Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method
AbstractStrontium titanate is a functional ceramic material with unique optical properties and chemical stability which facilitate its wider applicability as photocatalyst for solving energy crisis as well as environmental challenges, oxide thin film substrate and for manufacturing special oxygen se...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2023.2232596 |
| _version_ | 1797299267372580864 |
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| author | Miloud Souiyah |
| author_facet | Miloud Souiyah |
| author_sort | Miloud Souiyah |
| collection | DOAJ |
| description | AbstractStrontium titanate is a functional ceramic material with unique optical properties and chemical stability which facilitate its wider applicability as photocatalyst for solving energy crisis as well as environmental challenges, oxide thin film substrate and for manufacturing special oxygen sensors (high temperature) among others. Efficient utilization of strontium titanate as photocatalyst and opto-electronic device requires extension of light harvesting capacity beyond visible region through foreign material incorporation by doping mechanisms which is experimentally demanding and time consuming. This work models band gap of strontium titanate magnetic photocatalyst through extreme learning machine (ELM) using the crystal distortion (as a result of dopant incorporation) and crystallite size as predictors. The developed ELM-based models with sigmoid (Sig) and triangular basis (Tranba) activation function perform excellently better than the existing stepwise regression algorithm (SRA) model in the literature using different performance measuring parameters which include the coefficient of correlation (CC), mean absolute error and root mean square error. In the training developmental stage, the developed Sig-ELM model outperforms the developed Tranba-ELM and the existing SRA (2021) model with performance improvement of 2.96% and 67.37%, respectively, on the basis of CC performance metric, while similar performance improvement was obtained during testing phase of model development using different performance metrics. The superiority in the performance of the present models as compared to the existing model strengthens the potentials of the developed model in adjusting and extending light harvesting ability of strontium titanate semiconductor for various technological and industrial applications. |
| first_indexed | 2024-03-07T22:47:10Z |
| format | Article |
| id | doaj.art-00d687fbe97e440ea86c5078116de2e9 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-07T22:47:10Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-00d687fbe97e440ea86c5078116de2e92024-02-23T15:01:40ZengTaylor & Francis GroupCogent Engineering2331-19162023-12-0110110.1080/23311916.2023.2232596Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine methodMiloud Souiyah0Department of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin, Saudi ArabiaAbstractStrontium titanate is a functional ceramic material with unique optical properties and chemical stability which facilitate its wider applicability as photocatalyst for solving energy crisis as well as environmental challenges, oxide thin film substrate and for manufacturing special oxygen sensors (high temperature) among others. Efficient utilization of strontium titanate as photocatalyst and opto-electronic device requires extension of light harvesting capacity beyond visible region through foreign material incorporation by doping mechanisms which is experimentally demanding and time consuming. This work models band gap of strontium titanate magnetic photocatalyst through extreme learning machine (ELM) using the crystal distortion (as a result of dopant incorporation) and crystallite size as predictors. The developed ELM-based models with sigmoid (Sig) and triangular basis (Tranba) activation function perform excellently better than the existing stepwise regression algorithm (SRA) model in the literature using different performance measuring parameters which include the coefficient of correlation (CC), mean absolute error and root mean square error. In the training developmental stage, the developed Sig-ELM model outperforms the developed Tranba-ELM and the existing SRA (2021) model with performance improvement of 2.96% and 67.37%, respectively, on the basis of CC performance metric, while similar performance improvement was obtained during testing phase of model development using different performance metrics. The superiority in the performance of the present models as compared to the existing model strengthens the potentials of the developed model in adjusting and extending light harvesting ability of strontium titanate semiconductor for various technological and industrial applications.https://www.tandfonline.com/doi/10.1080/23311916.2023.2232596extreme learning machinestrontium titanatecrystallite sizelattice parameterenergy gap |
| spellingShingle | Miloud Souiyah Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method Cogent Engineering extreme learning machine strontium titanate crystallite size lattice parameter energy gap |
| title | Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method |
| title_full | Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method |
| title_fullStr | Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method |
| title_full_unstemmed | Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method |
| title_short | Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method |
| title_sort | computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method |
| topic | extreme learning machine strontium titanate crystallite size lattice parameter energy gap |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2023.2232596 |
| work_keys_str_mv | AT miloudsouiyah computationalenergygapestimationforstrontiumtitanatephotocatalystusingextremelearningmachinemethod |