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Svensson, Johannes,Sourab, Abdelrahim A,Tarakanov, Yury,Lee, Dong Su,Park, Seung Joo,Baek, Seung Jae,Park, Yung Woo,Campbell, Eleanor E B IOP Pub 2009 Nanotechnology Vol.20 No.17
<P>Direct measurements are presented of the Schottky barrier (SB) heights of carbon nanotube devices contacted with Pd electrodes. The SB barrier heights were determined from the activation energy of the temperature-dependent thermionic emission current in the off-state of the devices. The barrier heights generally decrease with increasing diameter of the nanotubes and they are in agreement with the values expected when assuming little or no influence of Fermi level pinning. </P>
Carbon nanotube field effect transistors with suspended graphene gates.
Svensson, Johannes,Lindahl, Niklas,Yun, Hoyeol,Seo, Miri,Midtvedt, Daniel,Tarakanov, Yury,Lindvall, Niclas,Nerushev, Oleg,Kinaret, Jari,Lee, Sangwook,Campbell, Eleanor E B American Chemical Society 2011 Nano letters Vol.11 No.9
<P>Novel field effect transistors with suspended graphene gates are demonstrated. By incorporating mechanical motion of the gate electrode, it is possible to improve the switching characteristics compared to a static gate, as shown by a combination of experimental measurements and numerical simulations. The mechanical motion of the graphene gate is confirmed by using atomic force microscopy to directly measure the electrostatic deflection. The device geometry investigated here can also provide a sensitive measurement technique for detecting high-frequency motion of suspended membranes as required, e.g., for mass sensing.</P>
Podolsky, Maxim D,Barchuk, Anton A,Kuznetcov, Vladimir I,Gusarova, Natalia F,Gaidukov, Vadim S,Tarakanov, Segrey A Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.2
Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.