Supramolecular-Based One-Pot Separation of Highly Pure Adsorbent-Free Semiconducting Single-Walled Carbon Nanotubes and Machine Learning-Based Nanotube Solubilization

Abstract

Carbon nanotubes are classified into single-walled carbon nanotubes (SWNTs), double-walled carbon nanotubes and multiwalled carbon nanotubes. Among these, SWNTs have remarkable electronic, mechanical, optical, chemical and thermal properties, which are derived from their one-dimensional extended π-conjugated structures, and thus, they demonstrate a high potential toward the development of the next-generation nanoelectronics, (nano)bio, and energy and environmental materials and devices. As-produced SWNTs are a mixture of semiconducting (sem-) and metallic (met-)-SWNTs; thus, chirality sorting is highly important. So far various methods have been presented for such a separation including (i) use of chemical adsorbents such as polyfluorenes (PFOs) and their analogues and (ii) physical methods including surfactant-aided density gradient ultracentrifugation (DGU), gel chromatography techniques, and the surfactant-aided aqueous two-phase extraction method. However, such methods are not simple, and the removal of the wrapped adsorbents on the SWNTs is very difficult. Thus, the development of a method to remove the adsorbent from the sorted SWNTs is highly important to obtain adsorbent-free pure sem-SWNTs.

In this Account, we provide a summary of a one-pot highly efficient sem-SWNT sorting using a solubilizer and removal of the wrapped solubilizer/adsorbent from the surfaces of the sorted tubes to provide highly pure adsorbent-free sem-SWNTs, in which the design and synthesis of adsorbents that selectively sorts sem-SWNTs from as-produced SWNTs, a mixture of sem-SWNTs and met-SWNTs, with easy removal property by a suitable method are described. In particular, we demonstrate a solubilizer-free sem-SWNT sorting based on supramolecular chemistry. The development of easy/simple and an efficient adsorbent-free sem-SWNT sorting method is highly important for proper fundamental use and application of SWNTs in industry. In addition, we describe computer simulations for selective sem-SWNT sorting based on a DFT method; in particular, we summarize our density functional theory (DFT) approach for helical wrapping of flavin molecules on the (8,6)-SWNT, leading to successful SWNT chirality separation. Finally, the introduction of a machine learning approach for SWNT solubilization is summarized.