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Fabrication and Failure Prediction of Carbon-alum solid composite electrolyte based humidity sensor using ANN

机译:基于人工神经网络的碳铝固体复合电解质湿度传感器的制作与故障预测

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Using low-cost materials, carbon, and alum, a new solid composite electrolyte system was fabricated and characterized using various techniques. Complex impedance spectroscopy was used to measure its ionic conductivity. A maximum conductivity of 3.4×10~(?5) S/cm was obtained when alum was doped with 40% carbon. An Arrhenius behavior was reported when the temperature dependence of conductivity was analyzed. Scanning electron microscopy was used to study the surface morphology of the synthesized electrolyte. Fourier transform infrared and X-ray diffraction results confirmed the formation of composite material. The synthesized solid composite electrolyte exhibited excellent humidity sensing behavior in the relative humidity range of 15%–65%. Various humidity characteristics of the sample were measured such as hysteresis loop, recovery, and response time of the sensor. An expert system was modeled using artificial intelligence techniques and failure of the sensor was predicted with 97.2% accuracy using artificial neural networks.
机译:使用低成本的材料,碳和明矾,使用各种技术制造并表征了一种新型的固体复合电解质系统。复数阻抗谱用于测量其离子电导率。当明矾掺有40%的碳时,可获得的最大电导率为3.4×10〜(?5)S / cm。当分析电导率的温度依赖性时,报告了阿累尼乌斯行为。扫描电子显微镜用于研究合成电解质的表面形态。傅里叶变换红外和X射线衍射结果证实了复合材料的形成。合成的固体复合电解质在15%–65%的相对湿度范围内表现出出色的湿度感应行为。测量了样品的各种湿度特性,例如磁滞回线,恢复率和传感器的响应时间。使用人工智能技术对专家系统进行建模,并使用人工神经网络以97.2%的精度预测传感器的故障。

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