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In this study we show an optical biosensor concept, based on elastic light scattering from sapphire microspheres. Transmitted and elastic scattering intensity of the microspheres (radius 500 μm, refractive index 1.77) on an optical fiber half coupler is analyzed at 1510 nm. The 0.43 nm angular mode spacing of the resonances is comparable to the angular mode spacing value estimated using the optical size of the microsphere. The spectral linewidths of the resonances are in the order of 0.01 nm, which corresponds to quality factors of approximately 105. A polydopamine layer is used as a functionalizing agent on sapphire microspherical resonators in view of biosensor implementation. The varying layer thickness on the microsphere is determined as a function of the resonance wavelength shift. It is shown that polymer functionalization has a minor effect on the quality factor. This is a promising step toward the development of an optical biosensor.
In this study, we show that synthetic sapphire (Al₂O₃), an established implant material, can also serve as a platform material for biosensors comparable to nanocrystalline diamond. Sapphire chips, beads, and powder were first modified with (3-aminopropyl) triethoxysilane (APTES), followed by succinic anhydride (SA), and finally single-stranded probe DNA was EDC coupled to the functionalized layer. The presence of the APTES-SA layer on sapphire powders was confirmed by thermogravimetric analyis and Fourier-transform infrared spectroscopy. Using planar sapphire chips as substrates and X-ray photoelectron spectroscopy (XPS) as surface-sensitive tool, the sequence of individual layers was analyzed with respect to their chemical state, enabling the quantification of areal densities of the involved molecular units. Fluorescence microscopy was used to demonstrate the hybridization of fluorescently tagged target DNA to the probe DNA, including denaturation- and re-hybridization experiments. Due to its high thermal conductivity, synthetic sapphire is especially suitable as a chip material for the heat-transfer method, which was employed to distinguish complementary- and non-complementary DNA duplexes containing single-nucleotide polymorphisms. These results indicate that it is possible to detect mutations electronically with a chemically resilient and electrically insulating chip material.
The objectives of the present work are to characterize the Gas Metal Arc Welding process of DP 600 sheet steel and to summarize the modelling techniques. The time-temperature evolution during the welding cycle was measured experimentally and modelled with the softwaretool SimWeld. To model the phase transformations during the welding cycle dilatometer tests were done to quantify the parameters for phase field modelling by MICRESS®. The important input parameters are interface mobility, nucleation density, etc. A contribution was made to include austenite to bainite transformation in MICRESS®. This is useful to predict the microstructure in the fast cooling segments. The phase transformation model is capable to predict the microstructure along the heating and cooling cycles of welding. Tensile tests have shown the evidence of failure at the heat affected zone, which has the ferrite-tempered martensite microstructure.
Bio-feedstocks
(2011)
Purpose
In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.
Design/methodology/approach
In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.
Findings
Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.
Originality/value
For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.