Two switchable, mesoscopically periodic materials were created by combining crystalline colloidal array (CCA) self-assembly with the temperature-induced volume phase transition of poly(N-isopropylacrylamide) (PNIPAM). Body-centered-cubic CCAs of hydrated, swollen PNIPAM particles Bragg-diffract infrared, visible, and ultraviolet light weakly, whereas arrays of compact shrunken particles diffract efficiently. A tunable diffracting array was also created by embedding a CCA of polystyrene spheres within a PNIPAM hydrogel that swells and contracts with temperature; thus the array lattice constant varies with temperature, and the diffracted wavelength was thermally tunable across the entire visible spectrum. These materials may find applications in many areas of optics and materials science.

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Over the past decade, we have been working to develop intelligent photonic-crystal materials with unique properties, which will be useful in a number of technological areas. These photonic-crystal materials utilize mesoscopically periodic arrays of spherical particles as their active optical elements and are easily fabricated chemically by the use of crystalline-colloidal-array (CCA) self-assembly techniques.Crystalline colloidal arrays are mesoscopically periodic fluid materials, which efficiently diffract light meeting the Bragg condition. These photonic-crystal materials consist of arrays of colloidal particles that self-assemble in solution into either face-centered-cubic (fcc) or body-centered-cubic (bcc) crystalline arrays (Figure 1), with lattice constants in the mesoscale size range (50-500 nm). Just as atomic crystals diffract x-rays that meet the Bragg condition, CCAs diffract ultraviolet, visible, and near-infrared light, depending on the lattice spacing; the diffraction phenomena resemble those of opals, which are close-packed arrays of monodisperse silica spheres.The CCA however can be prepared as macroscopically ordered arrays of non-close-packed spheres. This self-assembly is the result of electrostatic repulsions between colloidal particles, each of which has numerous charged surface functional groups. We have concentrated on the development of CCAs that diffract light in the visible spectral region and generally utilize colloidal particles of ~100-nm diameter. These particles have thousands of surface charges, which result from the ionization of surface sulfonate groups. The nearest-neighbor distances are often >200 nm.

We characterized the diffraction and crystal structure of a crystalline colloidal array (CCA) photonic crystal composed of 270 nm diameter polystyrene spheres which have a nearest neighbor spacing of approximately 540 nm. This CCA diffracts light in first order at approximately 1200 nm and shows strong diffraction in the visible spectral region from higher order planes. We quantitatively examined the relative diffraction intensities of the putative fcc (111), (200), (220), and (311) planes. Comparing these intensities to those calculated theoretically we find that the crystal structure is fcc with significant stacking faults. Essentially, no light transmits at the Bragg angle for the fcc (111) planes even through thin approximately 40 microm thick CCA. However, much of this light is diffusely scattered about the Bragg angle due to crystal imperfections. Significant transmission occurs from thin samples oriented at the Bragg condition for the fcc (200), (220), and (311) planes. We also observe moderately intense two-dimensional diffraction from the first few layers at the crystal surfaces. We also examined the sample thickness dependence of diffraction from CCA photonic crystals prepared from approximately 120 nm polystyrene spheres whose fcc (111) planes diffract in the visible spectral region. These experimental observations, aided by calculations based upon a simple but flexible model of light scattering from an arbitrary collection of colloidal spheres, make clear that fabrication of three-dimensional photonic band gap crystals will be challenged by crystal imperfections.

Optical coherence tomography (OCT) provides a non-invasive method for in-vivo imaging of sub-surface skin tissue. Many skin features such as sweat glands and blisters are clearly observable in OCT images. It seems therefore probable that OCT could be used for the detection and identification of lesions and skin cancers. These applications, however, have not been well developed. One area in dermatology where OCT has been applied is the measurement of epidermal thickness. OCT images are inherently noisy and measurements based on them require intensive manual processing. A robust method to automatically detect and measure features of interest is necessary to enable routine application of OCT. As a first step, we approach the seemingly straightforward problem of measuring epidermal thickness. In this paper we describe a novel shapelet-based image processing technique for the automatic identification of the upper and lower boundaries of the epidermis in living human skin tissue. These boundaries are used to measure epidermal thickness. To our knowledge, this is the first report of automated feature identification and measurement from OCT images of skin.

The examination of the dermis/epidermis junction (DEJ) is clinically important for skin cancer diagnosis. Reflectance confocal microscopy (RCM) is an emerging tool for detection of skin cancers in vivo. However, visual localization of the DEJ in RCM images, with high accuracy and repeatability, is challenging, especially in fair skin, due to low contrast, heterogeneous structure and high inter- and intra-subject variability. We recently proposed a semi-automated algorithm to localize the DEJ in z-stacks of RCM images of fair skin, based on feature segmentation and classification. Here we extend the algorithm to dark skin. The extended algorithm first decides the skin type and then applies the appropriate DEJ localization method. In dark skin, strong backscatter from the pigment melanin causes the basal cells above the DEJ to appear with high contrast. To locate those high contrast regions, the algorithm operates on small tiles (regions) and finds the peaks of the smoothed average intensity depth profile of each tile. However, for some tiles, due to heterogeneity, multiple peaks in the depth profile exist and the strongest peak might not be the basal layer peak. To select the correct peak, basal cells are represented with a vector of texture features. The peak with most similar features to this feature vector is selected. The results show that the algorithm detected the skin types correctly for all 17 stacks tested (8 fair, 9 dark). The DEJ detection algorithm achieved an average distance from the ground truth DEJ surface of around 4.7μm for dark skin and around 7–14μm for fair skin.

The measurement of stratum corneum (SC) thickness from in-vivo Raman water concentration depth profiles is gaining in popularity and appeal due to the availability and ease of use of in-vivo confocal Raman measurement systems. The foundation of these measurements relies on high-quality confocal Raman spectroscopy of skin and the robust numerical analysis of water profiles, which allow for accurate determination of SC thickness. These measurements are useful for studying intrinsic skin hydration profiles at different body sites and for determining hydration properties of skin related to topically applied materials. While the use of high-quality in-vivo Raman instrumentation has become routine and its use for SC thickness measurement widely reported, there is lack of agreement as to the best method of computing SC thickness values from Raman water profiles. Several methods have been proposed and are currently in use for such computations, but none of these methods has been critically evaluated. The work reported in this paper describes a new method for the determination of stratum corneum thickness from in-vivo confocal Raman water profiles. The method represents a consensus approach to the problem, which was found necessary to apply in order to properly model and quantify the large diversity of water profile types encountered in typical in-vivo Raman water measurement. The methodology is evaluated for performance using three criteria: 1) frequency of minimum fitting error on modeling to a standard numerical function; 2) frequency of minimum model error for consensus vs. individual SC thickness values; and 3) correlation with reflectance confocal microscopy (RCM) values for SC thickness. The correlation study shows this approach to be a reasonable replacement for the more tedious and time-consuming RCM method with R 2 = 0.68 and RMS error = 3.7 microns over the three body sites tested (cheek, forearm and leg).

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