The field of infrared spectral imaging and microscopy is advancing rapidly due in large measure to the recent commercialization of the first high-throughput, high-spatial-definition quantum cascade laser (QCL) microscope. Having speed, resolution and noise performance advantages while also eliminating the need for cryogenic cooling, its introduction has established a clear path to translating the well-established diagnostic capability of infrared spectroscopy into clinical and pre-clinical histology, cytology and hematology workflows.
Demand for even higher throughput while maintaining high-spectral fidelity and low-noise performance continues to drive innovation in QCL-based spectral imaging instrumentation. In this talk, we will present for the first time, recent technological advances in tunable QCL photonics which have led to an additional 10X enhancement in spectral image data collection speed while preserving the high spectral fidelity and SNR exhibited by the first generation of QCL microscopes. This new approach continues to leverage the benefits of uncooled microbolometer focal plane array cameras, which we find to be essential for ensuring both reproducibility of data across instruments and achieving the high-reliability needed in clinical applications. We will discuss the physics underlying these technological advancements as well as the new biomedical applications these advancements are enabling, including automated whole-slide infrared chemical imaging on clinically relevant timescales.
High-fidelity, broadly-tunable quantum cascade lasers (QCLs) are replacing thermal light sources in next-generation infrared chemical imaging and microscopy instrumentation. Their superior spectral brightness, beam quality, and reliability are enabling new applications in biomedical, pharmaceutical, and industrial markets which demand substantially better noise performance, higher throughput, and ease-of-use. In this talk we will discuss the state-of-the-art in QCL source technology and describe our systems approach to leveraging QCL sources in the next-generation of infrared chemical imaging microscopes.
Daylight Solutions has pioneered the development and commercialization of quantum cascade laser (QCL) technology
for commercial and military markets. Multi-Watt, multi-wavelength QCL-based systems have been manufactured and
tested against harsh military environmental requirements for military applications. These self-contained, turn-key
systems have been designed to comply with modular open system architecture (MOSA) principles, and have been
proven in several different system geometries. This paper will highlight the environmental requirements imposed upon,
and performance from, QCL-based laser systems for example military applications.
Many gas-phase sensing applications involve detection of multiple molecules with rotationallyresolved
spectra. The benefits of using mid-infrared (3 - 20 μm) radiation for detection are clear:
strong, characteristic absorption features across regions with varying degrees of overlap can be
exploited for multi-component detection. Quantum cascade lasers now provide CW radiation
throughout the mid-infrared, but there are no commercial lasers available that meet the combined
requirements of small size, low power consumption, and continuous broad tuning. Recent
progress in realizing these modules is discussed, along with commercial applications enabled by
The Multi-angle Imaging SpectroRadiometer (MISR) currently provides three independently derived cloud mask products at 1.1 km spatial resolution. The Radiometric Camera-by-camera Cloud Mask (RCCM) is terrain-referenced and calculated for each of the nine MISR cameras, the Stereoscopically Derived Cloud Mask (SDCM) is feature-projected and uses radiances from one pair of the MISR cameras, and the Angular Signature Cloud Mask (ASCM) uses a band-differenced angular signature based on the two most oblique cameras viewing forward scattering radiation. While each mask has been extensively validated, each having its own strengths and weaknesses, there has been no effort to combine the strengths of all of the masks to create a single consensus product. We present an algorithm which addresses the problem and produces a so called "consensus cloud mask" of improved performance, and elaborate on further cloud climatology applications.