Sequential Bayesian-optimized graphene synthesis by direct solar-thermal chemical vapor deposition

Abstract

This work reports the use of a high-flux solar simulator that mimics the solar spectrum and a cold-wall CVD reactor to demonstrate the feasibility of utilizing a renewable energy resource in synthesizing graphene under various conditions. A parametric study of process parameters was carried out using a probabilistic Bayesian regression model and an information acquisition function to find conditions that yield high-quality product. Backscattered electron images and Raman mapping were used to assess the effects of growth conditions on graphene characteristic sizes, film quality, and uniformity. We report the synthesis of high-quality single-layer graphene (SLG) and AB-stacked bilayer graphene films in a one-step, short-time process with \(I_{D}/I_{G}\) ratios of 0.21 and 0.14, respectively. Electron diffraction analysis shows peak intensities that resemble SLG and AB-bilayer graphene with up to 5 and 20 \(\mathrm{\mu}\) m grain sizes, respectively. The optical transmissivities of SLG and AB-bilayer graphene fall between 0.959-0.977 and 0.929-0.953, whereas the sheet resistances measured by a 4-point probe with 1 mm spacing are 15.5 \(\pm\) 4.6 and 3.4$\pm$1.5 k \(\Omega\) /sq, respectively. Further scale-up of the optimized graphene growth area was achieved by flattening the insolation profile, leading to spatial uniformity up to 15 mm in radius. Direct solar capture for CVD synthesis enable a practical and sustainable option for synthesizing graphene films applicable for photonic and electronic applications.