AI-Driven Process Optimization

To ensure consistent quality of nanomaterial products, Epic Advanced Materials uses a data-driven process optimization approach to rapidly explore the vast parameter spaces common in nanomaterial synthesis. Epic aims to create a robust “digital twin” of their synthesis machines – a virtual environment that can simulate the manufacturing processes, suggest production improvements, and improve quality assurance of produced materials. Epic is currently developing a platform of machine learning and computer vision tools to help move laboratory-proven high-sensitivity synthesis processes to the industrial scale and lower the barrier to entry for commercial nanomaterial adoption.

Consistent High Purity BNNT Production

The nature of the EPIC synthesis process allows for the production of high quality and consistent BNNTs that do not suffer from metal catalyst contamination, structural defects, or inefficient reactant conversion. The process is also remarkably scalable, allowing for high product yields of double-walled and multi-walled BNNTs. Contact us for more information on our BNNT product offerings.

What are BNNTs?

Boron Nitride Nanotubes (BNNTs) are nanoparticles that are structurally analogous to carbon nanotubes but consist of alternating atoms of boron and nitrogen. Their existence was first theorized by Dr. Alex Zettl’s group in 1994 and since then significant research and development work has been performed in an effort to better characterize and understand BN nanomaterials and their promising applications. Though a large body of work now exists describing the numerous applications and opportunities for BNNTs to revolutionize the field of materials science, they are still not very well-known and have not yet been widely adopted in industry, due primarily to low efficiency production methods and high costs for the material. Epic Advanced Materials aims to leverage a new and patented synthesis process and a platform of machine learning tools to deliver BNNTs in greater volume and at much lower cost than what is currently possible, enabling the next generation of advanced material technologies.