The effect of sodium alkyl sulfates (SAS) on the production of hydroxyl radical (･OH) from collapsing cavitation bubbles generated by irradiation with ultrasound in water was investigated. The surface physicochemical property, that is, dynamic surface tension, elongation of lamellae, and surface viscosity, of the SAS solution was also measured to correlate with the ･OH generation. Sodium dodecyl sulfate (SDS), sodium octyl sulfate (SOS), and sodium ethyl sulfate (SES) were employed as SAS. SAS was added to 0.1 mol/L aqueous disodium terephthalate solution, which is one of the florescence probes to detect ･OH, and was irradiated with ultrasound (47 kHz, 60 W). SDS and SOS prevented the generation of ･OH. However, SES promoted ･OH generation, especially at the low SES concentration. According to the result of dynamic surface tension, the generation of cavitation bubbles was accelerated as follows: SDS＞SOS＞SES≒without SAS. Moreover, the results of elongation of lamellae and surface viscosity demonstrated that the order of stability of cavitation bubbles was SDS＞SOS＞without SAS＞SES. Therefore, these results suggest that the promotion of ･OH generation by SES is associated with accelerated collapse of cavitation bubbles due to reduction in cavitation bubble stability.
Hydrogels have been utilized to design cell culturing scaffolds because they have a high potency to mimic biological environments due to their high water content. Viscoelasticity of the hydrogels has been reported to largely affect properties including differentiation and extracellular matrix formation of cells cultured in them. In practical use, biodegradable hydrogels, which continuously decrease the viscoelasticity, are highly demanded and installation of the biodegradability has been reported to improve the cells properties; however it is still unclear how cells respond to dynamic viscoelasticity change. Elucidation of this issue would promote future scaffold design and also tissue formation theory from cells.
The principle and structural analysis on polymer thin films and interfaces were overviewed for grazing-incidence scattering techniques with X-ray and neutron. The scattering contrasts for X-ray and neutron are defined by electron density and scattering length density, respectively, of materials. Deuterium labeling is adopted for neutron to make contrast in a sample without changing its physical properties by using a large difference in scattering length between a light hydrogen and a deuterium. Moreover, X-ray and neutron can non-destructively probe a deeply-buried interface, and can make an in-situ and time-resolved measurement under various sample environments due to their high transmissivity.
Although odor classification/quantification has been performed using pattern recognition of multiple-sensor output pattern in odor sensing systems, it has been limited to small-scale data. However, the amount of data to be handled is drastically increased using deep learning techniques. We proposed a method to predict odor impression from mass-spectrum data with a dimension of a few hundred using a deep learning technique and found that its prediction accuracy is higher than conventional linear techniques. Then, groups of odor descriptors were obtained to handle large-scale sensory data with binary value. It was found that natural grouping was obtained using natural language processing compared with conventional methods using correlation coefficient. Finally, Itakusa-Saitoh divergence was used as a cost function of the auto encoder. The features of small peaks were extracted using this method. In summary, useful information is obtained when mass spectra are analyzed using a deep learning technique.