Abstract
Materials and their synthesis play pivotal roles in energy storage and conversion. In particular, carbon nanotubes (CNTs) have exhibited potential due to their unique multifunctionality, positioning them as promising elements in this field. However, despite advance in the application of CNTs, challenges persist in both synthesis and processing. The advent of artificial intelligence (AI) has emerged as a transformative tool, empowering researchers and showing promise to expedite scientific breakthroughs. In this presentation, I will briefly discuss our development of a two-stage CNT synthesis process. In addition, I will discuss the application of AI to augment researchers’ capabilities in synthesis and processing. As examples, I will discuss our effort to apply a data-driven approach to attempt to break through synthetic trade-offs in single wall CNT forest growth. I will also discuss our development of a tool to determine dispersants for CNT processing.
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