资源环境科技发展态势分析平台

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.

Protein crystallization is important in structural biology, disease research and pharmaceuticals. It has recently been recognized that nonclassical crystallization involving initial formation of an amorphous precursor phase-occurs often in protein, organic and inorganic crystallization processes(1-5). A two-step nucleation theory has thus been proposed, in which initial low-density, solvated amorphous aggregates subsequently densify, leading to nucleation(4,6,7). This view differs from classical nucleation theory, which implies that crystalline nuclei forming in solution have the same density and structure as does the final crystalline state(1). A protein crystallization mechanism involving this classical pathway has recently been observed directly(8). However, a molecular mechanism of nonclassical protein crystallization(9-15) has not been established(9,11,14). To determine the nature of the amorphous precursors and whether crystallization takes place within them (and if so, how order develops at the molecular level), three-dimensional (3D) molecular-level imaging of a crystallization process is required. Here we report cryogenic scanning transmission microscopy tomography of ferritin aggregates at various stages of crystallization, followed by 3D reconstruction using simultaneous iterative reconstruction techniques to provide a 3D picture of crystallization with molecular resolution. As crystalline order gradually increased in the studied aggregates, they exhibited an increase in both order and density from their surface towards their interior. We observed no highly ordered small structures typical of a classical nucleation process, and occasionally we observed several ordered domains emerging within one amorphous aggregate, a phenomenon not predicted by either classical or two-step nucleation theories. Our molecular-level analysis hints at desolvation as the driver of the continuous order-evolution mechanism, a view that goes beyond current nucleation models, yet is consistent with a broad spectrum of protein crystallization mechanisms.