Curvature and wrinkling of premixed flame Kernels comparison between planar laser induced fluorescence (PLIF) and 3D direct numerical simulations (DNS)

A direct comparison between time resolved PLIF measurements of OH and two dimensional slices from a full three dimensional DNS data set of turbulent premixed flame kernels in lean methane/air mixture was presented. The local flame structure and the degree of flame wrinkling were examined in response to differing turbulence intensities and turbulent Reynolds numbers. Simulations were performed using the SEGA DNS code, which is based on the solution of the compressible Navier Stokes, species, and energy equations for a lean hydrocarbon mixture. For the OH PLIF measurements, a cluster of four Nd:YAG laser was fired sequentially at high repetition rates and used to pump a dye laser. The frequency doubled laser beam was formed into a sheet of 40 mm height using a cylindrical telescope. The combination of PLIF and DNS has been demonstrated as a powerful tool for flame analysis. This research will form the basis for the development of sub-grid-scale (SGS) models for LES of lean-premixed combustion systems such as gas turbines. This is an abstract of a paper presented at the 30th International Symposium on Combustion (Chicago, IL 7/25-30/2004).

Analysis of the ellipsometric spectra of amorphous carbon thin films for evaluation of the sp3-bonded carbon content

Using spcctroscopic ellipsometry (SE), we have measured the optical properties and optical gaps of a series of amorphous carbon (a-C) films ∼ 100-300 Å thick, prepared using a filtered beam of C+ ions from a cathodic arc. Such films exhibit a wide range of sp3-bonded carbon contents from 20 to 76 at.%, as measured by electron energy loss spectroscopy (EELS). The Taue optical gaps of the a-C films increase monotonically from 0.65 eV for 20 at.% sp3 C to 2.25 eV for 76 at.% sp3 C. Spectra in the ellipsometric angles (1.5-5 eV) have been analyzed using different effective medium theories (EMTs) applying a simplified optical model for the dielectric function of a-C, assuming a composite material with sp2 C and sp3 C components. The most widely used EMT, namely that of Bruggeman (with three-dimensionally isotropic screening), yields atomic fractions of sp3 C that correlate monotonically with those obtained from EELS. The results of the SE analysis, however, range from 10 to 25 at.% higher than those from EELS. In fact, we have found that the volume percent sp3 C from SE using the Bruggeman EMT shows good numerical agreement with the atomic percent sp3 C from EELS. The SE-EELS discrepancy has been reduced by using an optical model in which the dielectric function of the a-C is determined as a volume-fraction-weighted average of the dielectric functions of the sp2 C and sp3 C components. © 1998 Elsevier Science S.A.

A Bayesian approach to tracking in single molecule fluorescence microscopy

Using fluorescence microscopy with single molecule sensitivity it is now possible to follow the movement of individual fluorophore tagged molecules such as proteins and lipids in the cell membrane with nanometer precision. These experiments are important as they allow many key biological processes on the cell membrane and in the cell, such as transcription, translation and DNA replication, to be studied at new levels of detail. Computerized microscopes generate sequences of images (in the order of tens to hundreds) of the molecules diffusing and one of the challenges is to track these molecules to obtain reliable statistics such as speed distributions, diffusion patterns, intracellular positioning, etc. The data set is challenging because the molecules are tagged with a single or small number of fluorophores, which makes it difficult to distinguish them from the background, the fluorophore bleaches irreversibly over time, the number of tagged molecules are unknown and there is occasional loss of signal from the tagged molecules. All these factors make accurate tracking over long trajectories difficult. Also the experiments are technically difficulty to conduct and thus there is a pressing need to develop better algorithms to extract the maximum information from the data. For this purpose we propose a Bayesian approach and apply our technique to synthetic and a real experimental data set.