Resolution of Intermediate Adsorbate Structures in the Potential-Dependent Self-Assembly of n-Hexanethiolate on Silver by in situ Surface-Enhanced Raman Spectroscopy

Abstract

Resolution of the reaction steps and the associated component Raman spectra during the formation or desorption of self-assembled monolayers is challenging because intermediate adsorbate populations are present at low concentrations and their spectral bands overlap. By collecting Raman spectra versus applied potential into a two-dimensional data set, one can utilize multivariate statistical techniques to resolve the component concentration profiles along with their corresponding Raman spectra. In situ surface-enhanced Raman spectroscopy (SERS) spectra were collected during the potential-dependent formation and desorption (-1.50 to -0.70V versus Ag/AgCl) of n-hexanethiolate monolayer at a polycrystalline Ag electrode. Resolution of the pure component spectra from these components was accomplished by using self-modeling curve resolution (SMCR), which does not require a physical model. For monolayer adsorption, the potential-dependent Raman spectra could be described by three significant eigenvectors; the eigenvectors could be rotated into a set of pure component spectra and concentration profiles using a linear least-squares step to find a common plane in the space of the eigenvectors representing the linear combination of the real-component responses. The convex hull surrounding the data in the plane and positive amplitude criteria were utilized to identify the coordinates of the pure component responses. The C-S stretching vibrations of the resolved spectra show that the initial adsorbate is a gauche conformer, which allows the hydrocarbon chain to lie on the metal surface; a second phase arises at higher coverage with trans C-S conformation, where the hydrocarbon chains are oriented off the surface plane, and a final complete monolayer is formed with a well-ordered, all-trans C-S configuration. In contrast, desorption studies showed only two surface phases, the initial well-ordered monolayer and the low-density phase dominated by gauche conformations. The results illustrate the utility of self-modeling curve resolution to unravel interfacial reaction mechanisms and intermediate structures from two-dimensional SERS data, without requiring prior knowledge of a physical model for the process.

C18-Modified Metal-Colloid Substrates for Surface-Enhanced Raman Detection of Trace-Level Polycyclic Aromatic Hydrocarbons in Aqueous Solution

“…The electrochemical SERS experiment employed a fiber-optic bundle to couple the Raman scatter to the monochromator; these optics have recently been described in detail. 33 Intensity of the s-polarized 647.1 nm excitation was 100 mW. The potentiostat/galvanostat (EG&G PAR model 173) was connected to an x-y recorder (Kipp&Zonen, model BD90).…”

Surface-Enhanced Raman Scattering Study of the Kinetics of Self-Assembly of Carboxylate-Terminated n-Alkanethiols on Silver

“…n -Alkanethiol self-assembled monolayers (SAMs) on metal substrates have been widely used in controlling the surface properties with numerous applications, such as corrosion inhibition, colloidal stabilization, and molecular sensors. − Understanding their structure as well as their mechanism of formation can help to control the assembly and properties of the resulting monolayers, which are crucial for their applications. Many different techniques, including scanning tunneling microscopy, − surface plasmon resonance, atomic force microscopy, infrared spectroscopy, , quartz-crystal microbalance, and Raman spectroscopy, , have been utilized to elucidate the formation and structure of self-assembled monolayers. In previous studies, the assembly of n -alkanethiols on metal surfaces has been studied extensively, and a two-step assembly process (initial adsorption, then annealing to an organized structure) is most often reported to describe monolayer formation. ,,,,, …”

“…Many different techniques, including scanning tunneling microscopy, − surface plasmon resonance, atomic force microscopy, infrared spectroscopy, , quartz-crystal microbalance, and Raman spectroscopy, , have been utilized to elucidate the formation and structure of self-assembled monolayers. In previous studies, the assembly of n -alkanethiols on metal surfaces has been studied extensively, and a two-step assembly process (initial adsorption, then annealing to an organized structure) is most often reported to describe monolayer formation. ,,,,, …”

“…MUA was chosen because it has relatively high aqueous solution solubility, and it is reported to form compact SAMs on metal surfaces . Surface-enhanced Raman scattering (SERS) spectroscopy was employed because of its interfacial selectivity, so that the measurements exhibit little interference from the bulk solution. , In addition, the interactions of the carboxylate and thiol groups with the silver substrate and conformational changes during the adsorption process are readily elucidated using the vibrational spectroscopy information provided by SERS. The influence of several factors, including solvent composition, solution pH, and the potential of the metal surface, on the involvement of the carboxylate group in the assembly process was investigated.…”

Calorimetry-Derived Composition Vectors to Resolve Component Raman Spectra in Phospholipid Phase Transitions

“…17 Self-modeling curve resolution has been employed to elucidate Raman spectra of triplet excited states 5,18 and to determine intermediate structures in surface-enhanced Raman spectra of potential dependent self-assembly of monolayers. 19 Self-modeling curve resolution has also been applied in temperature-dependent Raman spectroscopy to investigate DPPC bilayer melting transitions carried out using a piecewise two-component approach to solving an intractable four-component curve resolution problem. 20 This piecewise decomposition approach highlights a major challenge of self-modeling curve resolution: that the number of elements in the rotation matrix, which transforms the abstract eigenvectors into component spectra, is the square of the number of spectral components.…”