Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method
Cuprous oxide (Cu2O) is a p-type metal oxide semiconducting material with potential in photovoltaic and photocatalysis applications due to its excellent absorption capacity in visible region and tunable energy gap. Experimental synthesis and energy gap characterization of thin film cuprous oxide sem...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2022.2137936 |
| _version_ | 1797705732570742784 |
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| author | Talal F. Qahtan Nahier Aldhafferi Abdullah Alqahtani Olawusi Richard Abidemi Miloud Souiyah Abdullah Almurayh Fahad A. Alghamdi Taoreed O. Owolabi |
| author_facet | Talal F. Qahtan Nahier Aldhafferi Abdullah Alqahtani Olawusi Richard Abidemi Miloud Souiyah Abdullah Almurayh Fahad A. Alghamdi Taoreed O. Owolabi |
| author_sort | Talal F. Qahtan |
| collection | DOAJ |
| description | Cuprous oxide (Cu2O) is a p-type metal oxide semiconducting material with potential in photovoltaic and photocatalysis applications due to its excellent absorption capacity in visible region and tunable energy gap. Experimental synthesis and energy gap characterization of thin film cuprous oxide semiconductor with desired dopants and varying experimental conditions for enhanced photocatalytic as well as photovoltaic activities are laborious and consume appreciable precious resources. This work hybridizes particle swarm optimization method with support vector regression algorithm for computing energy gap of thin film cuprous oxide semiconductor using the thickness of thin film and distorted lattice parameter as descriptors. The predictions of swarm-based support vector regression (S-SVR) model are compared with estimates of stepwise regression (SR) model while S-SVR shows superior performance of 39.47 %, 36.20 % and 114.41 % on testing data samples over SR model using root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC), respectively. The developed S-SVR model is characterized with 0.9559 CC, 0.0586 MAE and 0.028 RMSE on the basis of training samples. The developed S-SVR and SR models were further validated using external data samples while the developed S-SVR demonstrates excellent agreement with the measured values. The convincing precision demonstrated by S-SVR model would be of indispensable significance in determining energy gap of cuprous oxide semiconductor (for photocatalytic applications in pollutant removal, solar cell, gas sensors and thin film transistors) with appreciable quickness and reduced cost coupled with experimental difficulty circumvention. |
| first_indexed | 2024-03-12T05:41:48Z |
| format | Article |
| id | doaj.art-a312744a715d4b24b8ae6e0c739fa482 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T05:41:48Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-a312744a715d4b24b8ae6e0c739fa4822023-09-03T06:01:35ZengTaylor & Francis GroupCogent Engineering2331-19162022-12-019110.1080/23311916.2022.2137936Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational methodTalal F. Qahtan0Nahier Aldhafferi1Abdullah Alqahtani2Olawusi Richard Abidemi3Miloud Souiyah4Abdullah Almurayh5Fahad A. Alghamdi6Taoreed O. Owolabi7Physics Department, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaComputer Information Systems Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaComputer Information Systems Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaPhysics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, NigeriaDepartment of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin, Saudi ArabiaDepartment of Educational Technologies, Imam Abdulrahman bin Faisal University, Dammam, Saudi ArabiaDepartment of Management Information Systems, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaPhysics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, NigeriaCuprous oxide (Cu2O) is a p-type metal oxide semiconducting material with potential in photovoltaic and photocatalysis applications due to its excellent absorption capacity in visible region and tunable energy gap. Experimental synthesis and energy gap characterization of thin film cuprous oxide semiconductor with desired dopants and varying experimental conditions for enhanced photocatalytic as well as photovoltaic activities are laborious and consume appreciable precious resources. This work hybridizes particle swarm optimization method with support vector regression algorithm for computing energy gap of thin film cuprous oxide semiconductor using the thickness of thin film and distorted lattice parameter as descriptors. The predictions of swarm-based support vector regression (S-SVR) model are compared with estimates of stepwise regression (SR) model while S-SVR shows superior performance of 39.47 %, 36.20 % and 114.41 % on testing data samples over SR model using root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC), respectively. The developed S-SVR model is characterized with 0.9559 CC, 0.0586 MAE and 0.028 RMSE on the basis of training samples. The developed S-SVR and SR models were further validated using external data samples while the developed S-SVR demonstrates excellent agreement with the measured values. The convincing precision demonstrated by S-SVR model would be of indispensable significance in determining energy gap of cuprous oxide semiconductor (for photocatalytic applications in pollutant removal, solar cell, gas sensors and thin film transistors) with appreciable quickness and reduced cost coupled with experimental difficulty circumvention.https://www.tandfonline.com/doi/10.1080/23311916.2022.2137936Cuprous oxidesupport vector regressionthin filmthicknessparticle swarm optimizationenergy gap |
| spellingShingle | Talal F. Qahtan Nahier Aldhafferi Abdullah Alqahtani Olawusi Richard Abidemi Miloud Souiyah Abdullah Almurayh Fahad A. Alghamdi Taoreed O. Owolabi Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method Cogent Engineering Cuprous oxide support vector regression thin film thickness particle swarm optimization energy gap |
| title | Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method |
| title_full | Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method |
| title_fullStr | Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method |
| title_full_unstemmed | Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method |
| title_short | Modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method |
| title_sort | modeling optical energy gap of thin film cuprous oxide semiconductor using swarm intelligent computational method |
| topic | Cuprous oxide support vector regression thin film thickness particle swarm optimization energy gap |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2022.2137936 |
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