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.

Aminated lignin (AmL) has significant potential as a superior bio-adsorbent due to its unique structure and properties. In this study, AmL was synthesized by a Mannich reaction from coconut fiber alkali lignin (CFAL). The structure of CFAL and AmL was confirmed with the help of FT-MIR, ¹H-NMR spectroscopy, and CHNS elemental analysis. The crystallinity, morphology, and thermal properties were measured using XRD, SEM, and TGA, respectively. Finally, AmL was used to remove the anionic Congo red (CR) dye from an aqueous solution to investigate the performance of AmL compared to CFAL. The effect of pH, dose of adsorbent, and concentration of dye solution was investigated. 95.5% of the CR dye was removed within 80 minutes at pH 5 from a 20 ppm solution using 0.025 g of AmL. The dye removal efficiency of AmL samples was better than that of CFAL. This work demonstrates that AmL shows greater promise as a bio-adsorbent and can be further utilized as a green alternative in wastewater treatment. Thermodynamic analysis revealed that the adsorption process is spontaneous and exothermic, with negative Gibbs free energy (ΔG°) and enthalpy (ΔH°) values; AmL (ΔH° = −52.06 kJ mol⁻¹) displayed a stronger adsorption affinity than CFAL. In addition, reusability studies demonstrated that AmL retained 83.4% of its adsorption efficiency after four adsorption–desorption cycles, highlighting strong operational stability compared with CFAL. To complement the experimental findings, density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations were carried out at the B3LYP/6-31G(d) basis set level. The results revealed that amination slightly reduces the HOMO–LUMO band gap (4.761 → 4.747 eV), enhances charge delocalization, and stabilizes excited states through partial n → π* transitions introduced by nitrogen functionalities. Electrostatic potential (ESP) mapping further demonstrated moderated charge localization and more uniform surface polarity in AmL, providing additional active sites for electrostatic interactions and hydrogen bonding with CR dyes. The theoretical insights are consistent with the experimental findings, indicating that electronic modification via amination enhances both adsorption affinity and charge-transfer efficiency.

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 ( R 2 {\text{R}^{2}} ) of ANN varies from 72–99 % for calibration, validation and test datasets. However, by PLSR, R 2 {\text{R}^{2}} are much higher and consistent than those by earlier one. For α-cellulose, lignin, pentosan and holocellulose R 2 {\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.