AI Article Synopsis
- Recent advances in ultrafast laser technology are being applied to improve the detection of explosives by manipulating molecular processes at the quantum level.
- The dynamic detection method enhances the selectivity and sensitivity of nonlinear spectroscopic techniques through optimal shaping of laser pulses.
- The study compares the effectiveness of the Gerchberg-Saxton algorithm with machine-learning optimization for obtaining selective coherent anti-Stokes Raman spectra (CARS) from complex mixtures.
Article Abstract
We are utilizing recent advances in ultrafast laser technology and recent discoveries in optimal shaping of laser pulses to significantly enhance the stand-off detection of explosives via control of molecular processes at the quantum level. Optimal dynamic detection of explosives is a method whereby the selectivity and sensitivity of any of a number of nonlinear spectroscopic methods are enhanced using optimal shaping of ultrafast laser pulses. We have recently investigated the Gerchberg-Saxton algorithm as a method to very quickly estimate the optimal spectral phase for a given analyte from its spontaneous Raman spectrum and the ultrafast laser pulse spectrum. Results for obtaining selective coherent anti-Stokes Raman spectra (CARS) for an analyte in a mixture, while suppressing the CARS signals from the other mixture components, are compared for the Gerchberg-Saxton method versus previously obtained results from closed-loop machine-learning optimization using evolutionary strategies.
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| http://dx.doi.org/10.1007/s00216-011-5348-x | DOI Listing |
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