32 research outputs found
On the chemical bonding effects in the Raman response: Benzenethiol adsorbed on silver clusters
We study the effects of chemical bonding on Raman scattering from
benzenethiol chemisorbed on silver clusters using time-dependent density
functional theory (TDDFT). Raman scattering cross sections are computed using a
formalism that employs analytical derivatives of frequency-dependent electronic
polarizabilities, which treats both off-resonant and resonant enhancement
within the same scheme. In the off-resonant regime, Raman scattering into
molecular vibrational modes is enhanced by one order of magnitude and shows
pronounced dependence on the orientation and the local symmetry of the
molecule. Additional strong enhancement of the order of arises from
resonant transitions to mixed metal--molecular electronic states. The Raman
enhancement is analyzed using Raman excitation profiles (REPs) for the range of
excitation energies eV, in which isolated benzenethiol does not have
electronic transitions. The computed vibrational frequency shifts and relative
Raman scattering cross sections of the metal--molecular complexes are in good
agreement with experimental data on surface enhanced Raman scattering (SERS)
for benzenethiol adsorbed on silver surfaces. Characterization and
understanding of these effects, associated with chemical enhancement mechanism,
may be used to improve the detection sensitivity in molecular Raman scattering.Comment: 25 pages, 14 figures. Phys. Chem. Chem. Phys. in pres
Chemische Änderungen bei der Wärmebehandlung von Holz | Chemical changes following heat treatment of wood
Primary pulmonary malignant lymphoma, clinical and pathological findings, immunocytochemical and ultrastructural studies in 15 cases
Nanostructured Tin Oxide as a Surface-Enhanced Raman Scattering Substrate for the Detection of Nitroaromatic Compounds
Nanoporous Ag Films Prepared by Cluster-Source Sputtering as Substrates for Surface-Enhanced Raman Scattering
A Generalized Extreme Value Approach to Financial Risk Measurement
This paper develops an unconditional and conditional extreme value approach to calculating value at risk (VaR), and shows that the maximum likely loss of financial institutions can be more accurately estimated using the statistical theory of extremes. The new approach is based on the distribution of extreme returns instead of the distribution of all returns and provides good predictions of catastrophic market risks. Both the in-sample and out-of-sample performance results indicate that the Box-Cox generalized extreme value distribution introduced in the paper performs surprisingly well in capturing both the rate of occurrence and the extent of extreme events in financial markets. The new approach yields more precise VaR estimates than the normal and skewed "t" distributions. Copyright 2007 The Ohio State University.