Abstract:We are reporting the atomic force microscope (AFM) nanomanipulation of ultrasonically dispersed and reflux-oxidized multiwalled carbon nanotubes (MWCNT) and single walled carbon nanotubes (SWCNT) by controlling the AFM tip with a NanoManipulator on a silicon substrate. The structure and the morphology of the carbon nanotubes (CNT) were confirmed with AFM interfaced with NanoManipulator and transmission electron microscope. The modifying parameter, which controls the force exerted by AFM tip, was set to be 0.5 … Show more
“…For an ideal perovskite, t ¼ 1. Most perovskites have 0:75<t<1:03 (Li et al, 2004;Kumar et al, 2008). Below this range the ABO 3 solid tends to adopt other structure types, e.g.…”
Section: Perovskite Featuresmentioning
confidence: 99%
“…Our stated task is of course one that has been studied for several years (Li et al, 2004;Zhang et al, 2007;Feng et al, 2008;Kumar et al, 2008). These studies performed a classification into perovskite or not in the traditional way by using a structure map.…”
Section: Introductionmentioning
confidence: 99%
“…A structure map (Mooser & Pearson, 1959) is a two-dimensional plot of the values of two features of the known solids and with lines drawn by a pencil and ruler that separate the data points into the two classes of crystal structures. The tolerance and octahedral factors are the two most widely used perovskite features in these plots (Li et al, 2004;Feng et al, 2008;Kumar et al, 2008). We asked new questions: can we improve the predictions by using more than two features?…”
Section: Introductionmentioning
confidence: 99%
“…Besides the tolerance and octahedral factor feature pairs, we also considered the A and B ionic radii (relative to the ionic radius of O; Kumar et al, 2008), the bond valence distances of A and B from O (Zhang et al, 2007), and the Mendeleev numbers for the A and B atoms (Sutton, 2003;Pettifor, 1995). The values of all these pairs are readily available from published tables.…”
We explored the use of machine learning methods for classifying whether a particular ABO3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2-3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.
“…For an ideal perovskite, t ¼ 1. Most perovskites have 0:75<t<1:03 (Li et al, 2004;Kumar et al, 2008). Below this range the ABO 3 solid tends to adopt other structure types, e.g.…”
Section: Perovskite Featuresmentioning
confidence: 99%
“…Our stated task is of course one that has been studied for several years (Li et al, 2004;Zhang et al, 2007;Feng et al, 2008;Kumar et al, 2008). These studies performed a classification into perovskite or not in the traditional way by using a structure map.…”
Section: Introductionmentioning
confidence: 99%
“…A structure map (Mooser & Pearson, 1959) is a two-dimensional plot of the values of two features of the known solids and with lines drawn by a pencil and ruler that separate the data points into the two classes of crystal structures. The tolerance and octahedral factors are the two most widely used perovskite features in these plots (Li et al, 2004;Feng et al, 2008;Kumar et al, 2008). We asked new questions: can we improve the predictions by using more than two features?…”
Section: Introductionmentioning
confidence: 99%
“…Besides the tolerance and octahedral factor feature pairs, we also considered the A and B ionic radii (relative to the ionic radius of O; Kumar et al, 2008), the bond valence distances of A and B from O (Zhang et al, 2007), and the Mendeleev numbers for the A and B atoms (Sutton, 2003;Pettifor, 1995). The values of all these pairs are readily available from published tables.…”
We explored the use of machine learning methods for classifying whether a particular ABO3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2-3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.
“…Atomic force microscopy (AFM) has been a widely used tool to perform various kinds of CNT manipulation. Both contact mode and non-contact mode could be used to translate, bend, roll, split or cut the nanotubes (Hyon et al 2005;Kim et al 2003;Kumar et al 2012;Postma et al 2000;Ziyong et al 2003), either by applying the tip bias (Hyon et al 2005;Kim et al 2003;Park et al 2002) or by exerting mechanical forces (Hertel et al 1998). Devices based on SWNTs were also fabricated through AFM manipulation such as single-electron transistors (Postma et al 2001), field-effect transistors (Avouris et al 1999), diodes (Jiao et al 2008), and so on.…”
In this paper, we report a technique of controlling the number of single-walled carbon nanotubes (SWNTs) between two electrodes. The SWNT devices consist of an array of SWNTs between two silver/palladium electrodes, which are fabricated with electron beam lithography and silver. Using the tip-sample interaction in contact mode of atomic force microscopy (AFM), the number of individual SWNTs can be controllably decreased. The current-voltage measurements indicate that the resistance increases when the conducting channels of SWNTs are reduced. This simple and controllable approach could be useful for the assembly of high performance devices with a desired number of channels for future electronic investigations.
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