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Current–voltage modeling of graphene-based DNA sensor
Graphene is considered as an excellent biosensing material due to its outstanding and unique electronic properties such as providing large area detection, ultra-high mobility and ambipolar field-effect characteristic. In this paper, general conductance model of DNA sensor-based graphene is obtained,...
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| Published in: | Neural computing & applications 2014, Vol.24 (1), p.85-89 |
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| Main Authors: | , , , , , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c318t-436abf9c0056df08aaedd4833d232e7a4cbf3b5083d993efca3b21e0398c5f13 |
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| cites | cdi_FETCH-LOGICAL-c318t-436abf9c0056df08aaedd4833d232e7a4cbf3b5083d993efca3b21e0398c5f13 |
| container_end_page | 89 |
| container_issue | 1 |
| container_start_page | 85 |
| container_title | Neural computing & applications |
| container_volume | 24 |
| creator | Karimi Feiz Abadi, H. Yusof, R. Maryam Eshrati, S. Naghib, S. D. Rahmani, M. Ghadiri, M. Akbari, E. Ahmadi, M. T. |
| description | Graphene is considered as an excellent biosensing material due to its outstanding and unique electronic properties such as providing large area detection, ultra-high mobility and ambipolar field-effect characteristic. In this paper, general conductance model of DNA sensor-based graphene is obtained, and the electrical performance of nanostructured graphene-based DNA sensor is evaluated by the current–voltage characteristic. As a result, by increasing the complementary DNA concentration, the drain current is going toward higher amounts. |
| doi_str_mv | 10.1007/s00521-013-1464-1 |
| format | article |
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As a result, by increasing the complementary DNA concentration, the drain current is going toward higher amounts.</description><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Biological and medical sciences</subject><subject>Biosensors</subject><subject>Biotechnology</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Electronics</subject><subject>Exact sciences and technology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Genetic engineering</subject><subject>Genetic technics</subject><subject>Iconip 2012</subject><subject>Image Processing and Computer Vision</subject><subject>In vitro gene amplification. Pcr technique</subject><subject>Methods. Procedures. 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| ispartof | Neural computing & applications, 2014, Vol.24 (1), p.85-89 |
| issn | 0941-0643 1433-3058 |
| language | eng |
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| source | Springer Nature |
| subjects | Applied sciences Artificial Intelligence Biological and medical sciences Biosensors Biotechnology Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Electronics Exact sciences and technology Fundamental and applied biological sciences. Psychology Genetic engineering Genetic technics Iconip 2012 Image Processing and Computer Vision In vitro gene amplification. Pcr technique Methods. Procedures. Technologies Probability and Statistics in Computer Science Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices Transistors Various methods and equipments |
| title | Current–voltage modeling of graphene-based DNA sensor |
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