Monitoring the Formation and Decay of Transient Photosensitized Intermediates Using Pump-Probe UV Resonance Raman Spectroscopy. I: Self-Modeling Curve Resolution

Abstract

Resolution of transient excited-state Raman scattering from ground-state and solvent bands is a challenging spectroscopic measurement since excited-state spectral features are often of low intensity, overlapping the dominant ground-state and solvent bands. The Raman spectra of these intermediates can be resolved, however, by acquiring time-resolved data and using multidimensional data analysis methods. In the absence of a physical model describing the kinetic behavior of a reaction, resolution of the pure-component spectra from these data can be accomplished using self-modeling curve resolution, a factor analysis technique that relies on the correlation in the data along a changing composition dimension to resolve the component spectra. A two-laser UV pump-probe resonance-enhanced Raman instrument was utilized to monitor the kinetics of amine quenching of excited-triplet states of benzophenone. The formation and decay of transient intermediates were monitored over time, from 15 ns to 100 micros. Factor analysis of the time-resolved spectral data identified three significant components in the data. The time-resolved intensities at each Raman wavenumber shift were projected onto the three significant eigenvectors, and least-squares criteria were developed to find the common plane in the space of the eigenvectors that includes the observed data. Within that plane, the three pure-component spectra were resolved using geometric criteria of convex hull analysis. The resolved spectra were found to arise from benzophenone excited-triplet states, diphenylketyl radicals, and the solvent and ground-state benzophenone.

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

“…The next two columns of K describe the contributions of the second and third eigenvector to C. To reduce the number of degrees of freedom needed to obtain the K matrix, the first column of the K matrix will be determined from the least-squares best-fit plane allowing the number of degrees of freedom to be reduced from nine to six. 46 The vector in Ū associated with the largest eigenvalue is utilized to fit the plane since it contains the average intensity response from the entire data matrix and has the highest signal-tonoise (S/N) ratio. This choice will have less impact on the rotation of Q into C, since deviations from planarity are smaller than for the other eigenspectra having lower S/N ratios.…”

“…From the above reasoning, the pure component responses that describe the transformation matrix, K, must be located on or outside the line connecting the outer perimeter of the points in Ū . The perimeter of the set of Ū points can be described by its convex hull, which is the smallest polygon for which each point of Ū is either on the boundary or within the interior of the polygon, 46 shown as a dashed line in Fig. 6.…”

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

“…Self-modeling curve resolution in multidimensional vibrational spectroscopy has been used to determine component Raman spectra of sulfuric acid species in solutions of varying sulfuric-acid concentration 16 and methanol–water complexes from IR spectra of their aqueous solutions. 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.…”

Estimating the number of pure chemical components in a mixture by maximum likelihood

“…SMCR does not require a priori knowledge of the pure component spectra, which is particularly useful in characterizing unknown chemical species [3,4]. In general, homogeneity, concentration, and the number of pure components within the chemical or biological system is seldom known in advance [5,6].…”