Symposium GI01: Machine Learning and Data-Driven Materials Development and Design

Placidus Amama, Kansas State University

Industrial Waste Gas mixture as a feedstock in Efficient Carbon Nanotube Growth—Using an Autonomous Research System to Probe Growth Kinetics and Mechanisms

Written by Daniel Gregory

Aligned carbon nanotubes in “carpet” or “forest” arrays are highly desired in a range of applications, but require controlled properties and efficient production at large scale with low cost to meet this demand. Placidus Amama first explained the complexities of the Fischer-Tropsch synthesis method for liquid fuels, highlighting the inability to have 100% liquid fuel selectivity. From this, he described his group’s approach to the problem, wherein the gas phase of the synthesis could be combined with chemical vapor deposition to produce carbon nanotubes, alongside the liquid hydrocarbons. After showing favorable comparisons against other leading synthetic methods including carpet density, growth lifetime, and yield, Amama explained the group’s use of an autonomous research system (ARES) to optimize selected parameters. The system uses laser light to both heat the samples for chemical vapor deposition, and characterize them using Raman spectroscopy. A detailed description of the effect of heat and water content on yield was presented alongside microscopy characterizations of the optimized reaction conditions. Amama then briefly described the proposed mechanism for this reaction based on the collected data and some results from experiments changing the composition of the gas feedstock before concluding with future plans to use the ARES to further probe these compositional changes.