Energy efficient nitrogen reduction to ammonia at low overpotential in aqueous electrolyte under ambient conditions
Azofra, Luis Miguel
Suryanto, Bryan Harry Rahmat
MacFarlane, Douglas Robert
KAUST DepartmentKAUST Catalysis Center (KCC)
Physical Sciences and Engineering (PSE) Division
Chemical Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/629929
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AbstractThe electrochemical nitrogen reduction reaction (NRR) at ambient conditions is a promising alternative to the traditional energy-intensive Haber-Bosch process to produce ammonia. The challenge is to achieve a sufficient energy efficiency, yield rate and selectivity to make the process practical. Herein, we demonstrate that Ruthenium nanoparticles (Ru NPs) enable NRR in 0.01 M HCl aqueous solution at very high energy efficiency, i.e., very low overpotentials. Remarkably, the NRR occurs at potential close to or even above H+/H2 reversible potential, significantly enhancing the NRR selectivity versus the production of H2. NH3 yield rates as high as ~5.5 mg h-1 m-2 at 20°C and 21.4 mg h-1 m-2 at 60°C were achieved at E = -100 mV versus the relative hydrogen electrode (RHE) while a highest Faradaic efficiency of ~5.4% is achievable at E = +10 mV vs. RHE. This work demonstrates the potential use of Ru NPs as an efficient catalyst for NRR at ambient conditions. This ability to catalyse NRR at potentials near or above RHE is imperative in improving the NRR selectivity towards a practical process as well as rendering the H2 viable as by-product. DFT calculations of the mechanism suggest that the efficient NRR process occurring on these predominantly Ru(001) surfaces is catalysed by a dissociative mechanism.
CitationWang D, Azofra LM, Harb M, Cavallo L, Zhang X, et al. (2018) Energy-Efficient Nitrogen Reduction to Ammonia at Low Overpotential in Aqueous Electrolyte under Ambient Conditions. ChemSusChem 11: 3416–3422. Available: http://dx.doi.org/10.1002/cssc.201801632.
SponsorsThe authors thank Monash Centre for Electron Microscopy (MCEM) for the provision of access to their instruments. L.M.A., M.H. and L.C. acknowledge King Abdullah University of Science and Technology (KAUST) for support. Gratitude is also due to the KAUST Supercomputing Laboratory using the supercomputer Shaheen II for providing the computational resources. This study was supported by an Australian Research Council (ARC) Discovery Grant (DP170102267). D.R.M. is grateful to the ARC for his Australian Laureate Fellowship.