Work

Composition-Controlled Cu/Cu3N Nanoparticles via Cylindrical Magnetron Sputtering for Multicarbon CO2 Electroreduction

Year: 2026
Type: article
Abstract: Electrochemical CO2 reduction (eCO2R) represents an important strategy to mitigate CO2 emissions while yielding value-added multicarbon (C2+) products, such as ethylene and ethanol. Copper nitride (Cu3N) nanoparticles (NPs) have emerged as attractive catalysts for eCO2R; however, their synthesis remains challenging and is typically limited to multistep liquid phase methods. This work demonstrates an efficient gas-phase approach for the compositional and structural tuning of Cu3N-based NPs by using cylindrical magnetron reactive sputtering with controlled N2/Ar gas mixtures. By varying nitrogen incorporation, the NPs exhibit a controlled transition from metallic Cu to biphasic Cu/Cu3N and ultimately to stoichiometric Cu3N, while maintaining a nearly constant NP size (20–27 nm). Structural characterization combining HR-TEM, Doppler broadening variable-energy positron annihilation spectroscopy (DB-VEPAS), and variable-energy positron annihilation lifetime spectroscopy (VEPALS) reveals a progressive increase in structural defects with nitrogen incorporation, including vacancy clusters, subnanometer voids, mesopores, and nanometer-sized internal voids. The defect-rich Cu3N NPs exhibit the highest concentration of vacancy-type defects and the most pronounced internal porosities. Electrochemical measurements demonstrate that these NPs are active eCO2R catalysts for C2+ product formation, with Cu3N outperforming both metallic Cu and biphasic Cu/Cu3N in terms of the Faradaic efficiency (FE), achieving 50% for C2H4 and 20% for C2H5OH. Cu3N NPs also yield a narrower C2+ product distribution with an overall FE of 79%. These results establish reactively sputtered Cu3N-based NPs as a tunable catalyst platform in which composition, structure, and morphology govern eCO2R selectivity and efficiency.
Language: English
Field: Energy
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W7204777788
FWCI:
Cites: 71
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