. Furthermore, a self-adjoint problem has vorthogonal eigenfunctions corresponding to distinct eigenvalues. For a fixed bent radius, a 1 − D semi-analytic study shows that the bent waveguide eigenvalue problem has orthogonal eigenfunctions corresponding to distinct eigenvalues. To prove this analytically, we use the adjoint operator and show the orthogonality behavior of the eigenfunctions. For the bent waveguides, the asymptotic behavior of the eigenfunctions (i.e., bent modes) dictates the distribution of electromagnetic energy in the radial directions. In this work, we showed mathematically that the asymptotic behavior of the eigenfunctions is proportional to √1 r . This information helps to define the appropriate function space and the subsequent mathematical analysis of the wave propagation.Still, several mathematical questions about the bent waveguide model demand further investigation. e.g., the stability of the model for perturbations in the system parameters, the nature of its pseudospectra, etc. The compactness of the operator for the bent waveguide eigenvalue problem needs to be explored to get more insights into the model. Moreover, one can extend this work to the future 2−D set-up of the bent waveguidesItem Segmentation algorithms for automatic concealed object detection using Terahertz imaging(Indian Institute of Technology, Jodhpur, 2025-05-16) Bhatnagar, GauravThe safety and security of individuals and property is an indispensable issue for public venues, transportation hubs, and sensitive facilities. The increasing threat of concealed weapons and other valuable objects under human clothing, calls for sophisticated and reliable security screening technologies. Traditional methods like metal detectors and X-ray scanners face limitations in detecting non-metallic objects and can often be intrusive raising privacy concerns. Moreover, the growing diversity and ingenuity of concealed objects require advanced imaging systems that can address these challenges while ensuring public safety. This thesis presents novel approaches leveraging passive Terahertz (THz) imaging. It is a non-ionizing, privacy-preserving, covert, and contactless way of scanning humans for visualizing potential concealed objects. When applied to these images, image processing and computer vision algorithms give an automatic solution to the problem. They can provide a completely automatic solution to detect, localize, and recognize these objects in real time. This technology is in its infancy and has huge potential in the near future. This thesis attempts to solve the problem by taking a segmentation based approach to the problem. Unlike the visible spectrum which also captures an object’s color and inner texture, the object visualization in this spectrum captures only the shape, size, and morphology of the objects. Thus, pixel-level localization would be more appropriate. The goal of this thesis is to review and analyze various image processing and computer vision based algorithms suggested in the literature to perform automatic concealed object detection under human clothing for security check and entry control applications using passive Terahertz imaging, with a particular focus on investigating and analyzing image segmentation based approaches for the same. The most prominent methods in the literature are broadly classified into class-independent binary segmentation/foreground extraction methods and class-dependent object detection methods. The former methods give a pixel-level binary mask leading to the detection and localization of concealed objects in a class-independent manner. They thus can be used for all kinds of known and unknown object classes. On the other hand, the latter is more concerned with the recognition of specific classes apart from just detection and localization and mostly uses data-driven deep learning concepts to give optimal performance. The research undertaken is broadly divided into three parts: The first part (Chapter 2 and 3) aims to study all the class-independent binary segmentation methods for the effective detection and pixel-level localization of concealed objects on an extensive dataset, to propose and analyze novel segmentation based algorithms for the same. The method in Chapter 2 utilizes particular image properties of the dataset, principles of focus of attention, and superpixel segmentation to give a generic, image processing method, free of any kind of learning for the purpose. Additionally, robustness to noise is also inherently considered in the study. Thereafter, to make the approach robust to varying imaging conditions, the principles of machine learning are leveraged in Chapter 3 to suggest a more general solution to the problem. It is worth mentioning that the particular insights developed in Chapter 2 help us engineer robust features for this learning-based method in Chapter 3. The second part (Chapter 4) of the thesis takes an application-specific take on class-dependent object detection. For this, a novel blob-detection-based approach based on hierarchical segmentation has been proposed to give a region proposal technique, a starting point for many object detection algorithms. The proposed technique is a real-time technique, free of any kind of learning and utilizes specific image properties for the same. Finally, the third part (Chapter 5) of the thesis is more concerned with measuring the accuracy of superpixel segmentation used in the first part of the research. Apart from a general literature review, a novel, efficient, and mathematically rich information theoretic measure for the same has been proposed and analyzed. It can be used to evaluate the refinement accuracy of the superpixel segmentation and choose optimal parameters. In conclusion, a comprehensive study of segmentation and hierarchical segmentation based automatic, and accurate yet resource efficient methods for the application has been done. Additionally, all the novel segmentation algorithms proposed in this thesis are implemented, and empirically evaluated on a dataset in this thesis.Item Non-Self-adjoint Eigenvalue Problem for Optical Bent Waveguides(Indian Institute of Technology Jodhpur, 2024-07-25) Hiremath, Kirankumar R.Optics has emerged as a significant contributor to technological advancements, especially in optical communication and optical signal processing. Nowadays, with optical waveguides, it is possible to have all the facilities for communication and transmitting high-speed data. Due to its importance, researchers worldwide emphasize exploring optical waveguides and their different aspects, including design, properties, applications, and integration with other technologies. The most prominent waveguides are straight waveguides and bent waveguides. These waveguides are well-studied experimentally, numerically, and semi-analytically. The mathematical aspects of the optical straight waveguides are explored very well. In this thesis, we presented the mathematical aspects of the dielectric optical bent waveguides. The analysis was done by constructing an eigenvalue problem governing the bent waveguide model using Maxwell’s equations in the appropriate space setting. We restrict our-self to the 1 − D straight and bent waveguide model. The main difficulty in studying the mathematical aspects of the bent waveguide model was the non-self-adjointness of the bent waveguide problem. Unlike Sturm-Liouville’s theory for self-adjoint eigenvalue problems, there is no general theory for non-self-adjoint eigenvalue problems. It makes extracting information directly about the model associated with these problems challenging without conducting a separate analysis. In the Chap.1, we discussed the basics to understand how the optical waves propagate in the planner straight and bent waveguides. The rigorous mathematical model of the straight and bent slab waveguides with constant step-index profiles is discussed. The main objective of the model set-up is to understand the optical wave propagation analytically in the 1−D model. The literature survey for the optical waveguides is also a part of this chapter, where one can find the work based on experimental, numerical, and semi-analytic studies. For the mathematical study of the bent waveguide model, in Chap.2 we discussed some primary results on the spectral theory of self-adjoint and non-self-adjoint operators. We conducted a literature survey focused on the spectral theory of various non-self-adjoint operators. This survey aimed to provide insight into methodologies for analyzing different non-self-adjoint problems. The analytic study of the straight waveguides showed that the corresponding eigenvalues problem is self-adjoint. It has real eigenvalues. Also, the eigenfunctions corresponding to distinct eigenvalues are orthogonal. The analytical study of the bent waveguide was still missing due to the non-self-adjointness of the corresponding eigenvalue problem. Our study has addressed this gap, a part of Chap. 3. The analytic work presented in this chapter showed the non-self-adjointness of an eigenvalue problem based on an operator theoretic setting. Here, the non-self-adjointness of the bent waveguides problem is discussed by finding the adjoint operator of the problem. The non-self-adjoint problem has non-real eigenvalues, which indicate the lossy nature of the bent waveguide modes. This problem contains a bent radius parameter. The other studies show that when this parameter is large,this problem transforms into a straight waveguide problem. In terms of the underlying mathematics, we proved this using a transformation, showing that a non-self-adjoint problem transforms into a self-adjoint problem. Moreover, the non-real eigenvalues of the bent waveguide problem change into real eigenvalues of the corresponding equivalent straight waveguide problem. An explicit relation between the real and imaginary parts of the non-real-valued propagation constants is constructed on a detailed analysis. Based on this relation in Chap. 4, the boundedness of both real and imaginary parts of the propagation constants is proved, meaning they are confined within certain region in the complex plane. Furthermore, a self-adjoint problem has vorthogonal eigenfunctions corresponding to distinct eigenvalues. For a fixed bent radius, a 1 − D semi-analytic study shows that the bent waveguide eigenvalue problem has orthogonal eigenfunctions corresponding to distinct eigenvalues. To prove this analytically, we use the adjoint operator and show the orthogonality behavior of the eigenfunctions. For the bent waveguides, the asymptotic behavior of the eigenfunctions (i.e., bent modes) dictates the distribution of electromagnetic energy in the radial directions. In this work, we showed mathematically that the asymptotic behavior of the eigenfunctions is proportional to √1 r . This information helps to define the appropriate function space and the subsequent mathematical analysis of the wave propagation.Still, several mathematical questions about the bent waveguide model demand further investigation. e.g., the stability of the model for perturbations in the system parameters, the nature of its pseudospectra, etc. The compactness of the operator for the bent waveguide eigenvalue problem needs to be explored to get more insights into the model. Moreover, one can extend this work to the future 2−D set-up of the bent waveguidesItem Formazan-Based Metal Complexes for Applications in Resistive Switching and Electrocatalysis(Indian institute of Technology Jodhpur, 2025-04-22) Metre, Ramesh K.Drawing inspiration from nature, the concept of redox-active ligands explores the catalytic potential of earth-abundant metals like iron and copper despite their preference for one-electron redox reactions. Unlike traditional spectator ligands, redox-active ligands offer energetically accessible levels for reduction or oxidation, enabling oxidation state changes to occur either exclusively at the ligand or synergistically at both the ligand and metal. The incorporation of redox-active ligands presents numerous benefits, such as facilitating continuous multielectron transfer, regulating electron-deficient transition states, and enabling single-electron changes in the redox state of the metal center. In this context, we have meticulously adapted redox-active formazan-based ligands to explore a broad spectrum of applications, encompassing resistive switching memory, antibacterial properties, and electrocatalysis. Formazans boast a rich history in chemistry, where deprotonation generates monoanionic chelating N-donor ligands called "formazanates." The synthesis of formazan ligands provides a straightforward route to formazanate-based complexes with tunable electronic and steric characteristics. Complexes featuring a formazanate ligand typically exhibit a narrow HOMO–LUMO gap, resulting in energetically accessible π*-orbitals. This property renders formazanate complexes redox-active, enabling reversible storage of electrons in a ligand-based manner within these compounds. The utilization of formazanate-based ligands in coordination with base metal ions has initiated a novel research domain, expanding the range of applications in the design of magnetic, optical, and intriguing electrical materials. This is achieved by leveraging the distinctive optoelectronic properties and adaptable electrochemistry offered by formazanate ligands. The primary focus of this thesis lies in the synthesis of formazanate-based ligands, their corresponding metal complexes, and their diverse applications, ranging from resistive switching memory to antibacterial activity and electrocatalysis. An ionic organotin complex was prepared using a 2-pyridine substituted formazan ligand and investigated for its application in solid-state non-volatile write once read many (WORM) memory devices, demonstrating a high switching on-off ratio. Furthermore, distorted octahedral bis(formazanate) zinc complexes were used in the fabrication of a memory device that intriguingly exhibited non-volatile resistive switching memory behavior. The bis(formazanate) iron complexes displayed extensive redox behavior during the cathodic sweep in cyclic voltammetry analysis and were realized as electroactive cathode materials for the electrocatalytic H2O2 reduction in one-compartment H2O2 fuel cell. Later, a mono(formazanate) mercury complex was prepared and was further studied as an antimicrobial agent.Item Computer-aided Diagnostic System for Hypertensive and Diabetic Retinopathy(Indian Institute of Technology Jodhpur, 2025-04-21) Tiwari, Anil KumarRetinal diseases such as Diabetic Retinopathy (DR) and Hypertensive Retinopathy (HR) are serious health concerns. HR is a retinal disease caused by elevated blood pressure for a prolonged period. Diabetic Retinopathy(DR)is a progressive retinal disease caused by long-term diabetes. Non-proliferative diabetic retinopathy(NPDR) is an early stage of DR, damages blood vessels of the retina. Moreover, HR serves as a biomarker for several illnesses, including retinal diseases, atherosclerosis, strokes, kidney disease, and cardiovascular risks. Early identification of these diseases helps in the timely and proper treatment can prevent blindness. Manual diagnosis of these retinal diseases is time-consuming, resource demanding, inconvenient, costly, and demands specialized skills and experience. On the other hand, Computer-aided diagnosis (CAD) and Artificial intelligence (AI) based systems are expected to solve the above-mentioned challenges effectively. This thesis proposes an efficient algorithm for HR detection. Further, for effective management of these diseases, we develop an effective algorithm for severity grading of HR and NPDR.To achieve this, a careful study of existing literature in the field of automatic diagnosis of HR and DR is conducted to identify important research gaps in this area. It has been observed that the availability of public data sets for HR diagnosis is limited. Moreover, there is no such public dataset available for HR grading. Severity grading helps in the timely and effective management of these diseases and reduces future risks. However, very limited research has been conducted for the severity grading of HR and NPDR. Additionally, previous studies mainly relied on Arteriovenous Ratio (AVR) and the manual selection of regions of interest (ROI) around the optic disc (OD) for HR detection and its grading. Due to ROI selection primarily around OD, these method had risk of missing several important clinical features present elsewhere, resulting in less accurate diagnostic outcomes. Furthermore, one of the key challenges observed in DR and HR severity grading is the high-class imbalance high-class imbalance. Such class imbalance makes the training of learning based recognition models very challenging, as the majority of class samples predominate in the training process of the learning model. To address these limitations, a novel HR detection approach has been proposed, based on few-shot learning, using a pretrained initial baseline model to obtain transferable knowledge for feature embedding on few-shot prediction. This approach aims to reduce overfitting and improve generalization, which is especially advantageous for smaller datasets. Unlike previous methods, the proposed approach uses complete images to capture all clinical features. Experimental results demonstrate the effectiveness of the proposed HR detection method. In this thesis work, we develop “HRSG: Expert-Annotated Hypertensive Retinopathy Severity Grading Dataset” dataset, encompassing fundus images, and categorizing the severity into four classes: normal, mild, moderate, and severe. The grading process is conducted by three experienced ophthalmologists affiliated with prestigious medical institutions in India, including the All India Institute of Medical Sciences (AIIMS) in Jodhpur, the Aravind Eye Hospital in Madurai, and the Sri Aurobindo Institute of Medical Sciences Hospital in Indore. For effective severity grading of HR, a hybrid deep learning (DL) architecture is proposed that leverages the combined strengths of pretrained ResNet-50 and a modified Vision Transformer (ViT) enhanced with both global and locality self-attention mechanisms, enabling accurate grading into four classes: normal, mild, moderate, and severe. The proposed method effectively captures both local and global contextual information across the input image, leading to a robust and resilient classification model. Further, to address the class imbalance issue, we introduce a novel decouple representation and classifier (DRC) based training method. The proposed DRC method effectively addresses the class imbalance by improving the module’s capacity to identify effective feature learning while maintaining the original dataset’s distributional properties, leading to improved diagnostic accuracy. The extensive experimental results demonstrate the effectiveness of the proposed method in accurately grading HR severity. This thesis also presents a reliable method for NPDR severity grading into normal, mild, moderate, and severe classes. This method includes an initial image enhancement to improve quality for subsequent processing. A feature set is then developed using various descriptors, capturing rich information to identify distinct and unique properties of NPDR lesions. To address the class imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and an ensemble-learning-based Random Forest (RF) classifier to improve the model’s performance on the imbalance classes. Moreover, the comparison results show that the proposed method performs better than existing methods, making it suitable for the early diagnosis and effective management of NPDR. The contributions of this thesis aim to assist healthcare professionals in early HR detection, regular screening, risk stratification, and patient categorization based on HR and NPDR severity grading. These developments aim to optimize clinical decision-making, improve resource allocation, and enhance overall disease management. The proposed systems can assist clinicians in referral decisions and facilitate mass screening.Publication Computer-aided Dignostic System for Hypertensive and Diabetic Retinopathy(Indian Institute of Technology Jodhpur, 2025-04-21) Tiwari, Anil KumarRetinal diseases such as Diabetic Retinopathy (DR) and Hypertensive Retinopathy (HR) are serious health concerns. HR is a retinal disease caused by elevated blood pressure for a prolonged period. Diabetic etinopathy(DR)is a progressive retinal disease caused by long-term diabetes. Non-proliferative diabetic retinopathy(NPDR) is an early stage of DR, damages blood vessels of the retina. Moreover, HR serves as a biomarker for several illnesses, including retinal diseases, atherosclerosis, strokes, kidney disease, and cardiovascular risks. Early identificahese diseases helps in the timely and proper treatment can prevent blindness. Manual diagnosis of these retinal diseases is time-consuming, resource demanding, inconvenient, costly, and demands pecialized skills and experience. On the other hand, Computer-aided iagnosis (CAD) and Artificial intelligence (AI) based systems are expected to solve the above-mentioned challenges effectively. This thesis proposes an efficient algorithm for HR detection. Further, for effective management of these diseases, we develop an effective algorithm for severity grading of HR and NPDR. To achieve this, a careful study of existing literature in the field of automatic diagnosis of HR and DR is conducted to identify important research gaps in this area. It has been observed that the availability of public data sets for HR diagnosis is limited. Moreover, there is no such public dataset available for HR grading. Severity grading helps in the timely and effective anagement of these diseases and reduces future risks. However, very limited research has been conducted for the severity grading of HR and NPDR. Additionally, previous studies mainly relied on Arteriovenous Ratio (AVR) and the manual selection of regions of interest (ROI) around the optic disc (OD) for HR detection and its grading. Due to ROI selection primarily around OD, these method had risk of missing several important clinical features present elsewhere, resulting in less accurate diagnostic outcomes. Furthermore, one of the key challenges observed in DR and HR severity grading is the high-class imbalance high-class imbalance. Such class imbalance makes the training of learning based recognition models very challenging, as the majority of class samples predominate in the training process of the learning model. To address these limitations, a novel HR detection approach has been proposed, based on few-shot learning, using a pretrained initial baseline model to obtain ransferable knowledge for feature embedding on few-shot prediction. This approach aims to reduce overfitting and improve generalization, which is especially advantageous for smaller datasets. Unlike previous methods, the proposed approach uses complete images to capture all clinical features. Experimental results demonstrate the effectiveness of the proposed HR detection method. In this thesis work, we develop “HRSG: Expert-Annotated Hypertensive Retinopathy Severity Grading Dataset” dataset, encompassing fundus images, and categorizing the severity into four classes: normal, mild, moderate, and severe. The grading process is conducted by three experienced ophthalmologists affiliated with prestigious medical institutions in India, including the All India Institute of Medical Sciences (AIIMS) in Jodhpur, the Aravind Eye Hospital in Madurai, and the Sri Aurobindo Institute of Medical Sciences Hospital in Indore. For effective severity grading of HR, a hybrid deep learning (DL) architecture is proposed that leverages the combined strengths of pretrained ResNet-50 and a modified Vision Transformer (ViT) enhanced with both global and locality self-attention mechanisms, enabling accurate grading into four classes: normal, mild, moderate, and severe. The proposed method effectively captures both local and global contextual information across the input image, leading to a robust and resilient classification model. Further, to address the class imbalance issue, we introduce a novel decouple representation and classifier (DRC) based training method. The proposed DRC method effectively addresses the class imbalance by improving the module’s capacity to identify effective feature learning while maintaining the original dataset’s distributional properties, leading to improved diagnostic accuracy. The extensive experimental results demonstrate the effectiveness of the proposed method in accurately grading HR severity. This thesis also presents a reliable method for NPDR severity grading into normal, mild, moderate, and severe classes. This method includes an initial image enhancement to improve quality for subsequent processing. A feature set is then developed using various descriptors, capturing rich information to identify distinct and unique properties of NPDR lesions. To address the class imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and an ensemble-learning-based Random Forest (RF) classifier to improve the model’s performance on the imbalance classes. Moreover, the comparison results show that the proposed method performs better than existing methods, making it suitable for the early diagnosis and effective management of NPDR. The contributions of this thesis aim to assist healthcare professionals in early HR detection, regular screening, risk stratification, and patient categorization based on HR and NPDR severity grading. These developments aim to optimize clinical decision-making, improve resource allocation, and enhance overall disease management. The proposed systems can assist clinicians in referral decisions and facilitate mass screening.Item Heat Transfer Analysis on the Expedition of Temperature Distribution and Bubble Behavior from Nucleation to Critical Heat Flux during Pool Boiling(Indian Institute of Tehcnology, Jodhpur, 03-07-2023) Kothadia, Hardik B.The phase change heat transfer processes are widely implemented for heat extraction as they utilise both sensible and latent heat (Rohsenow and Griffith, 1955). The capability to remove the higher magnitude of heat at low wall superheats, and the lack of moving parts makes pool boiling appealing (Memmott and Manera, 2011). Pool boiling is economical, simple, and prevalent among all available cooling schemes. The utilization of the aforementioned technique is widely implemented for thermal management in nuclear industries and in microelectronic devices. Conventional air cooling systems cannot handle these devices cooling requirements which may be due to their low heat transfer performance. In such instances, pool boiling, and droplet evaporation techniques can be implemented. Nowadays, the nuclear industries, renewable energy sectors, and power plants are implementing compact heat exchangers as preheaters, regenerators, and intermediate heat exchangers (Pattanayak et al., 2022) (Pattanayak and Kothadia, 2020). These compact heat exchangers are basically of tubular or plate-type. Therefore it gives the urge to study the heat transfer characteristics of those heat exchangers and analyse the methodologies that can enhance the heat transfer from the surface of tubes and plates (Pattanayak et al., 2022). The lack of qualitative theories, quantitative data, and explanations in the area of critical heat flux (CHF) in tube and plate makes it an interesting domain of research. There is limited research explaining the effect of high, substrate and liquid temperatures, on droplet evaporation. There is a scarcity of research in analysing the heat transfer coefficient during the evaporation process (Pattanayak et al., 2021) (Pattanayak and Kothadia, 2022). The research highlights the critical heat flux (CHF) studies on mini-channels, micro-channels, and plates during pool boiling under uniform heat flux conditions. Identification of the tube and plate length and diameter beyond which CHF becomes independent of the dimensions is discussed. The effect of tube and plate orientations and pool subcooling on CHF has been analysed. Different regimes of pool boing under uniform heat flux conditions are discussed based on bubble behavior. The instantaneous heat transfer coefficient during droplet evaporation is analysed. The CHF data are used to derive an empirical correlation that includes the impact of subcooling, orientation, and dimensions (Pattanayak et al., 2023). In the case of the analysis of compact heat exchangers, SS 304 tubes and plates are used. The orientation is changed from 0ᵒ to 90ᵒ for tubes and 0ᵒ to 180ᵒ for plates. The length and diameter of tube is varied from 50 mm to 1000 mm and 1.2 mm to 9 mm, respectively. The water pool is kept at 30°C, 50°C, 75°C, and saturation temperature. The length of the plate is varied from 50 mm to 300 mm. The width of the plate ranges from 10 mm to 20 mm. The pool is maintained at 25℃ and saturation temperature corresponding to ambient pressure. It has been noted that the severity of CHF lessens as pool temperature rises. For a particular pool temperature, the shortest length has a higher magnitude of CHF. As tube diameter and width expand, CHF values decrease. In the case of tubes, the CHF value is larger for horizontal orientation than vertical orientation.In the case of the analysis of compact heat exchangers, SS 304 tubes and plates are used. The orientation is changed from 0ᵒ to 90ᵒ for tubes and 0ᵒ to 180ᵒ for plates. The length and diameter of tube is varied from 50 mm to 1000 mm and 1.2 mm to 9 mm, respectively. The water pool is kept at 30°C, 50°C, 75°C, and saturation temperature. The length of the plate is varied from 50 mm to 300 mm. The width of the plate ranges from 10 mm to 20 mm. The pool is maintained at 25℃ and saturation temperature corresponding to ambient pressure. It has been noted that the severity of CHF lessens as pool temperature rises. For a particular pool temperature, the shortest length has a higher magnitude of CHF. As tube diameter and width expand, CHF values decrease. In the case of tubes, the CHF value is larger for horizontal orientation than vertical orientation.In the case of the analysis of compact heat exchangers, SS 304 tubes and plates are used. The orientation is changed from 0ᵒ to 90ᵒ for tubes and 0ᵒ to 180ᵒ for plates. The length and diameter of tube is varied from 50 mm to 1000 mm and 1.2 mm to 9 mm, respectively. The water pool is kept at 30°C, 50°C, 75°C, and saturation temperature. The length of the plate is varied from 50 mm to 300 mm. The width of the plate ranges from 10 mm to 20 mm. The pool is maintained at 25℃ and saturation temperature corresponding to ambient pressure. It has been noted that the severity of CHF lessens as pool temperature rises. For a particular pool temperature, the shortest length has a higher magnitude of CHF. As tube diameter and width expand, CHF values decrease. In the case of tubes, the CHF value is larger for horizontal orientation than vertical orientation.The study demonstrates that for horizontally oriented tubes, CHF fluctuation is negligible beyond a length of 500mm, regardless of diameter. According to the study performed for vertical channels, CHF fluctuation is negligible for tubes with a diameter more than 2.5 mm beyond a length of 200 mm. The vertical orientation of the plates results in a higher CHF magnitude as compared to the horizontal upward and downward orientations respectively (Pattanayak and Kothadia, 2020), (Pattanayak et al., 2021), (Pattanayak et al., 2023). The hydrophobic surface of copper electrodeposited tubes exhibits a lesser CHF magnitude than the uncoated surface and is efficient for phase change heat transfer applications in lower heat flux regimes. Furthermore, the analysis of heat transfer during droplet evaporation is conducted to study the effect of surface and liquid temperature on the instantaneous heat transfer coefficient. It is observed that the evaporation rate is higher for copper than aluminum. The instantaneous heat transfer coefficient increases with the temperature of droplet evaporating on a given substrate and is higher for copper. When substrate temperature increases for a given droplet temperature, the instantaneous heat transfer coefficient increases (Pattanayak and Kothadia, 2021) (Pattanayak et al., 2022). The regimes from natural convection to CHF limit in a subcooled pool of water maintained under uniform heat flux conditions are studied for SS 304 upward-facing plates of different dimensions (Clifton and Chapman, 1969) (Pattanayak et al., 2022). During the heat transfer process, the temperature distribution along the plate is examined. The Nusselt number is seen to be independent of aspect ratio (Pattanayak et al., 2022) (Pattanayak and Kothadia, 2022). The Nusselt number rises when the plate length and width are independently increased. The study is also carried out in saline water of solutions with varying salinity from 0%, 0.2%, 0.5%, and 2%, and is observed that beyond salinity 0.2%, the heat transfer coefficient decreases (Pattanayak et al., 2022).