In this paper, we propose a passive gap-spacing control method in order to avoid stiction between top and bottom structures in in-plane sensor/actuator/generator applications. A patterned electret using a high-performance perfluoro polymer material is employed to induce a repulsive electrostatic force. An out-of-plane repulsive force is successfully demonstrated with our early prototype, in both air and liquid. By using the present electret-based levitation method to keep the air gap, a MEMS electret generator has been developed for energy-harvesting applications. A dual-phase electrode arrangement is adopted in order to reduce the horizontal electrostatic damping force. With the present prototype, about 0.5 μW is obtained for both phases of the generator, resulting in a total power output of 1.0 μW at an acceleration of 2 g with 63 Hz. With our electromechanical model of the generator, we have confirmed that the model can mimic the response of the generator prototype.
Recently, micro power generation using electrets has attracted much attention due to its large power output at a low frequency range. Since the theoretical power output is proportional to the square of the surface charge density of the electret, the development of a high-performance electret is required. In the present study, it is shown that the surface charge density of a CYTOP electret is significantly improved by the addition of terminal groups. Based on this fact, a novel high-performance polymer electret has been developed by doping a silane-coupling reagent into the polymer. A series of measurements of surface potential and TSD (thermally stimulated discharge) spectra was made for various CYTOP films prepared with different silane-coupling reagent concentrations. It is found that the surface charge density, charge stability and thermal resistibility of electric charges are markedly improved by the doping. A surface charge density of 1.5 mC cm −2 , which is three times larger than that of Teflon AF, has been obtained on a 15 μm thick film. In addition, the thermal stability of the CYTOP electret is superior to that of Teflon AF. Power generation experiment is also performed using the patterned CYTOP electret of 20 × 20 mm 2. At a low seismic frequency of 20 Hz, 0.7 mW power generation has been accomplished, which is about 2.5 times higher than our previous result.
We present the first micromachined rotational electret power generator, linearized theoretical model of electret power generation, and novel method to produce uniformly charged electret. We also improved our previously developed [ I , 21 thin film Teflon AF 1601-S electret technology with respect to dielectric thickness, charge uniformity, and processability. In demonstration, our prototype power generator successfully generated > 25pW with electret thickness of 9pm, effective charge density of -2.8xlO4C/m2, and rotational speed of4170RPM.
ω3 polyunsaturated fatty acids (PUFAs) have anti-allergic and anti-inflammatory properties, but the immune-metabolic progression from dietary oil remains to be investigated. Here we identified 17,18-epoxyeicostetraenoic acid (17,18-EpETE) as an anti-allergic metabolite generated in the gut from dietary ω3 α-linolenic acid (ALA). Biochemical and imaging mass spectrometry analyses revealed increased ALA and its metabolites, especially eicosapentaenoic acid (EPA), in the intestines of mice receiving ALA-rich linseed oil (Lin-mice). In murine food allergy model, the decreased incidence of allergic diarrhea in Lin-mice was due to impairment of mast cell degranulation without affecting allergen-specific serum IgE. Liquid chromatography–tandem mass spectrometry-based mediator lipidomics identified 17,18-EpETE as a major ω3 EPA-derived metabolite generated from dietary ALA in the gut, and 17,18-EpETE exhibits anti-allergic function when administered in vivo. These findings suggest that metabolizing dietary ω3 PUFAs generates 17,18-EpETE, which is an endogenous anti-allergic metabolite and potentially is a therapeutic target to control intestinal allergies.
An application of the neural network to quantitative structure-activity relationship (QSAR) analysis has been studied. The new method was compared with the linear multiregression analysis in various ways. It was found that the neural network can be a potential tool in the routine work of QSAR analysis. The mathematical relationship of operation between the neural network and the multiregression analysis was described. It was shown that the neural network can exceed the level of the linear multiregression analysis.
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