Polymer films of varying thicknesses were deposited onto cotton and polyester fabric samples by dip-coating from solution. Scanning electron microscopy (SEM) images of the coated fabric samples were used to evaluate the quality of the polymer coating. The samples were analyzed by infrared diffuse reflection spectroscopy to determine the relationship between film thickness and the effect of the coating on the spectroscopy of the two fabrics. Effects observed in four limiting cases are examined: (Case I) weak coating absorption on a fabric with weak absorption at the same frequency; (Case II) strong coating absorption in a spectral region of weak fabric absorption; (Case III) weak coating absorption in a spectral region of strong fabric absorption; and (Case IV) strong coating absorption in a spectral region of strong fabric absorption. In the first case, effects were dominated by reduced scattering as the coating is added. In the second case, the strong coating absorption that was observed at low coverages plateaus at higher coverage due to depth of penetration effects. In the third and fourth cases, reduced Fresnel diffuse reflection is measured as the coating is added, consistent with the reduction of scattering observed in the first case.
We combine a thermal light source with a conventional thermal infrared camera, alternating current (AC) detection methods, and chemical filtering of the infrared (IR) light to generate several imaging modalities in a simple manner. We demonstrate that digital lock-in amplifier techniques can increase the chemical contrast in an active thermal infrared image using both reflectance and thermal re-emission. We show this method is useful for visualizing thin coatings on fabrics that are invisible to the eye. We also take advantage of a "like-detects-like" chemical filtering approach to chemical selectivity for the purpose of chemical identification using a broadband thermal detector.
Infrared thermal imaging using lock-in and molecular factor computing methods for the detection of blood on a dark, acrylic fabric is shown. Contrast differences between the clean fabric and the fabric stained with blood diluted as low as 1:100 are reported. We have also demonstrated that this method can be used to discriminate between a bloodstain and four common interfering agents (bleach, rust, cherry soda, and coffee) to other blood detection methods. These results indicate that this system could be useful for crime scene investigations by focusing nondestructive attention on areas more likely to be suitable for further analysis.
We present a simulation-driven process to design an infrared camera system that is tuned to specific analytes of interest based on "molecular factor computing". There are many factors involved in optimizing discrimination using optical filtering aids, including, but not limited to, the detector response, optical throughput of the system, optical properties of the samples, and optical properties of the materials for sensitizing films/filters. There are nearly infinite possible setups for the system, which means it is neither cost nor time efficient to physically test each one. In lieu of this, we developed routines in MATLAB (The Mathworks, Natick, MA) that simulate the camera output, per pixel, given specific conditions. Beginning with measured spectra of calibration samples or standards and using an objective function or figure of merit (FOM) to measure simulated performance, these routines evaluate large numbers of combinations of chemical films as filters for discrimination based on linear discriminant analysis (LDA). In this study, the FOM was the Fisher ratio between a neat fabric and one stained with either a polymer film or blood.
The objectives of the study were to evaluate retrograde axonal transport of vascular endothelial growth factor A (VEGF-A) protein to sensory neurons after intramuscular administration of an engineered zinc finger protein activator of endogenous VEGF-A (VZ+434) in an experimental model of diabetes, and to characterize the VEGF-A target neurons.
We have been investigating the mid-infrared (MIR) reflection spectrum of microparticles on mirrored substrates. Gold-coated porous alumina filters were used as a substrate to layer the particles and provide consistent reflection spectra. Polystyrene spheres with measured diameters of 0.42 microm were studied using Fourier transform infrared (FT-IR) reflection microspectroscopy, and spectra are shown for coverages in the range 0.5-6 monolayers (ML). Results show that absorption has a nonlinear, stairstep-like dependence on particle coverage and a wavelength dependence that can be explained by electric field standing waves (EFSW) caused by the mirrored substrate. The same effect is found to cause progressive weakening of the observed spectra as a function of increasing wavelength in sub-monolayer coverage measurements. Scattering effects in the spectra are consistent with surface scattering at the antinodes of the EFSW. These observations provide explanations for differences seen between optical properties of particles calculated using the specular-reflection method versus those calculated using traditional aerosol methods. A simple multilayer method for estimating particle absorption coefficients is demonstrated that compares well with values reported using ellipsometry for bulk polystyrene. Another simple method based on submonolayer coverage spectra provides spectra suitable for classification analysis but is only semi-quantitative at determining absorption coefficients.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.