Development and characterization of borate-based bioactive glasses incorporating rice husk-derived silica Shaik Kareem Ahmmad, M.M. Ahmed Mahmoodi, A.S. Sai Prasad, Nazima Siddiqui, Kurapati Rajagopal, J. Laxman Naik Next Materials, 2025 This study explores the use of rice husk ash (RHA), an agricultural waste product rich in silica, as a sustainable alternative to conventional silica in the fabrication of bioactive glass. Five glass compositions in the system 30B₂O₃–20SiO₂–x(K₂O/Na₂O)–(20–x)Li₂O–20CaO–10ZnO (x = 0, 10) were synthesized via melt-quenching by replacing commercial SiO₂ with RHA-derived SiO₂. The structural, thermal, mechanical, and optical properties of these glasses were systematically evaluated through techniques such as XRD, XRF, EDS, DTA, UV-Vis spectroscopy, and ultrasonic measurements. Bioactivity assessments were performed by immersing samples in simulated body fluid (SBF) for up to 14 days. Results confirm that RHA-derived SiO₂ has significant purity and amorphous structure, making it suitable for glass formation. The glasses with RHA-SiO₂ exhibited comparable density, elastic moduli, and thermal stability to those with commercial silica. Energy dispersive X-ray spectroscopy (EDS) analysis revealed hydroxyapatite formation on the glass surfaces, confirming good in vitro bioactivity and validating RHA-derived silica as a viable substitute for commercial silica in bioactive glass formulations. Moreover, Bioactivity studies demonstrated a comparable or slightly enhanced formation of hydroxyapatite and calcite phases in RHA-SiO₂-based glasses, highlighting its potential as a sustainable and efficient alternative to conventional silica for the development of bioactive glasses in biomedical applications.
Dielectric constant of boro silicate aluminum glasses using AI and radar sensor Shaik Kareem Ahmmad, Kurapati Rajagopal, Nazima Siddiqui, Mohd Abdul Muqeet, Gouri R Patil, Ega Chandra Shekhar, P. Hima Bindu Results in Optics, 2025 • First time dielectric constant study using BGT60TR13C radar on SiO₂–Na₂O–CaO–B₂O₃–Al₂O₃ glasses. • Na₂O (38 %) showed strongest impact on ε r , followed by SiO₂, CaO, B₂O₃, and Al₂O₃. • ANN gave top accuracy (R²=0.985), beating GBR and RFR in nonlinear prediction. • AI enables fast glass optimization, reducing tests and accelerating materials research. This study presents a comprehensive analysis of the dielectric constant (ε r ) of glasses with varying chemical compositions, utilizing artificial intelligence (AI) predictions and experimental validation. An AI model, trained on over 100 glass compositions, was employed to predict ε r based on compositional inputs such as SiO 2 -Na 2 O-CaO-B 2 O 3 -Al 2 O 3 . For the first time, the BGT60TR13C radar sensor was adapted for non-contact dielectric constant measurements, offering a novel methodology for material characterization. To validate the AI predictions and sensor values, two additional experimental techniques were employed: an LCR meter for capacitance-based measurements and the parallel plate capacitor method. Results showed excellent agreement among all methods, confirming the reliability of AI predictions and the accuracy of the experimental techniques. Furthermore, the dielectric constant increased with higher concentrations of network modifiers and secondary network formers. This study highlights the integration of AI and advanced sensing technologies as a powerful hybrid framework for rapid and accurate material characterization, introducing the radar sensor as an innovative tool for dielectric measurements.
Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses B. Sreenivas, Shaik kareem Ahmmad, Y.S. Rammah, P. Hima Bindu Open Ceramics, 2023 Bismuth borate cadmium zinc glasses were prepared using melt quenching technique for shielding applications. The glasses had the composition (80-x)B2O3-xBi2O3-10CdO-10ZnO, where 0<x<20mol%. The densities of the glasses were predicted using machine learning tools and were found to increase with increasing Bi2O3 content. This increase in density was correlated with changes in the infrared spectrum of the glasses. The density of the glass changed from 4.410 g/cm³ to 6.9830 g/cm³ when bismuth oxide (Bi2O3) was added to the glass network. This is because Bi2O3 changes the structure of the glass. The presence of octahedral BiO6 units in the glass is confirmed by the peak at 514 cm-1 in the infrared spectrum. This peak shifts to a lower wavenumber as the amount of Bi2O3 in the glass increases. The shift of the infrared peak from 688 cm-1 to 596 cm-1 indicates that Bi2O3 is actively participating in the glass network, which results in a decrease in the number of BO3 units. The shift of the infrared peak from 1060 cm-1 to 1137 cm-1 is attributed to B-O stretching vibrations in BO4- units, and also confirms that the number of BO4 units is increasing. The density of the glass was predicted using machine learning algorithms that are trained on the chemical composition and experimental density of glasses. Polynomial regression and artificial neural networks (ANNs) were used to predict the density of glass from its chemical composition. Polynomial regression performed best with a 10th-degree polynomial, with an R2 value of 0.8820. ANNs performed best with a tanh activation function, with an R2 value of 0.8923. Random forest regression performed better than other machine learning models at predicting the density of glass, with an R2 value of 0.920. The predicted values were very close to the experimental values. The mass (MAC) and linear (LAC) attenuation coefficients of radiation shielding followed the following trend: (MAC, LAC)0 < (MAC, LAC)5 < (MAC, LAC)10 < (MAC, LAC)15 < (MAC, LAC)20. The half value layer followed the trend: HVL followed as: (HVL)0 > (HVL)5 > (HVL)10 > (HVL)15 > (HVL)20. The Radiation Protection Effectiveness (RPE) reinforces the superiority of BBCZ-20 glasses in shielding against gamma radiation, making them promising candidates for radiation protection applications.
