Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area
The discovery of carbon nanotubes (CNT) by Sumiio Iijima in 1991 has attracted many researchers worldwide to study and explore thc newly found materials. CNT are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. Rcccnt reports suggest t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2004
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| Online Access: | http://eprints.utm.my/706/1/AhmadFauziIsmail2004_ArtificialNeuralNetworkModelingOf_.pdf |
| _version_ | 1825908956455763968 |
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| author | Razak, N.A. Arshad, K.A. Rahman , A.A. Ismail , A.F. Sanip, S.M. |
| author_facet | Razak, N.A. Arshad, K.A. Rahman , A.A. Ismail , A.F. Sanip, S.M. |
| author_sort | Razak, N.A. |
| collection | ePrints |
| description | The discovery of carbon nanotubes (CNT) by Sumiio Iijima in 1991 has attracted many researchers worldwide to study and explore thc newly found materials. CNT are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. Rcccnt reports suggest that total surfacc area of carbon affect the hydrogen storage capacities in carbon nanotubes. An Artificial Neural Network model was created to study the relationship between the surface area of carbon and the hydrogen adsorption. |
| first_indexed | 2024-03-05T17:54:59Z |
| format | Article |
| id | utm.eprints-706 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T17:54:59Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | utm.eprints-7062017-09-06T06:39:38Z http://eprints.utm.my/706/ Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area Razak, N.A. Arshad, K.A. Rahman , A.A. Ismail , A.F. Sanip, S.M. TP Chemical technology The discovery of carbon nanotubes (CNT) by Sumiio Iijima in 1991 has attracted many researchers worldwide to study and explore thc newly found materials. CNT are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. Rcccnt reports suggest that total surfacc area of carbon affect the hydrogen storage capacities in carbon nanotubes. An Artificial Neural Network model was created to study the relationship between the surface area of carbon and the hydrogen adsorption. 2004 Article NonPeerReviewed application/pdf en http://eprints.utm.my/706/1/AhmadFauziIsmail2004_ArtificialNeuralNetworkModelingOf_.pdf Razak, N.A. and Arshad, K.A. and Rahman , A.A. and Ismail , A.F. and Sanip, S.M. (2004) Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area. Proceedings of Advances in Fuel Cell Research and Development in Malaysia . pp. 165-169. |
| spellingShingle | TP Chemical technology Razak, N.A. Arshad, K.A. Rahman , A.A. Ismail , A.F. Sanip, S.M. Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area |
| title | Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area |
| title_full | Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area |
| title_fullStr | Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area |
| title_full_unstemmed | Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area |
| title_short | Artificial Neural Network Modeling Of Hydrogen Uptake Based On Carbon Surface Area |
| title_sort | artificial neural network modeling of hydrogen uptake based on carbon surface area |
| topic | TP Chemical technology |
| url | http://eprints.utm.my/706/1/AhmadFauziIsmail2004_ArtificialNeuralNetworkModelingOf_.pdf |
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