Highly sensitive Fourier-transform coherent anti-Stokes Raman scattering spectroscopy via genetic algorithm pulse...

Area of Science:

• Spectroscopy
• Nonlinear optics
• Computational chemistry

Background:

• Fourier-transform coherent anti-Stokes Raman scattering (FTCARS) spectroscopy is a powerful vibrational spectroscopy technique.
• Adaptive dispersion compensation is crucial for optimizing spectroscopic signal quality.
• Genetic algorithms (GAs) offer a robust approach for complex optimization problems.

Purpose of the Study:

• To enhance the sensitivity of FTCARS spectroscopy using adaptive pulse shaping.
• To develop a novel GA-based method for dispersion compensation tailored to specific sample conditions.
• To demonstrate the effectiveness of using non-resonant four-wave mixing signals for GA training.

Main Methods:

• Implementation of a genetic algorithm (GA) for adaptive pulse shaping in FTCARS.
• Utilizing the non-resonant four-wave mixing (NR-FWM) signal from water as a fitness indicator for GA training.
• Comparison of GA training with NR-FWM versus second-harmonic generation (SHG) from a nonlinear crystal.

Main Results:

• Achieved a 3x improvement in peak signal-to-noise ratio for 2-propanol measurements.
• Demonstrated a 10x increase in peak intensities for high-throughput measurement of polystyrene microbeads under flow.
• Showcased superior GA adaptation to sample measurement conditions using NR-FWM compared to SHG.

Conclusions:

• GA-enabled pulse shaping provides a highly sensitive and adaptive method for FTCARS spectroscopy.
• Using sample-derived NR-FWM signals for GA training offers significant performance advantages.
• This approach enhances spectroscopic measurements for diverse applications, including microfluidics and chemical analysis.