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Artificial neural network modelling for the prediction of carbon surface area

Carbon nanotubes are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. Recent reports suggest that total surface area of carbon nanotubes affect the hydrogen storage capacities in carbon nanotubes. An Artificial Neural Network model has...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Abd. Razak, Norhuda, Arshad, Khairil Anuar, Abd. Rahman, Ali, M. Sanip, Suhaila, Ismail, Ahmad Fauzi
التنسيق: Conference or Workshop Item
اللغة:English
منشور في: 2004
الموضوعات:
T Technology (General)
الوصول للمادة أونلاين:http://eprints.utm.my/id/eprint/6079/1/NorhudaAbd.Razak2004_ArtificialNeuralNetworkModelling.pdf
http://eprints.utm.my/id/eprint/6079/
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id my.utm.6079
record_format eprints
spelling my.utm.60792010-06-01T15:38:39Z http://eprints.utm.my/id/eprint/6079/ Artificial neural network modelling for the prediction of carbon surface area Abd. Razak, Norhuda Arshad, Khairil Anuar Abd. Rahman, Ali M. Sanip, Suhaila Ismail, Ahmad Fauzi T Technology (General) Carbon nanotubes are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. Recent reports suggest that total surface area of carbon nanotubes affect the hydrogen storage capacities in carbon nanotubes. An Artificial Neural Network model has been created for the prediction of the surface area of carbon. The model is used to study the influence of the different type of carbon on the hydrogen storage properties of carbon nanotubes. 2004 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/6079/1/NorhudaAbd.Razak2004_ArtificialNeuralNetworkModelling.pdf Abd. Razak, Norhuda and Arshad, Khairil Anuar and Abd. Rahman, Ali and M. Sanip, Suhaila and Ismail, Ahmad Fauzi (2004) Artificial neural network modelling for the prediction of carbon surface area. In: The XXI Regional Conference on Solid State Science & Technology 2004, 12-13 October 2004, Hyatt Regency Kinabalu, Sabah, Malaysia.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Abd. Razak, Norhuda
Arshad, Khairil Anuar
Abd. Rahman, Ali
M. Sanip, Suhaila
Ismail, Ahmad Fauzi
Artificial neural network modelling for the prediction of carbon surface area
description Carbon nanotubes are considered for hydrogen storage due to their low density, high strength, and hydrogen adsorption characteristics. Recent reports suggest that total surface area of carbon nanotubes affect the hydrogen storage capacities in carbon nanotubes. An Artificial Neural Network model has been created for the prediction of the surface area of carbon. The model is used to study the influence of the different type of carbon on the hydrogen storage properties of carbon nanotubes.
format Conference or Workshop Item
author Abd. Razak, Norhuda
Arshad, Khairil Anuar
Abd. Rahman, Ali
M. Sanip, Suhaila
Ismail, Ahmad Fauzi
author_facet Abd. Razak, Norhuda
Arshad, Khairil Anuar
Abd. Rahman, Ali
M. Sanip, Suhaila
Ismail, Ahmad Fauzi
author_sort Abd. Razak, Norhuda
title Artificial neural network modelling for the prediction of carbon surface area
title_short Artificial neural network modelling for the prediction of carbon surface area
title_full Artificial neural network modelling for the prediction of carbon surface area
title_fullStr Artificial neural network modelling for the prediction of carbon surface area
title_full_unstemmed Artificial neural network modelling for the prediction of carbon surface area
title_sort artificial neural network modelling for the prediction of carbon surface area
publishDate 2004
url http://eprints.utm.my/id/eprint/6079/1/NorhudaAbd.Razak2004_ArtificialNeuralNetworkModelling.pdf
http://eprints.utm.my/id/eprint/6079/
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score 13.250246

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