Separation of Time‐Resolved Phenomena in Surface‐Enhanced Raman Scattering of the Photocatalytic Reduction of p‐Nitrothiophenol

Abstract

Straightforward analysis of chemical processes on the nanoscale is difficult, as the measurement volume is linked to a discrete number of molecules, ruling out any ensemble averaging over rotation and diffusion processes. Raman spectroscopy is sufficiently selective for monitoring chemical changes, but is not sufficiently sensitive to be applied directly. Surface‐enhanced Raman spectroscopy (SERS) can be applied for studying reaction kinetics, but adds additional variability in the signal as the enhancement factor is not the same for every location. A novel chemometric method described here separates reaction kinetics from short‐term variability, based on the lack of fit in a principal‐component analysis. We show that it is possible to study effects that occur on different time scales independently without data reduction using the photocatalytic reduction of p‐nitrothiophenol as a showcase system. Using this approach a better description of the nanoscale reaction kinetics becomes available, while the short‐term variations can be examined separately to examine reorientation and/or diffusion effects. It may even be possible to identify reaction intermediates through this approach. With only a limited number of reactive molecules in the studied volume, an intermediate on a SERS hot spot may temporarily dominate the spectrum. Now such events can be easily separated from the bulk conversion process by making use of this chemometric method.

Surface- and Tip-Enhanced Raman Spectroscopy in Catalysis

“…28,29 With increased control over these SERS substrates, improved Raman signals are obtained with shorter acquisition times, allowing the observation of possible reaction intermediates or even transition states. 30,31 …”

“…28,54 Previous research performed in our group utilized this principle to study the dimerization of 4-NTP to DMAB. 29,30,119 Chemometric methods were applied to obtain a clearer image of the kinetics during the reaction, demonstrated in Figure 7. 30 First, a one-component principal-component analysis (PCA) removed the spectral blinking from the data and already resulted in a lower signal-to-noise ratio (Figure 7 left panel vs middle panel).…”

“…29,30,119 Chemometric methods were applied to obtain a clearer image of the kinetics during the reaction, demonstrated in Figure 7. 30 First, a one-component principal-component analysis (PCA) removed the spectral blinking from the data and already resulted in a lower signal-to-noise ratio (Figure 7 left panel vs middle panel). After removal of the spectral blinking, the reaction spectra were taken through a two-component multivariate curve resolution (MCR) that described the reactant and the product.…”

“…The third component resembled much of DMAB, but the fourth component is a completely unknown structure and is a potential reaction intermediate. 30 …”

“…(Right) The discarded data after the PCA time filter can be analyzed with MCR to find possible intermediate structures. 30 Adapted with permission from ref (30). Copyright 2015 Wiley-VCH.…”