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.

An automated synthesis instrument comprising a series of continuous flow modules that are radially arranged around a central switching station can achieve both linear and convergent syntheses.

Automated synthesis platforms accelerate and simplify the preparation of molecules by removing the physical barriers to organic synthesis. This provides unrestricted access to biopolymers and small molecules via reproducible and directly comparable chemical processes. Current automated multistep syntheses rely on either iterative(1-4) or linear processes(5-9), and require compromises in terms of versatility and the use of equipment. Here we report an approach towards the automated synthesis of small molecules, based on a series of continuous flow modules that are radially arranged around a central switching station. Using this approach, concise volumes can be exposed to any reaction conditions required for a desired transformation. Sequential, non-simultaneous reactions can be combined to perform multistep processes, enabling the use of variable flow rates, reuse of reactors under different conditions, and the storage of intermediates. This fully automated instrument is capable of both linear and convergent syntheses and does not require manual reconfiguration between different processes. The capabilities of this approach are demonstrated by performing optimizations and multistep syntheses of targets, varying concentrations via inline dilutions, exploring several strategies for the multistep synthesis of the anticonvulsant drug rufinamide(10), synthesizing eighteen compounds of two derivative libraries that are prepared using different reaction pathways and chemistries, and using the same reagents to perform metallaphotoredox carbon-nitrogen cross-couplings(11) in a photochemical module-all without instrument reconfiguration.