(370p) Model-Based Optimization of Laser-Reduced Graphene with Sparse Datasets | AIChE

(370p) Model-Based Optimization of Laser-Reduced Graphene with Sparse Datasets


Johnson, P. A. - Presenter, University of Wyoming
Tyrrell, A., University of Wyoming
Jain, V., University of Wyoming
Kotthoff, L., University of Wyoming
Graphene has excellent physical and chemical properties, including outstanding electrical properties and ease of functionalization. Laser reduction of graphene oxide and polymers is a chemical-free and scalable approach to producing and writing graphene circuits on solid and flexible substrates. The local confinement of the writing process can be exploited for selective doping of heteroatoms such as boron or nitrogen, which enhances the electronic structure and electrochemical properties in graphene. Here, we report the fabrication of laser-reduced doped graphene patterns in high pressure. We investigate strain effects on the electrical properties of graphene circuits on flexible substrates. We utilize in-situ Raman and optical analysis with a view to automate the manufacturing process in a closed-feedback loop. Furthermore, we explore our multidimensional parameter space using model-based search. In particular, we use automated parameter tuning to optimize the fabrication of laser-reduced graphene circuits. We demonstrate effective and rapid optimizations even with sparse data and discuss the integration of machine learning in advanced manufacturing.