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Carbon nanotubes and fullerenes are allotropes of carbon characterized by a hollow structure and extraordinary thermal, electrical and mechanical properties. Spherical fullerenes are also called buckyballs, whereas cylindrical ones are known as nanotubes. The walls of these structures consist of a single layer of carbon atoms (graphene).
Non-volatile memory devices capable of recording and reading information at temperatures up to 600 °C can be built using aluminium scandium nitride ferroelectric diodes.
There is a lack of atomic level insight on the role of defects on carbon nanotubes' growth. Here, authors present a machine learning force field to drive near-microsecond simulations the entire growth process of this material, unveiling mechanisms of defect formation and healing.
Carbon nanotube-based single photon emitters allow for room-temperature operation, but suffer from vanishing indistinguishability due to strong dephasing. Following a theoretical proposal, the authors tackle the problem experimentally by using a cavity to enhance the photon coherence time and the emission spectral density in the regime of incoherent good cavity-coupling.
vdW assembly of low-dimensional materials has proven the capability of creating structures with on-demand properties. Here, the authors report on the structural collapse of CNTs in conjunction with a metal-semiconductor junction induced by the VdW encapsulation.
Non-volatile memory devices capable of recording and reading information at temperatures up to 600 °C can be built using aluminium scandium nitride ferroelectric diodes.
A 3D stackable computing-in-memory array that is based on resistive random-access memory could accelerate the implementation of machine learning algorithms.
Carbon nanotube transistors with high performance and integration density can be created using a full-contact structure to scale the nanotube–electrode contact length.