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Fast infrared chemical imaging with a quantum cascade laser
Kevin Yeh1, Seth Kenkel, Jui-Nung Liu
1Department of Bioengineering, ‡Department of Mechanical Science and Engineering, and §Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States.
Analytical Chemistry
|December 5, 2014
Summary
We developed a new discrete frequency infrared (DF-IR) spectral imaging system using quantum cascade lasers (QCL) that significantly accelerates chemical imaging of cells and tissues compared to existing Fourier transform infrared (FT-IR) methods.
Area of Science:
- Spectroscopy
- Microscopy
- Biomedical Imaging
Background:
- Infrared (IR) spectroscopic imaging visualizes molecular microstructure without dyes.
- Current Fourier transform infrared (FT-IR) imaging is dye-free but slow due to scanning.
- Quantum cascade lasers (QCL) offer high intensity for accelerated IR imaging.
Purpose of the Study:
- To develop a novel discrete frequency IR (DF-IR) spectral imaging microscope.
- To leverage QCLs for high-speed, high-definition IR chemical imaging.
- To compare the performance of the new DF-IR system with established FT-IR methods.
Main Methods:
- Coupled a rapidly tunable QCL with a high-performance microscope and focal plane array (FPA) detector.
- Multiplexed multiple QCLs for broad spectral coverage (776.9–1904.4 cm⁻¹).
- Designed the optical system for high-definition (HD) IR imaging based on theoretical rules.
Main Results:
- Achieved spectral and spatial fidelity comparable to the best FT-IR systems.
- Demonstrated a speedup in achieving equivalent signal-to-noise ratio (SNR) compared to linear array systems.
- Showcased a 3-order of magnitude faster scanning of tissue microarrays (TMA) per spectral frequency.
Conclusions:
- The new DF-IR microscope offers significant speed advantages for IR chemical imaging.
- The system provides high spectral and spatial fidelity for analyzing cells and tissues.
- This technology enables high-throughput molecular microstructure analysis.

