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Recent Submissions

Microwave Absorbing Properties of Composite Agriculture Waste and CNT
(2026-04-28) Tripathi, Anju; Sandha, Karmjit Singh
ABSTRACT In recent years, the exponential growth of wireless communication systems, radar technologies, and compact electronic devices has resulted in excessive electromagnetic (EM) wave emissions, causing severe electromagnetic interference (EMI) and electromagnetic pollution. This not only reduces the performance and reliability of sensitive electronic equipment but also poses potential risks to human health. Conventional chemical-based microwave absorber materials (MAMs) have been employed to mitigate this issue, but their use is constrained due to high production costs, bulkiness, brittleness, and environmental concerns. Therefore, there is a strong motivation to develop lightweight, cost-effective, and eco-friendly alternatives by utilizing agricultural waste-derived materials. This research explores the potential of dried banana leaves (DBL) and sugarcane bagasse (SG), which are rich in carbon content and abundantly available, as sustainable microwave absorbers. The dielectric and magnetic properties of these biowaste materials are first investigated within the X-band frequency range (8.2-2.4 GHz). However, due to their inherently low conductivity and limited absorption bandwidth, agricultural wastes alone cannot meet the requirements of high-performance MAMs. To overcome this limitation, multi-walled carbon nanotubes (MWCNTs), known for their high electrical conductivity, large aspect ratio, and excellent dielectric properties, are incorporated into DBL and SG matrices. These CNT-based composites enhance microwave absorption through mechanisms such as conduction loss, interfacial polarization, multiple scattering, and improved impedance matching. Composite samples of DBL-MWCNT and SG-MWCNT are fabricated by optimizing filler concentration and thickness, followed by evaluation of their dielectric response, reflection loss (RL), and absorption bandwidth. Experimental studies reveal that the addition of even a small amount of MWCNTs substantially improves absorption efficiency, with optimized composites achieving strong attenuation of incident EM waves. Structural and morphological analyses further support the enhanced absorption mechanisms. To ensure reliability under practical conditions, the thermal stability of these composites is also examined. Temperature-dependent studies of DBL and DBL-MWCNT absorbers demonstrate that MWCNT incorporation not only increases absorption efficiency but also stabilizes performance across varying thermal environments. Furthermore, the findings establish that agriculture waste-MWCNTs composites exhibit high absorption efficiency, broadband characteristics, reduced thickness, lightweight structure, and eco-friendly nature, which makes them promising candidates for applications in electromagnetic interference (EMI) shielding, stealth technology, aerospace, and wireless communication devices. The study concludes that utilizing agricultural residues such as DBL and SG, in combination with MWCNTs, provides a sustainable and cost-effective pathway to replace conventional chemical-based absorbers. These results validate the potential of hybrid agricultural waste-CNT composites as next-generation microwave absorber materials and highlight their significance in addressing both technological demands and environmental challenges.
Development of an efficient framework based on Deep Learning for Wildlife Surveillance using Robot
(2026-04-24) Kaur, Parminder; Kansal, Sachin; Singh, V. P.
Wildlife mortality on train tracks represents a significant conservation challenge, as expanding rail networks increasingly intersect with vital animal habitats, leading to frequent and often devastating collisions. Conventional methods for gauging this impact, such as on-the-ground counts, are often limited in their reach and are susceptible to inaccuracies caused by natural decay and predator activity. In contrast, contemporary wildlife tracking methods offer a far more robust solution. By employing remote sensing, camera networks, and audio recording devices, it is possible to achieve continuous and extensive data collection across wide areas. This reduces the need for constant human presence, minimizing disruption to vulnerable species. The capability to collect real-time data allows for the swift identification of high-risk zones and the evaluation of preventative measures, leading to more data-driven and timely conservation efforts. Deep learning algorithms are rapidly becoming crucial for interpreting wildlife tracking data, significantly enhancing the understanding of animal habits and mortality trends. By training neural networks on diverse datasets, including visual and auditory records, recognition and categorization of animals can be automated, even under difficult circumstances. This allows for the efficient processing of large datasets, revealing subtle patterns that would otherwise go unnoticed. By training image classification models on carefully labelled images, one can identify species, track movement, and analyse behaviour, providing essential information for understanding the factors that contribute to train-related animal deaths. Furthermore, it enables the assessment of the effectiveness of mitigation strategies, such as animal overpasses and deterrent systems, by analysing how animals interact with these structures. Autonomous mobile robots equipped with high-resolution cameras and onboard processing capabilities can traverse these areas, capturing vast amounts of visual data. These robots, guided by pre-trained image classification models, can autonomously detect and categorize animals, providing real-time insights into population dynamics and mortality risks. The principal aim of this research is to construct a robust, deep learningdriven framework for automated wildlife surveillance using robotic platforms. This investigation centres on creating a system that enables autonomous robots to navigate and analyze environments, capturing and processing visual data to identify, categorize, vi and track animal. This proposed approach allows for continuous, non-invasive monitoring, reducing human presence and minimizing disturbance to sensitive species. In the first proposed approach, the autonomous robot will sense the animal and capture the image using installed camera. This image is used for image segmentation and classification. To achieve this, Mask-RCNN for feature extraction and prediction with the concept of AMP is used. Unlike existing schemes, the proposed scheme is capable enough for fast computation and maintains accuracy that will be efficiently implemented in a real-time scenario such as wildlife detection. The proposed model is implemented on ROS based mobile robot with Raspberry Pi4. The results obtained using the proposed scheme obtained a mean Average Precision value of 85.47% and an F1 score of 87.73% with a precision value range between 92% to 99%. The second proposed approach introduces PAW: Prediction of wildlife animals using a robot under adverse weather conditions. In this, implementation of image dehazing is performed to extract features from images captured under adverse weather conditions. This approach is an improvement on previous approach, in which image dehazing algorithms are incorporated. In this approach, two main improvements have been done – one improvement is an addition of images in the dataset and other is dehazing before extracting features and classifying the image. In this, dataset of images clicked in bad weather conditions is used, such as mist, haze, smog, and fog, often suffer from poor visibility. To train and test the model, synthetic images consisting of haze and fog were generated using GIMP tool. The results obtained using the proposed scheme obtained a mean Average Precision value of 88% and an F1 score of 92%. In the third proposed approach: Improved Packet Delivery and Energy Consumption centric RSA (IPDEC-RSA), the traditional reptile search algorithm is modified to ensure fast and reliable message delivery with reduced energy consumption and minimum routing distance. The system operates in four phases: first phase is to deploy static robots with sensor nodes in clusters, each with a Cluster Head (CH). In the second phase, optimal CHs are selected for routing based on RSA approach. In third phase, messages are routed through the selected CHs to the Base Station (BS). In fourth stage, data is collected at the BS and transmitted to a cloud server or SMS using MQTT. The model is tested and validated on synthetic hazy dataset, which shows the effective results in controlled real-time implementation.
Transition metal oxides graphene composites for supercapacitor electrodes
(2026-04) Ajravat, Kaveri; Brar, Loveleen K.; Pandey, O. P.
Abstract According to World Energy Council projections, current global energy requirements are expected to double by the year 2050. At present, nearly 85% of the primary global energy supply is derived from fossil-based resources, including petroleum, coal, and natural gas. In view of the current environmental conditions, sustainable and green energy storage solutions are the need of the hour. Supercapacitors have emerged as a promising green energy storage solution with high power density. Supercapacitors also offer high cyclic stability with good capacitance retention. The low energy density of supercapacitors restricts their usage in most practical domains. In order to achieve high energy density along with high power density and high cyclic stability in wide working potential range an effective solution has been suggested by the assembling of hybrid supercapacitors (symmetric and asymmetric). Owing to the distinct potential ranges of the cathode and anode, the asymmetric arrangement requires the operating voltage of the device to be expanded. Graphene and nitrogen doped graphene based TMO composites offer synergistic advantages that include increased specific surface area, improved interfacial charge transport, elevated specific capacitance, tailored morphologies, and enhanced energy density. Collectively, these innovations have significantly boosted the electrochemical performance and practical scalability of Transition Metal Oxide@ graphene composites for high-efficiency energy storage systems. The entire work is presented in ten chapters which are as follows: Chapter 1 introduces briefly the ever increasing problem of global energy demands, supercapacitor as an electrochemical energy storage solution, its types and components. The importance and significant characteristics of nitrogen doped graphene and NiCo2O4 has been presented. The role of third metal incorporation candidates for ternary T-NiCo2O4, (T = Mo, V and Zn), their substitution sites based on crystal field stabilization theory and finally the significance of transition metal oxides @ graphene composites has been discussed. Chapter 2 describes the details of literature related to development of advanced materials for high-performance energy storage systems, particularly supercapacitors. This chapter presents a critical review of the literature pertaining to electrode materials, with a focus on nitrogen-doped graphene, binary transition metal oxides such as nickel cobalt oxide (NiCo2O4), and ternary metal oxides containing metals like molybdenum (Mo), zinc (Zn), and vanadium (V) as third metal in NiCo2O4. Chapter 3 presents the details of precursors and reagents which are used for synthesis of samples. The morphological, structural and physio-chemical characterizations used to study the physical and chemical properties of the synthesized samples are explained in detail. All the electrochemical techniques used to analyze the supercapacitive performance of the samples are explained with useful formulas. The method of preparation of active catalyst and device fabrication for electrochemical mearsurements is highlighted. Chapter 4 presents the results of systematic study for optimization of synthesis parameters for N doped graphene (NG) with appreciable range of N content. Microwave synthesis has been adopted to synthesize N-doped graphene in the present work. The samples were synthesized at different weight ratio of GO:urea. To understand the properties of synthesized samples, FESEM, XRD, RAMAN, N2 adsorption-desorption isotherm, XPS studies were carried out. The study also examines how different nitrogen bonding configurations, along with the ionic mobility and hydrodynamic sizes of the cations and anions in various aqueous electrolytes (0.5 M H2SO4; acidic, 0.5 M K2SO4; neutral and 0.5 M KOH; alkaline), influences the electrode's performance. The best optimized NG sample was chosen to make composites with transition metal oxides for further study and is presented in next chapters. Chapter 5 deals with the optimization of hydrothermal reaction parameters for synthesizing NiCo2O4 nanorods for high supercapacitive performance. Further, a systematic study has been put forth to highlight the effect of varying amount of NG sheets on the morphology and the resulting supercapacitive behavior of the composite nanoflowers formed from NiCo2O4 nanorods. To understand the properties of synthesized samples, FESEM, XRD, RAMAN, N2 adsorption-desorption isotherm and XPS studies were carried out. The samples are tested for supercapacitive performance using CV, GCD and PEIS. ECSA was calculated to determine the number of electrochemically active sites. Dunn’s method was used to determine the percentage contribution from redox controlled and double layer controlled capacitance. The study results in the composite with optimal NG sheets weight ratio having superior supercapacitive performance. This was fixed in the synthesis of ternary transition metal oxides in the further chapters. Chapter 6 presents hydrothermal synthesis approach employed to synthesize MoNiCoO and MoNiCoNG composites at varying synthesis temperatures, enabling a systematic investigation of their morphological and structural features. The NG sheets played a pivotal role in directing the growth of marigold-like nanoflowers composed of wavy, lamellar nanosheets, enhancing both structural integrity and electrochemical performance. To understand the properties of synthesized samples, FESEM, XRD, RAMAN, N2 adsorption-desorption isotherm, XPS studies were carried out. The samples are tested for supercapacitive performance using CV, GCD and PEIS. ECSA was calculated to determine the number of electrochemically active sites. Dunn’s method was used to determine the percentage contribution from redox controlled and double layer controlled capacitance. Chapter 7 discusses the effect of vanadium incorporation in nickel cobalt oxide at varying molar ratios systematically. The introduction of V induced lattice distortions due to the difference in ionic radii between Ni, Co, and V, initiating the formation of a V-NiCo2O4 and Co3V2O8 phase. Further the best synthesized oxide was attached with porous carbon framework: N-doped graphene (NG) and Carbon Black (CB) which significantly altered the structural evolution and electrochemical activity. To understand the properties of synthesized samples, FESEM, XRD, RAMAN, N2 adsorption-desorption isotherm, XPS studies were carried out. The samples are tested for supercapacitive performance using CV, GCD and PEIS. ECSA was calculated to determine the number of electrochemically active sites. Dunn’s method was used to determine the percentage contribution from redox controlled and double layer controlled capacitance. Chapter 8 presents a systematic study of Zn incorporated nickel cobalt oxide (Zn-NiCo2O4) composites integrated with NG synthesized via hydrothermal method The incorporation of NG sheets provided abundant nucleation sites and altered the reaction kinetics, leading to the formation of Zn-NiCo2O4@NG nanoflowers morphology. To understand the properties of synthesized samples, FESEM, XRD, RAMAN, N2 adsorption-desorption isotherm, XPS studies were carried out. The samples are tested for supercapacitive performance using CV, GCD and PEIS. ECSA was calculated to determine the number of electrochemically active sites. Dunn’s method was used to determine the percentage contribution from redox controlled and double layer controlled capacitance. Chapter 9 details the hybrid supercapacitor coin cell devices developed utilizing the best optimized samples identified from the preceding chapters. These devices were assembled in both symmetric and asymmetric configurations, and their electrochemical performance was systematically evaluated under various testing conditions. Further, two combination cells (MoNiCoNG//VNiCoCB and ZnNiCoNG//VNiCoCB) were also fabricated in order to check the supercapacitive performance parameters for enhanced voltage range leading to superior energy density. Chapter 10 presents the conclusions drawn from the work done. It further describes the future prospects that could lead more advanced and efficient next generation hybrid supercapacitors.
Social Media-Driven Stress and Sleep Analysis Using QPSO-Enhanced Explainable AI Models
(Thapar Institute of Engineering and Technology, 2025-01-01) Yogita; Kaur , Maninder; Rai, Rajanish Kumar
The growing use of digital technology in everyday life is notably through social networks, which has led to a worldwide surge in stress levels and disturbances of sleep quality. This work introduces a machine learning model for predicting sleep quality and stress based on social media usage patterns, lifestyle factors, and physiological signals. Four regression-based machine learning models are Random Forest, Gradient Boosting, Support Vector Regression, and Linear Regression. They were run on a dataset that includes behavioral (for example, inbed screen use), physiological (e.g., cortisol and melatonin levels), and self-reported health measures. Hyperparameter tuning was performed to improve performance using Quantum- Behaved Particle Swarm Optimization (QPSO), which resulted in substantive improvements in model accuracy and generalizability. In addition, explainable artificial intelligence (XAI) methods such as SHAP and LIME were used to explain the predictions of the models and determine the primary predictors that influence sleep and stress outcomes. The findings show that pre-sleep social media consumption, sleep latency, and stress ratings are good predictors of sleep quality and vice versa. The suggested ML-QPSO-XAI model improves predictive reliability and transparency, thus proving an effective tool for upcoming digital health devices and mental well-being monitoring systems.
Deciphering changes in photophysical properties of carbon dots and protein conformations induced by bioactive molecules
(2025-06-04) Kaur, Mandeep; Maity, Banibrata; Bhattacharya, Mily
The thesis entitled “Deciphering changes in photophysical properties of carbon dots and protein conformations induced by bioactive molecules” is divided into five chapters. Chapter-1 It describes the background information on proteins, biomolecules, bile salts, nanomaterials, nanosensor and deep eutectic solvents together with literature survey and scope of the work. This chapter provides detailed information on the significance of protein interactions with bioactive molecules (bile salts), carbon dots (CDs), its synthesis approaches, fluorescence mechanisms along with their optical properties and applications, newly emerged green deep eutectic solvents were also discussed. Are also discusses the various fluorescence quenching mechanisms of CDs in the presence of quenchers and their photophysical factors like UV-Visible absorption, steady state and time-resolved fluorescence studies, photostability, photoluminescence quantum yield, etc. Chapter 2 of the thesis includes a brief summary of the characterization techniques used in all of the experiments undertaken throughout the thesis. The widely used characterization techniques used includes UV-Visible spectroscopy, steady-state fluorescence spectroscopy, time-resolved fluorescence spectroscopy, high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), energy dispersive spectroscopy (EDS), attenuated total reflectance Fourier-transform infrared (ATR FT-IR) spectroscopy, Raman spectroscopy, grazingincidence X-ray diffraction (GIXRD), and zeta potential measurements are among the most widely utilized methods for characterization. These are utilized to better understand the interactions between protein and bile salts, as well as to research the physical, chemical, and optical properties of biomass-derived carbon nanoparticles, along with their practical usefulness as a nanosensor as well as the underlying mechanism. Bile salts are physiologically-important natural amphiphilic biosurfactants synthesized in the liver and play a vital role in the solubilization and digestion of dietary lipids, cholesterol, and other fatsoluble compounds in the body. Bile salts also exhibit pharmacological and biological uses asxx carriers for transporting poorly water-soluble drugs and other chemicals owing to their unique emulsifying, solubilizing capacities and micelle forming ability. In this context, we present an endeavour towards elucidating the interaction and binding between the most abundant plasma protein namely, human serum albumin (HSA) and bile salts to demonstrate an intriguing interplay of hydrophobic, electrostatic and hydrogen bonding effects using steady state absorption, fluorescence emission, anisotropy, time-resolved emission, and molecular modelling approaches. The outcome illuminates how amphiphilic interfaces of bile salts under various physico-chemical conditions trigger the conformational changes and binding affinities of native and molten-globule forms of HSA. To elucidate non-covalent interactions during HSA-bile salt supramolecular hostguest complex formation, changes in intrinsic (tryptophan) and extrinsic (ANS) fluorescence have been investigated. The results reveal upon binding of bile salts in subdomain IIA of HSA, the protein undergoes conformational changes mediated primarily by hydrophobic interactions. Furthermore, time-resolved fluorescence measurements provide important structural and dynamical insights into the protein-bile salt supramolecular complexes. Additionally, molecular docking studies on these complexes clearly reveal spontaneous binding of bile salts into subdomain IIA of HSA while suggesting that the binding affinity decreases with the decreasing order of hydrophobicity of the bile salts (NaDC>NaC>NaTC) (ΔGdock = -29.64 kJ mol-1, -26.15 kJ mol-1, -14.35 kJ mol-1) respectively. This study exclusively highlights the molecular mechanism of conformational perturbation in native (pH 7) and molten-globule (pH 3) forms of HSA, induced by bile salts. We believe that the results reported herein will be helpful in the design and formulation of protein-bile salt-based pharmacological carriers suitable for drug delivery. Riboflavin (RF) detection is essential for controlling nutritional health due to its increasing significance in the food and pharmaceutical industries. Regular daily intake of RF (vitamin B2) is important because it is not synthesized and stored in the human body in appreciable amounts. Therefore, an efficient and biocompatible nanosensor with good selectivity and sensitivity for RF detection is required. CDs derived from biomass have recently attracted interest in environmental science due to their simple, cost-effective methods of synthesis, as well as their sustainability advantages and practical implications. Herein, we demonstrate the utility of a ratiometric fluorescence-based CDs nanosensor for the detection of RF in its isolated, pure form as well as inxxi pharmaceutical tablets. We report the synthesis, characterization, and sensing potential of intrinsic nitrogen-functionalized carbon quantum dots (N-CDs) from Indian gooseberry (a renewable biomass precursor) using a microwave assisted pyrolysis method that involves a green methodology. High-resolution transmission electron microscopy (HRTEM) indicated that N-CDs are monodisperse with an average diameter of ∼8.1 nm. Fourier-transform infrared (FTIR) and Xray photoelectron spectroscopy (XPS) validated intrinsic nitrogen functionalization and the presence of amino, hydroxyl, and carboxyl groups on the surface of N-CDs. Further, X-ray diffraction (XRD), UV-Visible and fluorescence spectroscopy, and time-correlated single photon counting (TCSPC) measurements were also employed for the characterization of N-CDs. The asprepared nanoprobe exhibits bright green emission with a remarkable fluorescence quantum yield of ∼48%. Moreover, N-CDs are highly water-soluble and are extremely stable in a range of pH, ionic strength, and photoirradiation. Additionally, N-CDs selectively and specifically detect RF (vitamin B2) in aqueous media w.r.t various bio-analytes with a limit of detection (LOD) ∼35 nM. Our nanosensor can also detect vitamin B2 present in commercially available pharmaceutical tablets with an LOD of ∼61 nM. Mechanistic studies confirmed that sensing involves fluorescence resonance energy transfer (FRET) between RF and N-CDS interfaces. Overall, the present work provides a new vision for the development of an innovative and sensitive approach of a green fluorescent nanosensor for the detection of RF which may find potential applications in the pharmaceutical and food industries. The significant toxicity and environmental persistence of 4-nitrophenol (4-NP) create an urgent need for eco-friendly, effective detection methods. Due to its persistence, toxicity, and carcinogenicity, 4-NP has been classified by the U.S. Environmental Protection Agency as a primary pollutant. It commonly contaminates the environment, primarily as a byproduct of the pharmaceutical industry. In biological systems, 4-NP can cause significant damage to organs such as the liver and kidneys, impair central nervous system function, and contaminate the bloodstream. To address the challenge of 4-NP detection, the study introduces a novel and sustainable detection technique using nitrogen and chlorine co-functionalized carbon dots (abbreviated as S-CDs). The synthesis protocol was employed through hydrothermal method, using sucrose as a carbonxxii precursor and deep eutectic solvent (DES) composed of choline chloride and urea in a 1:2 molar ratio. The synthesized nanosensor exhibited brilliant green fluorescence under UV light, showed excellent water solubility, high photostability with a quantum yield value of ∼56%. HRTEM analysis revealed that the S-CDs were spherical, monodisperse, and had an average diameter of 3.06 nm. FTIR and XPS analyses confirmed intrinsic nitrogen and chlorine functionalization, showing the presence of amino, hydroxyl, carboxyl, and chlorine groups on the surface of S-CDs. Further characterization of S-CDs included X-ray diffraction (XRD), ultraviolet-visible (UV-Vis) spectroscopy, fluorescence spectroscopy, and time-correlated single photon counting (TCSPC) studies. The nanoprobe exhibited high selectivity and sensitivity for 4-nitrophenol (4-NP), with a detection limit of 10 nM. Mechanistic studies verified an inner filter effect (IFE) mechanism between S-CDs and 4-NP, with significant spectral overlap and no change in average lifetime values, attributed to the formation of a zwitterionic spirocyclic Meisenheimer complex. Additionally, photophysical parameters, including quenching efficiency and binding constant, were also assessed to further understand the sensing mechanism. This work paves the way for developing a sensitive, green fluorescent nanosensor for rapid, cost-effective and environmentally friendly approach as well as on-site detection of 4-NP, offering a promising tool for pollution monitoring and control for environmental water samples