Changes in Rheological Properties of Mortars with Steel Slags and Steel Fibers by Magnetic Field.

The effect of a magnetic field on the rheology of mortars with steel slags and fibers was evaluated in this study. The rheology of the mortar measured with and without a magnetic field was compared. The effect of steel fiber addition to normal and steel slag mortars, mix ratio and size of steel fibers, and magnetic field formation position on rheology were evaluated. Steel fiber addition increased the yield stress and viscosity of the normal and steel slag mortars. The increased rheology was almost restored because of the magnetic field applied to the normal mortars. However, the increased rheology of the steel slag mortars with steel fibers was restored only upon the application of the magnetic field, whose position was continuously changed by a power relay. It is deduced that the alignment of the steel fibers by the magnetic field contributes to the rheology reduction of the mortars. However, in the case of steel slag mortar, experimental results demonstrated that steel slag, which is a ferromagnetic material, receives constant force by the magnetic field, which increases the rheology. This is evidenced by the decrease in the rheology of steel slag mortars under a continuously changing magnetic field formation position by power relay.

Quantitative image analysis of broadband CARS hyperspectral images of polymer blends.

We demonstrate that broadband coherent anti-Stokes Raman scattering (CARS) microscopy can be very useful for fast acquisition of quantitative chemical images of multilayer polymer blends. This is challenging because the raw CARS signal results from the coherent interference of resonant Raman and nonresonant background and its intensity is not linearly proportional to the concentration of molecules of interest. Here we have developed a sequence of data-processing steps to retrieve background-free and noise-reduced Raman spectra over the whole frequency range including both the fingerprint and C-H regions. Using a classical least-squares approach, we are able to decompose a Raman hyperspectral image of a tertiary polymer blend into quantitative chemical images of individual components. We use this method to acquire 3-D sectioned quantitative chemical images of a multilayer polymer blend of polystyrene, styrene-ethylene/propylene copolymer, and polypropylene that have overlapping spectral peaks.