69 research outputs found

    Nonresonant EFISH and THG studies of nonlinear optical property and molecular structure relations of benzene, stilbene, and other arene derivatives

    Get PDF
    D.c. elec. field induced 2nd-harmonic generation (EFISH) and 3rd-harmonic generation (THG) measurement results are reported on the intrinsic mol. hyperpolarizabilities of benzenes, stilbenes, and other arene derivs. Structure-property relations, as revealed by a comprehensive set of systematic measurements, are discussed. Issues concerning donor-acceptor strength; charge-transfer; transparency trade-off; conjugation planarity, length, and aromaticity; and heteroatom and side-group substitution effects are included

    Nonresonant EFISH And THG Studies Of Nonlinear Optical Property And Molecular Structure Relations Of Benzene, Stilbene, And Other Arene Derivatives.

    Full text link
    D.c. elec. field induced 2nd-harmonic generation (EFISH) and 3rd-harmonic generation (THG) measurement results are reported on the intrinsic mol. hyperpolarizabilities of benzenes, stilbenes, and other arene derivs. Structure-property relations, as revealed by a comprehensive set of systematic measurements, are discussed. Issues concerning donor-acceptor strength; charge-transfer; transparency trade-off; conjugation planarity, length, and aromaticity; and heteroatom and side-group substitution effects are included

    Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

    Get PDF
    Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics

    The Kerr effect of amino acids in water

    No full text

    Measurement of Kerr Constants of Conducting Liquids

    No full text

    The temperature dependence of the Kerr constant of polar liquids

    No full text

    Polymers for Photonics

    No full text
    • …
    corecore