Nutrient diffusion-inspired catalysts with self-reinforced concentration gradient for sustainable electroreduction of dilute CO2
Year: 2025
Type: article
Abstract: The electrocatalysis of flue gas into CO in membrane electrode assembly (MEA) provides a sustainable route for realizing practical CO2 electrolysis technology but suffers from restricted CO2 mass transport due to thick gas boundary layer (GBL) and weak concentration gradient. Inspired by nutrient diffusion mechanism in plant, we introduce the concept of self-reinforced CO2 concentration gradient, which is realized via porous carbon nanosheets (PC) as soil for enriching CO2 and single-atomic Ni-doped carbon nanotubes (Ni-CNTs) as rhizome for electro-catalyzing CO2. A combined experimental and simulation study reveals optimal length of Ni-CNTs on PC reduces the GBL thickness and spontaneously enhances CO2 concentration gradient, synergistically breaking the limitation of CO2 transport. Consequently, the CO Faradaic efficiency attains >90% with varying CO2 concentration of 4-15 vol. % CO2 in MEA. Further through incorporation of an O2-adsorption packed column before MEA, we realize the stable and selective conversion of O2-containing flue gas into CO. The dilute CO2 electrocatalysis suffers from low selectivity in membrane electrode assembly. Here, the authors report the design of self-reinforced CO2 concentration gradient that breaks CO2 mass transport limit, realizing direct and selective conversion of simulated flue gas in membrane electrode assembly.
Source: Nature Communications
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: National Natural Science Foundation of China, Natural Science Foundation of Tianjin City, Fundamental Research Funds for the Central Universities, Overseas Expertise Introduction Center for Discipline Innovation of Food Nutrition and Human Health (111 Center), Beijing National Laboratory for Molecular Sciences
Awards: G1505768483, G3156332808, G4598227957
OpenAlex ID: W4413257241
