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Recent experimental and theoretical results on intrinsic superconductivity in ropes of single-wall carbon nanotubes are reviewed and compared. We find strong experimental evidence for superconductivity when the distance between the normal electrodes is large enough. This indicates the presence of attractive phonon-mediated interactions in carbon nanotubes, which can even overcome the repulsive Coulomb interactions. The effective low-energy theory of rope superconductivity explains the experimental results on the temperature-dependent resistance below the transition temperature in terms of quantum phase slips. Quantitative agreement with only one fit parameter can be obtained. Nanotube ropes thus represent superconductors in an extreme 1D limit never explored before.
We have altered the superconductivity of a suspended rope of single walled carbon nanotubes, by coating it with organic polymers. Upon coating, the normal state resistance of the rope changes by less than 20 percent. But superconductivity, which on t
We show that carbon nanotubes (CNT) are good candidates for realizing one-dimensional topological superconductivity with Majorana fermions localized near the end points. The physics behind topological superconductivity in CNT is novel and is mediated
We report Meissner effect for type-II superconductors with a maximum Tc of 19 K, which is the highest value among those in new-carbon related superconductors, found in the honeycomb arrays of multi-walled CNTs (MWNTs). Drastic reduction of ferromagne
We report that entirely end-bonded multi-walled carbon nanotubes (MWNTs) can show superconductivity with the transition temperature Tc as high as 12K that is approximately 40-times larger than those reported in ropes of single-walled nanotubes. We fi
Magnetization and resistance measurements were carried out on carbon-based multiwall nanotubes. Both magnetization and resistance data can be consistently explained in terms of bulk superconductivity above 400 K although we cannot completely rule out other possible explanations to the data.