TIET Digital Repository

Thapar Institute of Engineering & Technology (TuDR)

Welcome to Thapar Institute of Engineering & Technology Digital Repository (TuDR).

TuDR is the digital asset management system which integrates the intellectual output in the form of research articles, PhD theses, and M.Tech / M.E. theses. TuDR facilitates the sharing and exchange of intellectual output of the university.

TuDR supports the management of scholarly resources of enduring value to Thapar University. Faculty members, students, and research scholars use TuDR services to share their intellectual work with the global academic community.

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Recent Submissions

Identification of Novel DNA Gyrase B Inhibitors Using Integrated QSAR Modelling, Pharmacophore-Based Virtual Screening and Molecular Docking
(2026-09-15) Raina, Saaid; Mandal, Debasish
Antimicrobial resistance (AMR) has become a major concern for global healthcare, reducing the effectiveness of existing antibiotics and emphasizing the need for new antibacterial agents. DNA Gyrase B, which is an essential bacterial enzyme involved in the ATP-dependent DNA supercoiling, is considered a promising therapeutic target because of its role in survival of bacteria and the absence of a human counterpart. The present study employs, an integrated computational approach to facilitate the discovery of potential DNA Gyrase B inhibitors. Machine learning-based Quantitative Structure–Activity Relationship (QSAR) modelling was used to predict the biological activity of reported inhibitors, while a structure-based pharmacophore model was developed for virtual screening of chemical libraries. The identified compounds were subsequently evaluated through drug-likeness assessment, applicability domain analysis, and molecular docking to prioritize promising candidates. This workflow provides a systematic strategy for accelerating antibacterial lead discovery and may support the development of novel therapeutic agents against drug-resistant bacterial infections. Keywords: Antimicrobial Resistance, DNA Gyrase B, QSAR, Machine Learning, Pharmacophore Modelling, Molecular Docking
Influence of Stationary Phase Capacity on Anionic Separation: A Comparative Study of Dionex IONPAC AS 23 and AS11 Columns
(2026-09-15) Bounthiyal, Kashak; Reddy, M Sudhakara; Sharma, Anupma
Ion chromatography is a known laboratory technique used for separation and quantification of inorganic anions in food, environmental, pharmaceutical and industrial products. Ion- exchange chromatography works on the basis of ion-exchange principle of stationary phase which is a key factor in retention time, selectivity and resolution of an analyte. The main objective of the current research is to evaluate the effect of stationary phase capacity on ionic separation through comparative analysis of the Dionex Ion Pac AS11 and Dionex Ion Pac AS23 columns with use of suppressed conductivity detection. Major inorganic anions such as fluorides, chlorides, nitrites, nitrates, phosphates and sulfates were detected in standard solutions and in cases of real environmental waters and milk at optimized chromatographic conditions. The performance evaluation of the two types of columns was based on the results of investigations for retention period, peak resolution, selectivity, linearity, precision, accuracy as well as limits of detection (LOD) and quantification (LOQ). The findings showed that the Ion Pac AS11 column has a better ability to differentiate and slightly retain highly retained and multivalent anions during gradient elution. Therefore, the use of the Ion Pac AS11 column is align with analyzing complicated environmental samples. On the other hand, the Ion Pac AS23 column is characterized by shorter analysis time, stable baseline performance, and excellent resolution of easily soluble inorganic anions during isocratic elution, which suits it for everyday water quality testing. Validation of the method showed that it works well, with good linearity, precision, accuracy, recovery, sensitivity, and reproducibility. These results emphasize the importance of choosing the right stationary phase depending on the complexity of the sample and objectives of analysis. This study brings practical advice on improving ion chromatographic processes in laboratories aimed to analyze different inorganic anions.
A Sustainable Deep Eutectic Solvent-Assisted Approach for the Synthesis of Ascorbic Acid-Derived Carbon Dots toward Dual Fluorescence Sensing of Manganese(VII) and Glutathione
(2026-09-14) Puri, Manu; Maity, Banibrata
In this thesis, blue-emissive carbon dots (AA-CDs) was synthesized via a sustainable hydrothermal approach using L-ascorbic acid as the carbon precursor and a choline chloride/urea (1:2) deep eutectic solvent (DES) as a green reaction medium. The synthetic strategy emphasizes environmentally benign chemistry by employing renewable, low-cost, and readily available precursors while avoiding hazardous reagents. The as-prepared AA-CDs exhibited excitation-dependent photoluminescence with a maximum emission centered at 416 nm under 330 nm excitation. Comprehensive investigations of pH, ionic strength, temperature, and continuous UV irradiation demonstrated their excellent photostability and environmental robustness, highlighting their suitability for fluorescence sensing under diverse operating conditions. The AA-CDs displayed high selectivity toward multiple metal ions, with exceptional sensitivity for permanganate (Mn(VII) ions), achieving a detection limit of 307.25 nM. Furthermore, fluorescence quenching induced by Mn(VII) was selectively restored in the presence of glutathione (GSH), enabling its determination with a detection limit of 35.27 μM. The fluorescence recovery originates from the redox reaction between GSH and Mn(VII), which effectively eliminates the quenching species and restores the emissive state of the AACDs. This sequential fluorescence "turn-off/turn-on" sensing platform provides a simple, rapid, and highly selective strategy for the dual detection of Mn(VII) and GSH. The combination of green synthesis, excellent photostability, and sensitive dual-analyte detection underscores the potential of DES-derived carbon dots as versatile fluorescent nanoprobes for environmental monitoring and bioanalytical applications.
Rice Husk Biochar-Based Ag3PO4/g-C3N4 Ternary Photocatalyst for Solar-Light-Driven Tetracycline Degradation: Kinetics, Mechanism and Degradation Pathways
(2026-09-08) Kaur, Loveneet; Basu, Soumen
The present study involves the preparation of a Ag3PO4/g-C3N4/Rice husk biochar (CAR) heterojunction photocatalyst by adding 30 wt% biochar to the Ag3PO4/g-C3N4 (AC) binary composite (70 wt%). Different composites were prepared with weight ratio of Ag3PO4 and g-C3N4 (1:1, 1:3, and 3:1), while keeping the amount of biochar constant, for the degradation of tetracycline (TC) from water. The material was characterized using XRD, XPS, FESEM, EDS, HRTEM, BET, BJH, FTIR, UV–Vis DRS, and PL, which confirmed its good crystal structure, high surface area, and strong response to sunlight. Among all the prepared samples, the 1:1 composition (CAR11) showed the best photocatalytic activity. It degraded about 96% (0.03549 min-1) of TC (25 ppm) under sunlight within 75 min, while under visible and UV light, the degradation efficiencies were 47.5% and 65.5%, respectively. The effects of different experimental conditions, such as pH, light intensity, catalyst dosage, and reactive species, were also studied to understand the photocatalytic process. The catalyst showed good stability and could be reused for six cycles, maintaining about 81% of its photocatalytic activity. Radical trapping experiments revealed that •O2⁻ radicals were the main reactive species in TC degradation. The degradation intermediates were identified by HRMS analysis, and TOC (76%) and COD (73.2%) measurements confirmed that most of the dye was mineralized into simpler and less harmful products. These results show that the CAR11 exhibited high efficiency and stability for solar-light-driven pollutant degradation.
Polycystic Ovary Syndrome (PCOS) Detection: Using Deep Learning Approaches for Images and Clinical Features
(NA, 2026-09-04) Madaan, Arushi; Bajaj, Anu; Garhwal, Sunita
The hormone disorder known as polycystic ovarian syndrome (PCOS) is most prevalent in women nowadays. The ovaries of women are directly impacted by this metabolic disorder, where numerous small sacs of fluid develop around the edges of the ovary, called cysts or follicles. Despite advancements in technology, the exact cause of PCOS still remains unknown. It takes a lot of work for the doctors to manually diagnose it from the ultrasound (US) images. For PCOS identification, a range of machine learning (ML), deep learning (DL) and image processing techniques were employed, that allowed for the identification of follicle counts and follicle size analysis. The current research on PCOS detection for image segmentation and classification, as well as clinical characteristics, was reviewed in this work. For this study, we utilised two different kinds of dataset: image data and clinical feature dataset. We proposed a transfer learning-based DL approach for classifying women with PCOS by using ultrasound images. InceptionV3 and ResNet50 models were used, which achieved accuracy rates of 99.68% and 97.5%, respectively. Numerous DL algorithms were applied on the clinical dataset using various feature selection techniques. The flower pollination algorithm (FPA), cuckoo search algorithm (CSA), and genetic algorithm (GA) are the nature-inspired feature selection algorithms that have been utilised in the study. The results of the proposed GA-based feature selection using an ensemble model were statistically tested with the existing algorithms using Analysis of variance (ANOVA) and the Tukey test, with a 94.58% accuracy, 97.48% precision, 83.76% recall, 90.08% F1-Score, and 99.09% specificity. The interpretability of the results is further ascertained by explainable artificial intelligence (XAI) techniques, i.e., LIME and SHAP. Overall, by using reliable and accurate prediction models, this work advances predictive analytics in PCOS diagnosis with the goal of assisting early diagnosis and well-informed decision-making processes.