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Solid polymer electrolytes (SPEs) of polyacrylamide-co-acrylic acid (PAA) as the polymer host and zinc acetate (ZnA) as an ionic dopant were prepared using a single solvent by the solution casting technique. The amorphous and crystalline structures of film were investigated by X-ray diffraction (XRD). The surface morphology of samples was examined by scanning electron microscopy (SEM). The composition and complex formation of films were characterized by Fourier transform infrared (FTIR) spectroscopy. The conductivity of the PAA-ZnA films was determined by electrochemical impedance spectroscopy. According to the XRD and FTIR analyses, all electrolyte films were in amorphous state and the existence of interaction between Zn2+ cations and the PAA structure confirms that the film was successfully prepared. The SEM observations reveal that the electrolyte films appeared to be rough and flat with irregularly shaped surfaces. The highest ionic conductivity (σ) of 1.82 × 10-5 Scm-1 was achieved at room temperature (303 K) for the sample containing 10 wt % ZnA.
This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.