资源环境科技发展态势分析平台

Graphene films grown by chemical vapour deposition have unusual physical and chemical properties that offer promise for applications such as flexible electronics and high-frequency transistors(1-10). However, wrinkles invariably form during growth because of the strong coupling to the substrate, and these limit the large-scale homogeneity of the film(1-4,11,12). Here we develop a proton-assisted method of chemical vapour deposition to grow ultra-flat graphene films that are wrinkle-free. Our method of proton penetration(13-17) and recombination to form hydrogen can also reduce the wrinkles formed during traditional chemical vapour deposition of graphene. Some of the wrinkles disappear entirely, owing to the decoupling of van der Waals interactions and possibly an increase in distance from the growth surface. The electronic band structure of the as-grown graphene films shows a V-shaped Dirac cone and a linear dispersion relation within the atomic plane or across an atomic step, confirming the decoupling from the substrate. The ultra-flat nature of the graphene films ensures that their surfaces are easy to clean after a wet transfer process. A robust quantum Hall effect appears even at room temperature in a device with a linewidth of 100 micrometres. Graphene films grown by proton-assisted chemical vapour deposition should largely retain their intrinsic performance, and our method should be easily generalizable to other nanomaterials for strain and doping engineering.

The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy(1,2). Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy(3-8). Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products(9-16), and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 +/- 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction(17). Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C-C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.