Enhancing the performance of 3D printed bio-photoelectrochemical cells through multi-objective Bayesian optimisation

Abstract

Attaching thylakoid membranes (TM) on bio-photo-electrochemical cells (BPEC) enables energy harvesting through photoelectrode extraction. However, the attachment methods rely on thin coating methods such as dip-coating, drop-casting or electrospray deposition. We herein demonstrate the use of direct ink writing in coating TM on BPEC cells, aiming for rapid prototyping and mass production of BPEC cells. As photoelectron extraction through high TM loadings is not feasible, we investigate previously reported conducting materials to be used in a mixture with TM. The conductive TM composite ink, referred to as BPEC ink in this study, is optimised through multi-objective Bayesian optimisation (MOBO) with the two objectives of maximising current density and maximising printability. Fourteen initial searches were taken, followed by 15 MOBO searches. We confirm that after MOBO, current density and printability were enhanced by 162% and 149%, respectively. Using the optimised BPEC ink, we demonstrate the 3D printing of fully integrated BPEC cells arranged in series.

Keywords

• Photosynthetic electrons
• 3D printing
• multi-objective Bayesian optimisation
• energy conversion device