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3D Bioprinting on Electrospun Nanofibrous Scaffolds to Fabricate Myocardial Patches
No full textTissue-engineered three-dimensional (3D) myocardial patches offer superior and innovative treatment strategy for myocardial infarction (MI). Among the various scaffold fabrication techniques, electrospinning and 3D bioprinting have received great attention due to their ability to recreate native cardiac tissue mimetic characteristics. Despite several advantages, electrospun nanofibers have limitations in cell delivery due to random cell seeding, while bioprinting faces challenges in creating hydrogel constructs with ideal mechanical properties capable of supporting cardiac contraction/relaxation cycle. Hence in this work, hybrid myocardial patches were developed by converging electrospinning and bioprinting techniques.
Electrospinning of PLCL:PEOz (B73, 7:3 blend of poly-L-lactide-co-ε-caprolactone (PLCL) and polyethyl oxazoline (PEOz)) produced a hydrophilic, axially aligned and cardiac mimetic ECM having heterogeneous fiber distribution. In vitro evaluation using primary neonatal rat ventricular cardiomyocytes (NRVCMs) showed enhanced viability, contractility, and maturity indicating the compatibility of B73 nanofiber for cardiac tissue engineering applications.
Further, two different bioprinting strategies such as laser-assisted bioprinting (LAB) and extrusion bioprinting were explored by developing specific bioinks for the fabrication of cardiac patch using B73 as support matrix. LAB confirmed high-resolution printing using sodium alginate /gelatin bioink, however it was found not suitable to create 3D hydrogel constructs with high cell density. Hence, extrusion bioprinting was preferred to fabricate a hybrid cardiac patch. Two different thermoresponsive bioink compositions such as carboxymethyl cellulose/agarose/gelatin (CAG) and sodium alginate/gelatin/PEOz (AGP) were prepared and found to have excellent printability. However, CAG bioink failed to achieve high-resolution strand deposition.
Hence, AGP bioink along with dual crosslinking (100 mM CaCl2 and 2.5% (w/v)) TG (microbial transglutaminase) was optimized for hybrid cardiac patch fabrication. Bioprinted AGP cardiac constructs had improved NRVCMs viability, orientation, contractility and maturity. Hybrid cardiac patch (AGP-B73) was found to have a significant improvement in mechanical properties, along with improved shape fidelity and handleability compared to AGP constructs. The results confirm the potential of AGP-B73 cardiac patch to maintain NRVCMs viability and functional maturation. This study serves as a proof of concept in demonstrating the successful development of AGP-B73 cardiac patch for targeting MI and other drug testing studies in the future
Non-Invasive Near-Field Microwave Sensors for Industrial Internet of Things Applications
No full textoai:knowledgeconnect.sastra.edu:theses-1001The industrial sector has been revolutionized by Industrial Revolution 4.0 with the advent of the Internet of Things (IoT) which integrates the physical environment using smart sensors and Artificial Intelligence (AI) mechanisms. This demands the design of smart sensors and actuators to improve the quality of services in Industrial IoT (IIoT). The sensor design using the microwave resonating principle has attracted the research and industrial community for portable, non-invasive, non-destructive and hygienic means to evaluate the quality of substances in the food and farming industry. The microwave planar sensor has advantages in terms of low footprint, low cost, and ease of integration with the existing framework. Alternatively, the microwave sensors have design Challenges in terms of sensitivity and selectivity based on the choice of resonators, substrates and frequency of operation used for a particular application.
The research aims to develop non-invasive near-field microwave sensors for industrial Internet of Things applications with the IEEE 154 standard, focusing on enhanced sensing with real-time quality assurance in dairy, agricultural warehouses, and aquaculture industries. This study focuses on the various techniques and on the principle of operation in the design for microwave sensing, namely, Split Ring Resonator (SRR), metamaterial planar sensor, Complementary SRR (CSRR) sensor, Substrate Integrated Waveguide (SIW) sensor and microstrip ring resonators will be investigated for various substrates, materials under test and frequencies of operation. This work includes studying numerical methods for complex permittivity extraction and measurement techniques.Further, the study focuses on the quality factor analysis, sensitivity analysis, detectivity range, and possible location of a device under test.
The proposed sensors include a milk adulteration identification sensor, a grain moisture and gas detection sensor, and a formalin contamination detection sensor in fish. This noninvasive milk adulteration sensors, double heterogeneous ring resonating sensor and assimilated ring resonating sensor identifies adulteration substances like urea, water, detergent and starch, safeguarding the purity and safety of dairy products. The grain microwave sensors, namely, snowflake resonating sensor measures the moisture content and ammonia gas in stored grains to avoid spoilage. Snowflake resonating sensor with metasurface enables non-invasive detection of grain during storage and processing. Further, substrate integrated waveguide enabled identical circular patch sensor and triple oval planar resonating sensor identifies the presence of formalin content in fish, a toxic preservative often used to prolong freshness. These sensors displays a promising role in food safety, quality control, and public health
Biogenic Synthesis of Metallic and Bimetallic Oxide Nanoparticles for Water Treatment Applications
No full textAccess to clean water is a quality-of-life indicator. The availability of clean water apart from water scarcity is marred due to contamination by heavy metals and synthetic dyes, posing a grave environmental and public health challenge. This necessitates the implementation of advanced and sustainable treatment solutions towards water remediation. This research work focusses on the fabrication and application of biogenic and chemically synthesized metallic and bimetallic nanoparticles for the efficient removal of uranium, chromium, and toxic dyes from aqueous environments. The study particularly focuses on zero-valent (C-Fe and B-Fe) and bimetallic (C-NiFe and B-NiFe) nanoparticles, evaluating their adsorption capabilities and degradation efficiencies in treating contaminated water.
Conventional water treatment techniques, such as chemical precipitation, solid-phase extraction, ion exchange and membrane separation, often face limitations due to high operational costs and interference from competing ions. In response, this research investigates an innovative approach—nanoparticle-based remediation—leveraging both chemical reductions using sodium borohydride (for C-Fe and C-NiFe) and eco-friendly biogenic synthesis employing plant extracts (for B-Fe and B-NiFe).
Characterization through X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM) and Fourier Transform Infrared Spectroscopy (FTIR) confirms structural and morphological integrity of the synthesized nanoparticles. Experimental studies assess the adsorption efficiency of these nanoparticles under varying pH, contaminant concentrations, and adsorbent loadings. The results demonstrate the remarkable potential of biogenic nanoparticles, achieving 99.7% uranium removal at an initial concentration of 500 μg/L and an optimal pH of 7.0. Chromium removal efficiency reaches 95.5% at 100 mg/L, highlighting the effectiveness of both biogenic and chemically synthesized nanoparticles in mitigating heavy metal pollution.
Furthermore, the study addresses the degradation of Congo Red, a hazardous azo dye, utilizing advanced oxidation processes (AOPs). The findings reveal that biogenic nanoparticles facilitate over 90% dye removal, with a peak efficiency of 92.3% at 50 mg/L within 60 minutes. Kinetic studies further elucidate the adsorption mechanisms, showing rapid contaminant removal, with equilibrium achieved within 30 minutes for uranium and 45 minutes for chromium. The research underscores the significance of optimizing process parameters to enhance nanoparticle performance and maximize contaminant removal.
Overall, this thesis contributes to the advancement of environmentally sustainable water treatment technologies by demonstrating the superior performance of biogenic (B-Fe and B-NiFe) and chemically synthesized (C-Fe and C-NiFe) nanoparticles. The findings advocate for continued research into scalable applications of these materials, reinforcing their potential as viable alternatives to conventional water treatment methods for addressing global water pollution challenges
Fault Diagnosis and Fault Tolerant Structure for Multilevel Inverters using Machine Learning Techniques
Get PDFA paradigm shift towards electric drives in domestic and industrial sectors has significantly increased the use of multilevel inverters (MLI). MLIs are constructed using more semiconductor devices, which hinders safety and reliability. Literature states 31.2% of failures in MLIs are due to semiconductor devices. Hence, there is a need for fault detection and tolerant mechanisms to ensure the safety and reliability of MLIs.
MLIs like Cascaded H-bridge(CHB) and Packed U cell(PUC) are mostly preferred due to low harmonic distortion, which is considered in this work. The complexity associated with fault diagnosis with more components in MLIs is addressed by machine learning (ML). Various open and short circuit faults are simulated in the MLIs. About 14 features like Vrms, Mean, % THD, and harmonic orders (2-12) have been extracted from the output voltage using fast Fourier transform (FFT).
Principal Component Analysis (PCA) is used to reduce 42% of the redundant features, which relatively makes the ML model training faster and more accurate. Four different ML classifiers, namely Decision Tree(DT), k-nearest neighbour (KNN), Support Vector Machine(SVM) and Naive Bayesian ( NB) classifiers, are considered in this study. Simulation results illustrate that KNN and NB classifiers provide the highest classification accuracy of 96.32% and 97.11% for CHBMLI and PUCMLI, respectively.
Challenges associated with feature and classifier selection for fault classifier design are addressed using a novel ACO-based combined optimizer. The selection process is formulated as a combinatorial optimization problem and solved using multi-objective ACO. Among the four classifiers, KNN and NB provides 97.80% and 98.07% fault detection accuracy in CHBMLI and PUCMLI, respectively, with eight optimal features.
A fault-tolerant structure for PUCMLI is proposed to ensure operation continuity even under faulty conditions. It can provide continuous operation of the MLI with the same rated power under faulty circumstances. The failure rate of individual switches is estimated based on voltage stress and temperature.
It is observed that failure rate of switches T1 and T4 in PUCMLI is found to be higher at 0.914 failures/million hours as compared with others. Thus, the proposed structure is capable of preventing the failure of MLIs and guarantees a higher reliability. The proposed fault diagnosis approach can be extended to identify deterioration in passive components and to diagnose the multiple switch faults in MLI
Design and Modelling of Advanced Power Converter Control for Harmonics Suppression and Energy Management
No full textThe quality of electrical power plays a crucial role in both industrial and domestic sectors. This issue becomes even more critical in systems that incorporate power converter-based loads, which are commonly integrated at the grid connection point. Motor drives and grid-integrated electric vehicle (EV) charging stations belong to a category of non-linear loads that inherently introduce substantial harmonic distortion into the current drawn from AC mains. This harmonic distortion adversely affects power quality, potentially leading to overheating and malfunctions in sensitive equipment across the grid. Furthermore, the widespread adoption of solar photovoltaic (PV) technology for EV charging stations has surged due to its numerous advantages.
The key benefits of solar PV-based EV charging systems include minimal maintenance requirements and a modular panel design that enables flexible scalability to meet varying energy demands. Despite these advantages of solar PV-based EV charging systems, certain challenges must be addressed to ensure stable and high-quality power transfer. The most notable challenges include the intermittency of solar power caused by variations in weather and sunlight availability, as well as the harmonic distortion introduced by the non-linear characteristics of EV charging loads. These distortions can degrade overall power quality, affecting grid stability and operational efficiency.
Achieving distortion-free EV charging requires an advanced converter control system capable of performing multiple functions. First, the system must enable efficient AC-to-DC power conversion to supply the EV battery. This conversion needs to adhere to established grid codes to ensure proper synchronization with the grid, thus facilitating seamless energy transfer between the solar PV converter and the power distribution network. Additionally, to maintain high power quality, the interfacing converter between EV charging system and solar PV system should be designed to minimize harmonic distortion and voltage fluctuations, providing cleaner, more stable power output.
Another essential function of this interfacing converter is its ability to act as a Distribution Static Compensator (DSTATCOM) during periods of low or zero solar irradiation, such as at night or on cloudy days. By acting as a DSTATCOM, the interfacing converter can help balance reactive power, reduce total harmonic distortion, and stabilize the voltage profile in the grid. Moreover, advanced energy management capabilities are also necessary to coordinate power flow based on real-time demand, energy availability, and grid conditions.
Many researchers have proposed various adaptive filter based fundamental load current component extraction techniques for harmonic suppression and energy management. However, these methods could cause unstable operation due to their large steady-state errors and delayed responses in the extracted fundamental load current component. In this research work, robust cost function based adaptive filtering techniques Lorentzian norm, conjugate gradient, M-estimate function, Logarithmic hyperbolic cosine function and variable regularization factor based filters have been implemented for the switching control of DSTATCOM in distribution grids and microgrids.
Furthermore, to enhance energy management in islanded solar-powered EV charging stations, a load profile-based superconducting magnetic energy storage (SMES) converter control technique has been introduced. This technique ensures effective utilization of battery and SMES energies according to the fluctuating demand of EV charging stations. This technique also addresses energy availability challenges that arise from solar intermittency. The incorporation of SMES technology in offgrid EV charging stations ensures that excess energy generated during peak solar hours is stored and released as required, maintaining a stable power supply and reducing dependence on the battery energy storage system (BESS)
Design and Investigation of Triarylamine Functionalized Luminophores for Multi-Functional and Stimuli-Responsive Applications
No full textOrganic solid-state fluorescent materials have garnered attention over many decades in virtue of their contribution in the development of LED devices and biomedical applications. Nevertheless, the potential of fluorescent molecules in device applications was underused due to aggregation-caused quenching (ACQ) effect. The ACQ effect commonly quenches the organic molecules fluorescence intensity in the aggregated or condensed state, which can significantly influence their performance in practical applications.
The organic polyaromatic fluorophores exhibit strong fluorescence in the solution state. After culmination of years of work, Prof. Tang’s research group discovered a new phenomenon of aggregation-induced emission (AIE) in the fluorescence molecules constructed using the twisted propeller units. Converse to ACQ, the aggregation light-up or enhanced the emission in the condensed or aggregated state, which are generally non-emissive/weakly emissive in solution state.
The development of conformationally flexible triarylamine based donor-π-acceptor (D-π-A) motif with excellent photophysical and optical characteristics is of great demand owing to their practical utility in optoelectronics, organic field effect transistor, chemo-sensors and displayes. Stimuli-responsive material exhibits fluorescence switching and tuning in response to external stimuli such as pressure, temperature, light, pH and vapours thereby influencing molecular packing and weak intermolecular interactions. The conformationally twisted molecules displaying strong solid-state fluorescence along xx with stimuli responsive fluorescence switching behaviour are suitable for the development of molecular switches and sensors. Herein, we have developed carbazole integrated multistimuli responsive materials with subtle structural variation and explored the impact of structural variation on the fluorescence properties and stimuli-responsive fluorescence switching.
To circumvent ACQ and hydrophobicity issues, the charged AIEgens was inherited and benefitted better optical, forensic and biological applications. We have developed triarylamines based ionic fluorophores with distinct noteworthy tunable emission while susceptible to external stimuli. The ionic fluorophores by quarternizing pyridine nitrogen of varied chain length was synthesized in order to study the influence of molecular assembly in solid state emission. The hydrophobic longer alkyl chain was successfully utilized for lighting up latent finger prints with level 3 information on porous and non- porous surfaces
Design and Investigation of Deep Eutectic Solvent-Assisted Extractive Fermentation of Biomolecules
No full textThe increasing demand for sustainable and efficient bioprocessing techniques has prompted the development of novel separation strategies integrating bioproduction with downstream processing. Extractive fermentation has emerged as a promising process intensification to overcome product inhibition and enhance productivity by constantly removing target biomolecules from the fermentation medium. Although the extractive fermentation of acids and alcohols has been extensively studied, less attention has been focused on green solvents and their compatibility, phase-forming abilities, and reactor configurations.
Therefore, advancing extractive fermentation technology necessitates comprehensive studies on solvent systems and the development of modified bioreactor configurations to aid future optimization. Initially, 22 DESs were prepared under four sub-classes (SDES, GDES, PNDES, and PDES), and their physical properties were characterized as a function of temperature, followed by the screening of DESs for the extractive fermentation process based on their partition coefficient and phase ratio.
Among the four groups, GDES was observed to perform better, exhibiting a density (1.005 to 1.071 g/cm3), viscosity (0.004 to 0.483 kg/m/s), and excess molar volume (-20.108 to - 46.926 cm3/mol). Additionally, GDES achieved a partition coefficient of 3.6 and a phase ratio of 3.9. The process feasibility was demonstrated using fibrinolytic protease (FLP) as a model bioproduct, facilitated by customizing reactor configurations to support extractive fermentation.
Hydrodynamic parameters were evaluated as they directly influence mass transfer, phase separation, and overall process efficiency with an enhanced hold-up volume of 67 %, a residence time of 11.3 min, and a mass transfer coefficient rate of 9.78 min-1. The interfacial area (804 m2/m3) revealed the total surface area available for the mass transfer between the aqueous and solvent phases for the efficient transfer of biomolecules. ΔG°E (-8.95 KJ/mol), ΔH (6.389 KJ/mol), and ΔS (0.0490 KJ/mol K) data were evident that the product partitioning was thermodynamically favorable and spontaneous, ensuring the phase behavior and solvent stability.
The activation energy (48.3 kJ/mol), deactivation constant (0.055 min-1), and half-life time (12.6 min) determine the enzyme transition and stability in the DES environment. In addition, a life cycle analysis was conducted for sustainability improvement and to quantify the environmental impacts of the integrated process. The obtained outcomes underscore the potential of extractive fermentation to revolutionize industrial and pharmaceutical production by enhancing yield and reducing costs. Thus, eco-friendly bioprocessing with green solvents has a significant breakthrough in sustainable bioprocessing
Customized feminine hygiene wash containing postbiotics from Lactobacillus spp. to treat Urinary Tract Infections (UTI)
No full textProbiotics, which support host health by maintaining normal microbial flora, offer a promising approach for preventing recurrent infections. To evaluate the prophylactic potential of vaginal probiotics against urinary tract infections (UTIs), we conducted a study on 54 healthy South Indian women aged 18–40 who visited the Obstetrics and Gynaecology outpatient clinic at Trichy SRM Medical College and Research Centre between June and July 2022. Vaginal swabs were collected following ethical guidelines, and commensal microbiota were isolated for probiotic characterization, with the broader objective of developing a customized feminine hygiene wash enriched with vaginal postbiotics.
Lactobacilli cell-free supernatant (CFS) demonstrated potent antibiofilm activity against Uropathogenic Escherichia coli (UPEC). Tryptamine, a major bioactive compound identified in the CFS, significantly inhibited UPEC biofilm formation by disrupting bacterial motility, reducing cell surface hydrophobicity, and impairing extracellular matrix production. Structural analyses confirmed alterations in biofilm morphology, along with reduced bacterial viability and increased reactive oxygen species (ROS) generation. Gene expression studies further supported these findings by showing downregulation of key biofilm-associated genes, including fimA, fimH, papG, and csgA.
In addition to antibiofilm effects, the study explored the efflux pump inhibitory potential of metabolites produced by Lactobacillus jensenii. Seven compounds were isolated from the CFS, among which Compound 7 (C7) exhibited strong fluorescence under UV light. MIC reversal assays demonstrated that C7 significantly enhanced erythromycin susceptibility in Klebsiella pneumoniae, reducing the MIC from 512 μg/mL to 8 μg/mL. GC–MS analysis identified C7 as (–)-Terpinen-4-ol, an isomer of terpineol.
The effectiveness of (–)-Terpinen-4-ol was further validated in vivo using zebrafish infection models, where its combination with erythromycin substantially lowered bacterial bioburden in resistant K. pneumoniae strains. The study also investigated how Lactobacillus-derived metabolites influence antibiotic persistence in UPEC. Antibiotics such as colistin and meropenem increased persister cell formation, but metabolites including itaconic anhydride and (–)-Terpinen-4-ol significantly reduced persistence by enhancing membrane permeability and elevating ROS levels, thereby improving antimicrobial penetration.
Overall, the findings highlight the strong antibiofilm, efflux inhibitory, and antipersister properties of Lactobacillus-derived metabolites, underscoring their potential role in preventing UTIs. In vivo validation using BALB/c mice models demonstrated that the formulated feminine hygiene wash significantly reduced urinary tract colonization and biofilm formation. These results support the development of a postbiotic-based vaginal wash as an effective preventative strategy against UTIs
Bridging Scales in Epidemic Modeling From Individuals to Networks and Ecosystems
No full textEpidemics have shaped the trajectory of human history, repeatedly exposing vulnerabilities in healthcare systems and influencing socio-economic dynamics on a global scale. The COVID-19 pandemic, in particular, underscored the pressing need for flexible and responsive modeling framework capable of adapting to dynamic and evolving epidemic scenarios. Motivated by these challenges, this thesis explores the development of innovative epidemic models that integrate classical mathematical modeling techniques, network-based frameworks, and modern data-driven approaches. Each chapter addresses a distinct dimension of epidemic spread, from vaccination dynamics and community coupling to ecological complexity and adaptive human behavior, culminating in a model-free paradigm suitable for early-stage outbreak scenarios.
The thesis begins by extending classical SEIR compartmental models to incorporate waning vaccine-induced immunity, providing a refined understanding of herd immunity thresholds. Through parameter estimation using COVID-19 data from India, the model illustrates how even extensive vaccination efforts may not prevent resurgence without booster strategies. The study quantitatively captures the influence of vaccination rate on long-term equilibrium states and provides critical thresholds for controlling disease spread. Building upon this foundation, the next focus lies in modeling multi-wave epidemics in spatially coupled communities. Here, a logistic influx term is added to the susceptible population to mimic the periodic emergence of new viral variants. Bifurcation analysis reveals how vaccination influences the system’s transition across periodic, endemic, and disease-free regimes. Extension to a two-patch system with dispersal shows rich dynamical features including birhythmicity and multistability, underscoring the complex role of mobility in epidemic control.
The third contribution investigates eco-epidemiological systems with seasonal forcing, inspired by Rabbit Hemorrhagic Disease. A predator-prey infection model is analyzed under varying seasonal amplitudes and frequencies, uncovering the emergence of extreme events (EEs)—rare, large-amplitude outbreaks. Chaos and periodicity are examined through Lyapunov exponents and Probability Density Functions (PDFs) of prey population maxima provide a more robust measure of extreme events than conventional threshold based approaches. The work emphasizes how seasonality shapes ecological and epidemiological transitions, providing insights into biodiversity conservation and disease management. Shifting to the network paradigm, the fourth chapter introduces a novel adaptive network model in which the contact structure co-evolves with epidemic prevalence. The reduction in an individual’s maximum contact capacity during an outbreak is modeled using a logistic decay function, reflecting adaptive behaviour in response to disease prevalence. Using CoMix survey data from countries like Belgium and the UK, the model is validated and analyzed using both effective-degree ODEs and Gillespie simulations. Results show that early, strong contact reduction can significantly suppress epidemic peaks, highlighting the necessity for adaptive, timely interventions.
Finally, the thesis proposes a model-free approach for early outbreak prediction, focusing on the monkeypox epidemic. An Echo State Network (ESN), a type of reservoir computing architecture, is trained on real-time data to forecast outbreak trends. While achieving high predictive accuracy, the approach is paired with powerlaw scaling analysis to enhance interpretability. This hybrid framework offers a balance between purely data driven forecasts and mechanistic understanding, which is especially beneficial during the early stages of an epidemic when key parameters are yet to be identified. Overall, this thesis presents a multi-scale, multidisciplinary modeling framework that addresses key limitations in classical epidemic models. By progressively incorporating vaccination effects, spatial coupling, ecological complexity, adaptive behavior, and machine learning, the work enhances our ability to understand, predict, and mitigate epidemic outbreaks under diverse real-world conditions. These insights hold substantial implications for public health planning and intervention design in a rapidly changing world
Evidentiary Practices in Arbitration: Need for a Minimum Framework
No full textIndian arbitration has emerged as a preferred method of dispute resolution, offering the benefits of flexibility, cost-effectiveness, and confidentiality over conventional litigation. Although this flexibility, more specifically in evidentiary matters, has brought procedural inconsistencies and uncertainties that detract from the enforceability of arbitral awards.
This is in line with Section 19 of the Arbitration and Conciliation Act of 1996, which prohibits arbitral tribunals from following the Indian Evidence Act of 1872 or the Code of Civil Procedure of 1908. While the provision allows procedural autonomy, it also brings with it an uncertainty in the evidentiary process of arbitrations, mostly when parties do not specify their evidentiary framework in advance.
This study enters the core inquiry of whether the absence of minimum evidentiary requirements in arbitration has resulted in inconsistency and procedural problems, particularly under Section 34, setting out the annulment of arbitral awards. The study uses both doctrinal and empirical methods. Doctrinally, it analyses legislative systems, judicial decisions, and international soft law instruments, such as the IBA Rules and Prague Rules.
Empirically, it surveys views of 400 arbitrators from various legal, technical, and commercial backgrounds to study real evidentiary practice and effects. The study acknowledges that the independence afforded to arbitrators to accept or reject evidence in the quest for efficiency as well as party autonomy leads to uneven practices, influencing fairness and uniformity of arbitral proceedings. Arbitration rules (e.g., of ICADR, IIAC, CNICA, NPAC) provide some structuring of procedure, but not everyone necessarily does so and they are not binding in ad hoc arbitrations.
The thesis argues that an evidentiary model is necessary not to limit autonomy but to promote procedural legitimacy of arbitration in India. Such a model would increase party’s confidence in arbitral awards, reduce disputes over admissibility and weight of evidence, and bring Indian arbitration in line with international best practice. The research thus makes an important contribution to legal reform, arbitrator training, and international debate on standardizing evidence in arbitration