Rapid in Situ Detection of Ammonia with Surface Enhanced Raman Spectroscopy

Rapid and reliable ammonia detection is of great significance to environmental monitoring and heterogeneous electrocatalysis, wherein ammonia serves as a product or reaction intermediate. Non-perturbative and sensitive or even in operando ammonia detection is of particular interest to the rapidly booming field of the electrochemical nitrogen reduction reaction for ambient and distributed ammonia synthesis.¹ Various ex-situ analytical methods have been applied or recently developed, including colorimetric methods, nuclear magnetic resonance (NMR) and liquid chromatography − mass spectrometry (LC-MS).^2–5 In this talk, we will present a novel approach to ammonia detection, with the aid of surface enhanced Raman scattering (SERS).⁶ This is a simple, fast and sensitive method that provides chemical selectivity to ammonia. In addition, there is no need for a complex sensor design, specific beam path geometry, or chemical modifications of the solution. The method features a detection of sub-1 ppm ammonia concentrations in less than 1 second, which paves the way to ultrasensitive in operando electrochemical experiments in the conventional H-cell or other assemblies. Considering the short spatial reach of plasmonic enhancement, the observed signals correspond to approximately 10⁴–10⁵ molecules detected locally at the region of interest. Moreover, the concept of SERS detection does not enforce any limitations on the identity or morphology of the electrode so long as it can be placed close enough to the SERS substrate. Then, the SERS substrate can be easily engineered according to specific experimental constraints to probe the ammonia near the electrode surface. This enables the possible detection of local ammonia in closest proximity to the reaction site.

Acknowledgement

We gratefully acknowledge the support from DOE-EERE Advanced Manufacturing Office award via Sandia National Laboratories (AOP 34920). We would also like to acknowledge the NSF grant CHE-0960179 and the UNM Center for Advanced Research Computing for computational resources.

Reference

1. J. G. Chen et al., Science, 360, eaar6611 (2018)

2. S. Z. Andersen et al., Nature, 570, 504–508 (2019)

3. R. Y. Hodgetts et al., ACS Energy Lett., 736–741 (2020)

4. A. C. Nielander et al., ACS Catal., 9, 5797–5802 (2019)

5. W. Yu, N. S. Lewis, H. B. Gray, and N. F. Dalleska, ACS Energy Lett., 1532–1536 (2020)

6. Y. Liu et al., iScience, 23, 101757 (2020) https://doi.org/10.1016/j.isci.2020.101757.

Figure 1