Conductive polymer composites are becoming more important and useful in many electrical applications. This paper reports on the carbon black (CB) reinforced polyvinyl chloride (PVC) conductive composites. Conductive filler CB was reinforced with thermoplastic PVC by compression molding technique to make conductive composites. The particle size of CB was measured, as it affects the electrical conductivity of the composites. Different types of CB-PVC compression-molded composites were prepared, using CB contents from 5 to 30 wt %. The electrical and tensile properties of these composites were studied and compared. Improved electrical properties were obtained for all CB-PVC conductive polymer composites compared to virgin PVC composite. However, the tensile properties of the CB-PVC composites increased up to 15 wt % CB loading, and then decreased, and elongation at break decreased with increasing CB loading. The structure of the CB, PVC and CB-PVC composites were studied by attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic analysis. ATR-FTIR spectra provide evidence of the formation of CB-PVC composites. The microstructural analyses showed a good dispersion of CB in PVC matrix.

In this investigation, a nondestructive technique has been developed for determining chemical composition of jute fiber by chemometric modeling with pretreated FT-NIR spectroscopic data. The chemical composition of jute fibers in wet chemical method were, 58 to 61.80 % α-cellulose, 13.0 to 21.90 % lignin, 9.89 to 16.8 % pentosan and 79.02 to 88.33 % holocellulose. FT-NIR spectral data from range 9000–4000 cm−1 of all jute samples were collected from the instrument. Spectral data of jute samples were pretreated with second order derivatives (SOD), standard normal variate (SNV) techniques and both together were used before calibration. Two chemometric calibration techniques: partial least square regression (PLSR) and artificial neural network (ANN) were assessed for predicting chemical compositions of Jute fibers. Result shows that prediction efficiency ({\text{R}^{2}}) of ANN varies from 72–99 % for calibration, validation and test datasets. However, by PLSR, {\text{R}^{2}} are much higher and consistent than those by earlier one. For α-cellulose, lignin, pentosan and holocellulose {\text{R}^{2}} values hover around 95–99 %. Thereby, a non-destructive, simple and cost effective novel method is being proposed to determine chemical compositions of jute with pretreated FT-NIR spectral data and chemometric calibration techniques.

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