Two-dimensional angle-resolved optical scattering (TAOS) is an experimental method which collects the intensity pattern of monochromatic light scattered by a single, micron-sized airborne particle. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. The solution proposed herewith relies on a learning machine (LM): rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified. The LM consists of two interacting modules: a feature extraction module and a linear classifier. Feature extraction relies on spectrum enhancement, which includes the discrete cosine Fourier transform and non-linear operations. Linear classification relies on multivariate statistical analysis. Interaction enables supervised training of the LM. The application described in this article aims at discriminating the TAOS patterns of single bacterial spores (Bacillus subtilis) from patterns of atmospheric aerosol and diesel soot particles. The latter are known to interfere with the detection of bacterial spores. Classification has been applied to a data set with more than 3000 TAOS patterns from various materials. Some classification experiments are described, where the size of training sets has been varied as well as many other parameters which control the classifier. By assuming all training and recognition patterns to come from the respective reference materials only, the most satisfactory classification result corresponds to ≈ 20% false negatives from Bacillus subtilis particles and ≤ 11% false positives from environmental and diesel particles.
Real-time and in-situ detection and discrimination of aerosol particles, especially bio-aerosols, continues to be an important challenge. The technique labeled TAOS (Two-dimensional Angular Optical Scattering) characterizes particles based upon the angular distribution of elastically scattered light. The detected angular distribution of light, labeled the TAOS pattern, depends upon the particle’s shape, size, surface features, and its complex refractive index. Thus, the absorptive properties of a particle affect the TAOS pattern. Furthermore, we expect to use this change in the TAOS pattern, which occurs when the particle absorption band includes the input wavelength, to characterize the strength of the absorption. Thus, by illuminating a particle in the mid-infrared wavelength range, high frequency vibrational modes that are unique to the aerosol can be reached and quantified.
Spherical aerosol particles (in the diameter range of 50-60 micrometers) were generated via a droplet generator and illuminated by an Interband Cascade (IC) laser designed to emit in the 3-5 micrometers wavelength range. The TAOS pattern of the elastically scattered light was detected with an InSb-focal-plane-array infrared camera.
Fluorescent sensing of oxygen is an optical method for determining the concentration of dissolved or gaseous oxygen in a medium based on flourescent quenching. In the literature, papers on fluorescent quenching oxygen sensor have highlighted certain key problems that limit the sensitivity an disability of these devices. In this paper, we describe a novel optical collection scheme using planar waveguide that overcomes these key issues. The light collection scheme incorporates multiple alterations over the original simple planar waveguide design. These alterations included shearing the end-face of the waveguide, adding reflective coatings, increasing the refractive index of the waveguide material, and finally, tapering one end of the waveguide. The design is modeled and tested using a computer-simulation program. The end result is a light collection scheme that can have a large fluorescing surface are while maintain in a high light collection efficiency. The optimized waveguide is found to guide 7.0% of the total emitted fluorescent power to the detector for an arbitrary surface area of fluorescence material. This design should greatly help to combat a key problem with fluorescent sensing: photo-bleaching.