Faculty Publications
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Item Biocompatible Nanohydroxyapatite from Cuttlefish Bone by Mechanochemical Method for Bone Tissue Engineering Applications(Springer, 2024) Jalageri, M.B.; Kumar, G.C.Hydroxyapatite was synthesized from coral cuttlebone using a mechanochemical method in this study. The synthesized material was characterized using various techniques to determine its phases and functional groups. Field emission scanning electron microscope (FESEM), Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis TGA were employed. FESEM analysis revealed an onedimensional nanorod morphology of the developed material. X-ray diffraction (XRD) confirmed that the primary phase was hydroxyapatite, with slight traces of tricalcium phosphate detected after calcination at 800 °C. The FTIR spectra exhibited peaks corresponding to phosphate and hydroxyl groups. At the same time, TGA results indicated the absence of any organic phase. Furthermore, the synthesized hydroxyapatite displayed excellent antimicrobial activity against Escherichia coli and Staphylococcus aureus bacteria. Cytocompatibility tests with MG63 fibroblast cells demonstrated that these materials are both antimicrobial and biocompatible, making them suitable for various biomedical applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Processing and characterization of egg shell derived nano-hydroxyapatite synthetic bone for Orthopaedic and Arthroscopy implants and substitutes in dentistry(Elsevier Ltd, 2023) Santosh Kumar, B.Y.; Kumar, G.C.; Shahapurkar, K.; Tirth, V.; Algahtani, A.; Al-Mughanam, T.; Alghtani, A.H.; Murthy, H.C.The present work is focused on the nano-Hydroxyapatite (nHAp) synthesis with two different Indian breed Aseel and Kadaknath eggshells. The alloplast implants were developed through the foam replica method with polyurethane 45-PPI as a porous template. The synthesized nHAp was characterized by Field Emission Scanning Electron Microscopy (FE-SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The FE-SEM images of the nHAp showed the one dimensional clustered nanoparticles and the X-ray diffraction spectrum confirms that the major phase was hydroxyapatite with a small trace of β-tricalcium phosphate. The maximum compression strength of the sample was 5.49 ± 0.12 MPa which is in the range of the compression strength of human trabecular bone. The thermal and degradability studies results confirmed that these are highly stable and provides necessary a resorption needed for new bone tissue formation. Besides, the antimicrobial activity against tested human microbiome are satisfactory and the cell viability towards MG 63 human osteoblast-like cells provides a potential pathway for developing the nHAp implants for bone tissue engineering. © 2023 Elsevier LtdItem Predictive modeling of PMMA-based polymer composites reinforced with hydroxyapatite: a machine learning and FEM approach(Gruppo Italiano Frattura, 2025) Singh, R.K.; Verma, K.; Kumar, G.C.This research examines the mechanical characteristics of polymer composites (PMMA) that are reinforced with Hydroxyapatite (HAp), with a particular emphasis on the Elastic Modulus and Compressive Strength. The investigation employs a multifaceted approach that integrates experimental methods, micromechanical analysis, and machine learning techniques. Experimental assessments of Elastic Modulus and Compressive Strength were conducted at various HAp concentrations (5%, 15%, and 30%) and were compared with theoretical predictions derived from Representative Volume Element (RVE) and micromechanical frameworks, including Voigt and Reuss bounds. Various machine learning algorithms, such as Feedforward Neural Network (FFNN), Radial Basis Neural Network (RBNN), and Support Vector Machine (SVM), were used to predict the mechanical properties. The RBNN exhibited high accuracy (R² = 0.92; MAE = 0.05) for intermediate HAp levels (20-30%) but displayed instability at the extremes % of reinforcements values . The FFNN consistently provided lower estimates of the properties, whereas the SVM yielded robust and stable predictions that closely matched both experimental and theoretical results with the error of (2-5) % (Result value). This research highlights the effectiveness of integrating micromechanical modeling with machine learning to improve the prediction and comprehension of composite behavior, thereby offering valuable insights for the design and application of advanced materials. © 2025, Fracture Structural Integr. All rights reserved.