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.

The taste of sugar is one of the most basic sensory percepts for humans and other animals. Animals can develop a strong preference for sugar even if they lack sweet taste receptors, indicating a mechanism independent of taste(1-3). Here we examined the neural basis for sugar preference and demonstrate that a population of neurons in the vagal ganglia and brainstem are activated via the gut-brain axis to create preference for sugar. These neurons are stimulated in response to sugar but not artificial sweeteners, and are activated by direct delivery of sugar to the gut. Using functional imaging we monitored activity of the gut-brain axis, and identified the vagal neurons activated by intestinal delivery of glucose. Next, we engineered mice in which synaptic activity in this gut-to-brain circuit was genetically silenced, and prevented the development of behavioural preference for sugar. Moreover, we show that co-opting this circuit by chemogenetic activation can create preferences to otherwise less-preferred stimuli. Together, these findings reveal a gut-to-brain post-ingestive sugar-sensing pathway critical for the development of sugar preference. In addition, they explain the neural basis for differences in the behavioural effects of sweeteners versus sugar, and uncover an essential circuit underlying the highly appetitive effects of sugar.

Experiments in mice show that a population of neurons in the vagal ganglia respond to the presence of glucose in the gut and connect to neurons in the brainstem, revealing the circuit that underlies the neural basis for the behavioural preference for sugar.