Effect of adsorbent loading on NaNiRu-DFMs' CO2 capture and methanation: finding optimal Na-loading using Bayesian optimisation guided experiments

Abstract

Designing dual function materials (DFMs) entails an optimisation of CO₂ adsorption and catalytic conversion activity, often requiring a large number of experimental parametric studies screening various types and loadings of adsorbent and catalyst components. In this study, we used a Gaussian process model optimised with Bayesian optimisation (BO) to find the DFM composition leading to the highest methanation activity. We focused on optimising Na (adsorbent) loading in a DFM where Na loading was varied from 2.5–15% by weight. The results from the experimental tests indicated that the sample with the highest Na-loading (15 wt%) possessed the highest CO₂ desorption during CO₂-TPD, however, it was not the best DFM, as it did not show the highest methane production. By testing Bayesian optimisation recommended experiments we identified 7.9 wt% Na as the optimal Na loading, which showed the highest methane production for a cycle (398.6 μmol g_DFM⁻¹) at 400 °C. This forms a case study for how BO can help accelerate materials discovery for DFMs.

Keywords: DFM; ICCC; Methanation; Gaussian process; Bayesian optimisation.