Rapid quantitative imaging of chemical species is an important tool for investigating heterogenous mixtures, such as laminated plastics, biological samples and vapor plumes. Using traditional spectroscopic methods requires difficult computations on very large data sets. By embedding a spectral pattern that corresponds to a target analyte in an interference filter in a beamsplitter arrangement; the chemical information in an image can be obtained rapidly and with a minimal amount of computation. A candidate filter design that is tolerant to the angles present in an imaging arrangement is evaluated in near-infrared spectral region for an organic analyte and an interferent.
Multivariate Optical Computing (MOC) devices have the potential of greatly simplifying as well as reducing the cost of applying the mathematics of multivariate regression to problems of chemical analysis in the real world. These devices utilize special optical interference coatings known as multivariate optical elements (MOEs) that are encoded with pre-determined spectroscopic patterns to selectively quantify a chemical species of interest in the presence of other interfering species. A T-format prototype of the first optical computing device is presented utilizing a multilayer MOE consisting of alternating layers of two metal oxide films (Nb2O5 and SiO2) on a BK-7 glass substrate. The device was tested by using it to quantify copper uroporphyrin in a quaternary mixture consisting of uroporphyrin (freebase), tin uroporphyrin, nickel uroporphyrin, and copper uroporphyrin. A standard error of prediction (SEP) of 0.86(mu) M was obtained for copper uroporphyrin.
A new algorithm for the design of optical computing filters for chemical analysis otherwise known as Multivariate Optical Elements (MOEs), is described. The approach is based on the nonlinear correlation of the MOE layer thicknesses to the standard error in sample prediction for the chemical species of interest using a modified version ofthe Gauss-Newton nonlinear optimization algorithm. The design algorithm can either be initialized by random layer thicknesses or by a pre-existing design. The algorithm has been successfully tested by using it to design a MOE for the determination of copper uroporphynn in a quaternary mixture of uroporphyrin (freebase), nickel uroporphyrin, copper uroporphynn, and tin uroporphyrin.
12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.
Carl Dirk, Aruna Nagarur, Jin Lu, Lixia Zhang, Priya Kalamegham, Joe Fonseca, Saytha Gopalan, Scott Townsend, Gabriel Gonzalez, Patrick Craig, Monica Rosales, Leslie Green, Karen Chan, Robert Twieg, Susan Ermer, Doris Leung, Steven Lovejoy, Suzanne Lacroix, Nicolas Godbout, Etienne Monette
Summarized are two project areas: First, the development of a quantitative structure property relationship for analyzing thermal decomposition differential scanning calorimetry data of electro-optic dyes is presented. The QSPR relationship suggest that thermal decomposition can be effectively correlated with structure by considering the kinds of atoms, their hybridization, and their nearest neighbor bonded atoms. Second, the simple preparation of clad plastic optical fibers (POF) is discussed with the intention of use for nonlinear optical applications. We discuss preparation techniques for single core and multiple core POF, and present some recent data on index profiles and the optimization of thermal stability in acrylate-based POF structures.
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