Analytical development and optimization of a graphene–solution interface capacitance model
Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and fa...
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| Published in | Beilstein journal of nanotechnology Vol. 5; no. 1; pp. 603 - 609 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Germany
Beilstein-Institut
09.05.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2190-4286 2190-4286 |
| DOI | 10.3762/bjnano.5.71 |
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| Abstract | Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of
E
composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy. |
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| AbstractList | Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy. Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy.Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy. Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy. |
| Author | Hadiyan, Moein Karimi, Hediyeh Rahmani, Rasoul Shirdel, Amir H Haghighian, Niloofar Mashayekhi, Reza Ismail, Razali Movahedi, Parisa Ranjbari, Leyla |
| AuthorAffiliation | 7 Department of Information Technology, University of Turku , 20014 Turku, Finland 1 Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia 2 Malaysia Japan International Ins. Of Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia 3 Faculty of Electrical Engineering, khayyam higher education Institute, 9189747178, Mashhad, Iran 6 Department of physics and CNISM, University of Genova, Via Dodecaneso 33, 16146 Genova, Italy 4 Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Malaysia 8 Department of Electrical and computer engineering, K. N. Toosi University of Technology, Tehran, Iran 9 Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Johor, Malaysia 5 Department of Chemical Engineering, Åbo Akademi University, 20500 Åbo, Finland |
| AuthorAffiliation_xml | – name: 5 Department of Chemical Engineering, Åbo Akademi University, 20500 Åbo, Finland – name: 3 Faculty of Electrical Engineering, khayyam higher education Institute, 9189747178, Mashhad, Iran – name: 1 Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia – name: 2 Malaysia Japan International Ins. Of Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia – name: 4 Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Malaysia – name: 7 Department of Information Technology, University of Turku , 20014 Turku, Finland – name: 8 Department of Electrical and computer engineering, K. N. Toosi University of Technology, Tehran, Iran – name: 9 Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Johor, Malaysia – name: 6 Department of physics and CNISM, University of Genova, Via Dodecaneso 33, 16146 Genova, Italy |
| Author_xml | – sequence: 1 givenname: Hediyeh surname: Karimi fullname: Karimi, Hediyeh – sequence: 2 givenname: Rasoul surname: Rahmani fullname: Rahmani, Rasoul – sequence: 3 givenname: Reza surname: Mashayekhi fullname: Mashayekhi, Reza – sequence: 4 givenname: Leyla surname: Ranjbari fullname: Ranjbari, Leyla – sequence: 5 givenname: Amir H surname: Shirdel fullname: Shirdel, Amir H – sequence: 6 givenname: Niloofar surname: Haghighian fullname: Haghighian, Niloofar – sequence: 7 givenname: Parisa surname: Movahedi fullname: Movahedi, Parisa – sequence: 8 givenname: Moein surname: Hadiyan fullname: Hadiyan, Moein – sequence: 9 givenname: Razali surname: Ismail fullname: Ismail, Razali |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24991496$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1038/nature11458 10.1021/ci049610z 10.2174/1877946811303030006 10.1039/b923596e 10.1073/pnas.0502848102 10.1016/j.tcs.2005.05.020 10.1016/j.ejor.2006.06.046 10.1021/nl201655c 10.1073/pnas.1018388108 10.1016/j.jweia.2013.10.004 10.1016/j.ssc.2008.02.024 10.1103/PhysRevLett.99.186801 10.1063/1.1803614 10.1103/PhysRevB.77.115436 10.1126/science.1102896 10.1038/nnano.2009.177 10.1103/PhysRevB.79.241406 10.1109/ICCAD.2008.4681637 10.1109/3477.484436 10.1140/epjb/e2011-20225-8 10.1038/nnano.2011.129 10.1103/PhysRevB.76.113406 10.1088/0957-4484/18/35/355709 10.1038/nnano.2009.194 10.1109/MCI.2006.329691 10.1109/TMAG.2006.878852 10.1021/jp2030768 10.1021/ja104850n 10.1209/0295-5075/80/47001 10.1002/adma.201306041 10.1038/nnano.2010.275 10.1109/TCSI.2007.907835 10.1166/sam.2012.1405 10.1109/ICSICT.2008.4734555 10.1103/PhysRevLett.98.206805 10.1109/4235.585892 10.1109/TED.2007.902692 10.1109/TED.2007.891872 |
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| Keywords | quantum capacitance analytical modeling ant colony optimization (ACO) graphene electrolyte-gated transistors (EGFET) |
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| SubjectTerms | analytical modeling ant colony optimization (ACO) electrolyte-gated transistors (EGFET) Full Research Paper graphene Nanoscience Nanotechnology quantum capacitance |
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| Title | Analytical development and optimization of a graphene–solution interface capacitance model |
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