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Abstract Our use of a commercially available monolithic surface enhanced Raman spectroscopy substrate based on metallized arrays of nanopillars has been hindered by the structured and variable background spectrum found in as‐supplied substrates. We identify surface contamination—at minimum from the shipping container—as the root cause of the variability and observed that our cleaning protocol could enhance the spectral performance but could also significantly degrade it in some cases. These uniquely nanostructured substrates offer on‐demand hot spot formation by solvent‐evaporation‐induced nanopillar leaning, so that oxygen‐plasma treatment was used as a noncontact method to preserve this capability. Suitable plasma cleaning conditions produced less structured background spectra and yielded higher signal intensities. Detrimental plasma power and exposure durations could yield significantly cleaner background spectra but at the significant cost of response to analyte. Scanning electron micrographs showed the plasma could alter the surface morphology. Optimization of oxygen‐plasma settings must therefore balance favorable effects such as contaminant removal, improved substrate wetting, and signal enhancement with deleterious effects such as structural damage to the substrate. 

Background subtraction is a general problem in spectroscopy often addressed with application-specific techniques, or methods that introduce a variety of implementation barriers such as having to specify peak-free regions of the spectrum. An iterative dual-tree complex wavelet transform-based background subtraction method (DTCWT-IA) was recently developed for the analysis of ultrafast electron diffraction patterns. The method was designed to require minimal user intervention, to support streamlined analysis of many diffraction patterns with complex overlapping peaks and time-varying backgrounds, and is implemented in an open-source computer program. We examined the performance of DTCWT-IA for the analysis of spectra acquired by a range of optical spectroscopies including ultraviolet–visible spectroscopy (UV–Vis), X-ray photoelectron spectroscopy (XPS), and surface-enhanced Raman spectroscopy (SERS). A key benefit of the method is that the user need not specify regions of the spectrum where no peaks are expected to occur. SER spectra were used to investigate the robustness of DTCWT-IA to signal-to-noise levels in the spectrum and to user operation, specifically to two of the algorithm parameter settings: decomposition level and iteration number. The single, general DTCWT-IA implementation performs well in comparison to the different conventional approaches to background subtraction for UV–Vis, XPS, and SERS, while requiring minimal input. The method thus holds the same potential for optical spectroscopy as for ultrafast electron diffraction, namely streamlined analysis of spectra with complex distributions of peaks and varying signal levels, thus supporting real-time spectral analysis or the analysis of data acquired from different sources.