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Volumn 7, Issue 1, 2007, Pages 45-50

DNA nucleoside interaction and identification with carbon nanotubes

Author keywords

[No Author keywords available]

Indexed keywords

DNA NUCLEOSIDES; GATE VOLTAGE; QUANTUM MECHANICAL CALCULATIONS; ULTRAFAST DNA SEQUENCING;

EID: 33846871006     PISSN: 15306984     EISSN: None     Source Type: Journal    
DOI: 10.1021/nl0619103     Document Type: Article
Times cited : (149)

References (32)
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    • MacKerell, A. D., Jr.; Bashford, D.; Bellott, R. L.; Dunbrack, R. L., Jr.; Evanseck, J. D.; Field, M. J.; Fischer, S.; Gao J.; Guo, H.; Ha, S.; Joseph-McCarthy, D.; Kuchnir, L.; Kuczera, K.; Lau, F. T. K.; Mattos, C.; Michnick, S.; Ngo, T.; Nguyen, D. T.; Prodhom, B.; Reiher, W. E., III; Roux, B.; Schlenkrich, M.; Smith, J. C.; Stote, R.; Straub, J.; Watanabe, M.; Wiorkiewicz-Kuczera, J.; Yin, D.; Karplus, M. J. Phys. Chem. B 1998, 102, 3586-3616.
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    • For these calculations, we employed a periodic supercell with dimensions of 20 Å × 20 Å × 3c. Larger supercells show minor differences in the structural and electronic features. A sampling of a 1 × 1 × 3 k-point mesh in reciprocal space is used to generate the charge density and the density of states. Gaussian smearing of electronic eigenvalues with a width of 0.1 eV is employed.
    • For these calculations, we employed a periodic supercell with dimensions of 20 Å × 20 Å × 3c. Larger supercells show minor differences in the structural and electronic features. A sampling of a 1 × 1 × 3 k-point mesh in reciprocal space is used to generate the charge density and the density of states. Gaussian smearing of electronic eigenvalues with a width of 0.1 eV is employed.
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    • (c) Wales, D. J. Science 2001, 293, 2067-2070.
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    • Wales, D.J.1
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    • Ph.D. Thesis, Ludwig-Maximilians Universität, München
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    • We used a standard feed-forward artificial neural network, the multilayer perceptron trained with the back-propagation algorithm and the leave-one-out cross-validation scheme implemented with the WEKA toolkit. Witten, I. H, Frank, E. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed, Morgan Kaufmann, San Francisco, 2005
    • We used a standard feed-forward artificial neural network, the multilayer perceptron trained with the back-propagation algorithm and the leave-one-out cross-validation scheme implemented with the WEKA toolkit. Witten, I. H.: Frank, E. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed.: Morgan Kaufmann, San Francisco, 2005.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.