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Non-invasive measurement of membrane morphology via UFDR: pore-size characterization

机译:通过UFDR进行膜形态的无创测量:孔径表征

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This paper describes the development of an ultrasonic technique for the characterization of membrane morphology. Ultrasonic frequency-domain reflectometry (UFDR) using a 90 MHz focused immersion transducer has been employed to obtain characteristic acoustic responses from microporous polymeric membranes with nominal pore sizes in the range of 0.1-0.6 +m. Systematic and statistically significant differences in signal amplitude are observed in the frequency domain for reflections from the back surface of poly(vinylidene fluoride) (PVDF) and mixed cellulose ester (MCE) membranes. As the pore-size of the membranes increases, the amplitude of the reflected signal at high frequencies decreases due to increased scattering of the ultrasonic wave. These UFDR differences correspond well with those obtained from independent measurements including scanning electron microscopy (SEM) and gas-liquid porometry. A simple phenomenological artificial neural network (ANN) model has been developed to predict membrane pore-size based upon the signal amplitude at five frequencies. Such predictive capability suggests that the combination of UFDR and ANN may have significant utility, for membrane quality control applications.
机译:本文介绍了用于表征膜形态的超声技术的发展。已采用使用90 MHz聚焦浸没式换能器的超声频域反射法(UFDR)从标称孔径在0.1-0.6 + m范围内的微孔聚合物膜获得特征性的声学响应。从聚偏二氟乙烯(PVDF)和混合纤维素酯(MCE)膜的背面反射的频域中,观察到信号幅度的系统性和统计学意义上的显着差异。随着膜的孔径增大,由于超声波的散射增加,高频反射信号的振幅减小。这些UFDR差异与从独立测量(包括扫描电子显微镜(SEM)和气液孔法)获得的差异非常吻合。已经开发了一种简单的现象学人工神经网络(ANN)模型,可以基于五个频率下的信号幅度来预测膜孔径。这种预测能力表明,UFDR和ANN的结合对于膜质量控制应用可能具有重要的用途。

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