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• W2893763914 abstract "Surface-enhanced Raman spectroscopy is a powerful analytical tool and a strongly surface structure-dependent process. Importantly, it can be coupled with electrochemistry to simultaneously record vibrational spectroscopic information during electrocatalytic reactions. Highest Raman enhancements are obtained using precisely tuned nanostructures. The fabrication and evaluation of a high number of different nanostructures with slightly different properties is time-consuming. We present a strategy to systematically determine optimal nanostructure properties of electrochemically generated Ag void structures in order to find the void size providing highest signal enhancement for Raman spectroscopy. Ag-coated Si wafers were decorated with a monolayer of differently sized polymer nanospheres using a Langmuir-Blodgett approach. Subsequently, bipolar electrochemistry was used to electrodeposit a gradient of differently sized void structures. The gradient structures were locally evaluated using Raman spectroscopy of a surface-adsorbed Raman probe, and the surface regions exhibiting the highest Raman enhancement were characterized by means of scanning electron microscopy. High-throughput scanning droplet cell experiments were utilized to determine suitable conditions for the electrodeposition of the found highly active structure in a three-electrode electrochemical cell. This structure was subsequently employed as the working electrode in operando surface-enhanced Raman measurements to verify its viability as the signal amplifier and to spectroscopically rationalize the complex electrochemical reduction of carbon dioxide." @default.
• W2893763914 created "2018-10-05" @default.
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• W2893763914 date "2018-09-24" @default.
• W2893763914 modified "2023-10-17" @default.
• W2893763914 title "Optimized Ag Nanovoid Structures for Probing Electrocatalytic Carbon Dioxide Reduction Using Operando Surface-Enhanced Raman Spectroscopy" @default.
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• W2893763914 doi "https://doi.org/10.1021/acs.langmuir.8b02501" @default.
• W2893763914 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30247044" @default.
• W2893763914 hasPublicationYear "2018" @default.
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