Governing Factors for Carbon Nanotube Dispersion in Organic Solvents Estimated by Machine Learning

The insolubility of single-walled carbon nanotubes (SWCNTs) in most common organic solvents has been the cause of a bottleneck in their practical utilization. Aqueous SWCNT inks containing amphiphilic surfactants are widely used for processing including coatings and composite fabrication. Most practical processes are, however, designed to be compatible with organic solvents, generating a technological mismatch between production and utilization. This work reports on the surfactant-assisted dispersion of SWCNTs in useful organic solvents, at up to quantitative yields. A feature extraction based on machine learning offers seemingly important, highly intuitive physicochemical factors that lead to efficient dispersion. These elucidated factors are associated not only with solvents-surfactant, but also solvent-SWCNT interactions. The organic SWCNT dispersion as well as its research methodology developed here may find widespread applications ranging from nanofluidics to functional materials design.

Automated summary

This study developed a method for dispersing SWCNTs in organic solvents using surfactants, achieving high dispersion efficiency. Feature extraction based on machine learning revealed important physicochemical factors that affect the dispersion, which are related to both surfactant and solvent-SWCNT interactions. The organic SWCNT dispersion method developed in this study may have wide applications in nanofluidics and functional materials design.