Resonance-enhanced multiphoton ionization time-of-flight mass spectrometry (REMPI-TOFMS) was used to study the characteristic signal behaviors obtained from two types of emulsions: water-in-oil (W/O) and oil-in-water (O/W). All emulsions were prepared using phase inversion emulsification, i.e., a solution for an aqueous phase was added dropwise to an oil phase with constant stirring to obtain an emulsion. Toluene served as a detection component. When using REMPI-TOFMS to measure an emulsion, a time profile for the target component can be constructed by plotting peak areas for the corresponding component on a series of mass spectra. In the case of a W/O emulsion at a water volume fraction (f w) of 0.005, the concentration of toluene was instantaneously decreased due to the existence of water droplets, and therefore, several negative spikes were detected on the time profile while establishing a baseline. In the case of a W/O emulsion at f w = 0.3, negative peaks consisting of several plots appeared on the time profile because of the formation of aggregates of water droplets while the emulsion was flowed through a capillary column for sample introduction. An O/W emulsion at f w = 0.995 was analyzed following phase inversion, and positive peaks were detected due to the aggregates of many oil droplets. In this manner, the direct mass analysis of emulsions before and after phase inversion was achieved, and the resultant signal inversion was confirmed via REMPI-TOFMS.

In this study, we used a quantitative analytical method to indicate creaming behavior in an emulsion. An oil-in-water emulsion was directly measured by resonance-enhanced multiphoton ionization time-of-flight mass spectrometry, and the time profiles of the peak areas of an oil component, styrene, were obtained at heights of 1, 2, and 3 cm from the bottom of a sample that had a height of 4 cm. All time profiles roughly indicated that the signal intensity increased once, then decreased, and finally settled. Moreover, we proposed a fitting equation for the time profiles by subtracting two sigmoid functions, whereby the degree of the signal increases at the initial stage, the degree of the signal decreases after the increase, and the times for continuing the higher signal intensities were all longer as the monitoring positions were raised. This method would surely provide useful information about emulsions that undergo creaming behavior.

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In this work, we propose a model that incorporates QT‐interval variability (QTV) by extending the synthetic electrocardiogram (ECG) model including heart rate variability (HRV). The model consists of three modified coupled ordinary differential equations and an equation to incorporate QTV by shifting the position of the T‐wave; in addition, RR and QT tachograms that have frequency characteristics composed of multiple Gaussian distribution peaks are used. An ECG signal can be generated by adapting a certain ECG waveform and by giving the means, standard deviations, and power spectral density parameters of HRV and QTV. This proposed model may reveal the neurophysiological mechanisms underlying the relationship between QTV and HRV and/or the autonomic activity, and it may solve problems in the measurement of QT‐interval. In conclusion, this proposed model is a very useful tool in medical education and experimental simulation. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.