Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Open Ceramics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666539523001657 |
| _version_ | 1797390355390267392 |
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| author | B. Sreenivas Shaik kareem Ahmmad Y.S. Rammah P. Hima Bindu |
| author_facet | B. Sreenivas Shaik kareem Ahmmad Y.S. Rammah P. Hima Bindu |
| author_sort | B. Sreenivas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T23:10:30Z |
| format | Article |
| id | doaj.art-86b0085f19ba431c87be778c109be9ae |
| institution | Directory Open Access Journal |
| issn | 2666-5395 |
| language | English |
| last_indexed | 2024-03-08T23:10:30Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Open Ceramics |
| spelling | doaj.art-86b0085f19ba431c87be778c109be9ae2023-12-15T07:26:22ZengElsevierOpen Ceramics2666-53952023-12-0116100493Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glassesB. Sreenivas0Shaik kareem Ahmmad1Y.S. Rammah2P. Hima Bindu3Department of Physics, University College of Science, Osmania University, Hyderabad, Telangana State, 500007, IndiaDepartment of Physics, Muffakham Jah College of Engineering and Technology, Hyderabad, 500034, Telangana State, India; Corresponding author.Department of Physics, Faculty of Science, Menoufia Univesity, 32511, Shebib El- Koom, EgyptDepartment of Physics, University College of Science, Osmania University, Hyderabad, Telangana State, 500007, India; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2666539523001657Gamma-ray shieldingDensityFTIRPolynomial regressionANN and RFR |
| spellingShingle | B. Sreenivas Shaik kareem Ahmmad Y.S. Rammah P. Hima Bindu Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses Open Ceramics Gamma-ray shielding Density FTIR Polynomial regression ANN and RFR |
| title | Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses |
| title_full | Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses |
| title_fullStr | Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses |
| title_full_unstemmed | Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses |
| title_short | Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses |
| title_sort | radiation shielding capacity and machine learning density prediction of boro bismuth cadmium zinc glasses |
| topic | Gamma-ray shielding Density FTIR Polynomial regression ANN and RFR |
| url | http://www.sciencedirect.com/science/article/pii/S2666539523001657 |
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