http://hdl.handle.net/2080/19 2026-09-24T08:28:29Z http://hdl.handle.net/2080/5953 Title: Fluorescent Quantum Dot Architectures: Multiplexed Detection of Biomarkers for Precision Healthcare Diagnostics Authors: Mohapatro, Upasana; Mohapatra, Sasmita Abstract: Early and accurate disease diagnosis remains a critical bottleneck in modern healthcare due to scarcity of precise and sensitive detection methods [1]. Carbon quantum dots(CD) present finest sensing platforms due to their exceptional photostability, biocompatibility, and surface-engineering versatility [2,3]. Herein, unique surface-engineered biosensors are introduced; Zn-CD@Eu, CD@Tb and LysoDot for multiplexed detection of DPA,UA, EP, PPi, lysosomal-microviscosity; possessing excellent bioimaging ability and resilience to matrix interference for their distinct detection strategies. Description: Copyright belongs to the proceeding publisher 2026-08-01T00:00:00Z http://hdl.handle.net/2080/5952 Title: An Enhanced Deep Learning Framework for Breast Cancer Histopathological Image Classification Authors: Kumar, Ranjan; Patel, Sanjeev Abstract: Accurate and timely identification of breast cancer (BC) is crucial for effective clinical decision-making and treat-ment planning. Histopathological image analysis provides valu-able information for BC diagnosis, although manual assessment is labour-intensive and subject to differences between observers. In this work, a deep learning-based framework is developed to automatically classify breast histopathological images from the BreakHis dataset. The framework leverages an EfficientNet-based transfer learning strategy and integrates focal loss with label smoothing, data augmentation, cosine learning-rate decay, and fine-tuning to improve learning robustness and classification performance. Our model is designed to optimize malignant recall with an overall high level of accuracy and stability. Experimental studies state that the proposed method achieves a test accuracy of 94.08%, AUC of 0.9727, a malignant recall of 99%, and a significant improvement over multiple baseline CNN architectures. Through detailed evaluation using confusion matrix analysis, ROC curve analysis and learning dynamics, it has been observed that the proposed framework demonstrates the effectiveness and stability for BC histopathological image classification. Description: Copyright belongs to proceeding publisher 2026-09-01T00:00:00Z http://hdl.handle.net/2080/5951 Title: Strontium-Functionalized Titanium Nanotube Surfaces for Next-Generation Dental Implants Authors: Mishra, Ankita; Biswas, Amit Abstract: Dental implants are widely used for oral rehabilitation, however their long-term success is often compromised by inadequate osseointegration and peri-implantitis. Ti–6Al–4V is a commonly used implant material due to its excellent mechanical properties, corrosion resistance, and biocompatibility. Nevertheless, bacterial colonization and the release of alloying ions may trigger inflammation and impair bone healing. This work aims to develop a functionalized titanium surface featuring strontium-incorporated titanium nanotubes to improve antibacterial and osteogenic properties, thereby effectively preventing peri-implantitis and enhancing the long-term success of dental implants. The optimized anodization process produced uniform titania nanotubes with excellent wettability and structural integrity, while controlled Sr deposition enabled stable strontium incorporation and maintained superhydrophilicity. Surface and chemical analyses confirmed the successful modification of the nanotube surface. These findings establish strontium incorporated titania nanotubes as a promising platform for further functionalization with growth-factor loading to enhance osseointegration, reduce bacterial colonization, and minimize peri-implantitis risk in next-generation dental implants. Description: Copyright belongs to the proceeding publisher 2026-08-01T00:00:00Z http://hdl.handle.net/2080/5950 Title: Synthesis and Characterization of Cellulose Nanocrystals from Cottonseed Hulls Using Acid Hydrolysis and Ultrasonication Authors: Gopalakrishnan, Kishore Kumar; Singh, Sushil Kumar Abstract: Cottonseed hulls are abundant byproducts of the cottonseed oil industry and are rich in cellulose fibers (30-50 %). In this research, cellulose nanocrystals were synthesized from cottonseed hulls via acid hydrolysis followed by ultrasonication. We investigated two acid concentrations (50 % and 60 % (v/v)) and ultrasound amplitudes (30 % and 50 %) in the synthesis of cellulose nanocrystals. The effects of treatment conditions on the physical, thermal, and structural properties were assessed using Fourier Transform Infrared Spectroscopy, X-ray diffraction spectroscopy, Differential Scanning Calorimetry, and Field Emission Scanning Electron Microscopy. The FESEM results showed fiber-like structures in the range of 100- 200 nm. The results revealed that the synthesis conditions significantly (p<0.05) affected particle size and structural properties, and that the ultrasound treatment helped reduce the acid concentration and obtain uniform cellulose nanofibers. The obtained nanofibers found diverse applications in food packaging, biomedical, waste-water purification, and textiles industries, etc. Description: Copyright belongs to proceeding publisher 2026-08-01T00:00:00Z