In this study, derivatives of polyvinyl alcohol (PVA) grafted with phthalic anhydride (PhA) and dyes were prepared to produce polymeric materials of PVA-g-PhA, PVA-g-PhA-anthocyanin, PVA-g-PhA-bromophenol blue, and PVA-g-PhAthymol blue. The materials were characterised by FTIR and 1 H NMR spectroscopies. The crystallinity of the polymers was evaluated with powder X-ray difraction, and the thermal stability by thermogravimetric analysis (TGA). The synthetic procedure for the polymeric materials entailed the formation of esters. The FTIR spectra of the polymers confrmed their formation since the ester carbonyl group stretch was observed at approximately 1691–1716  cm−1 in each material. NMR spectroscopy confrmed the addition of t

In the current study, synthesis and characterization of silver nanoparticles (AgNPs) before and after functionalization with ampicillin antibiotic and their application as anti-pathogenic agents towards bacteria were investigated. AgNPs were synthesized by a green method from AgNO3 solution with glucose subjected to microwave radiation. Characterization of the nanoparticles was conducted using UV-Vis spectroscopy, scanning electron microscopy (SEM), zeta potential determination and Fourier transform infrared (FTIR) spectroscopy. From SEM analysis, the typical silver nanoparticle particle size was found to be 30 nm and Zeta potential measurements gave information about particle stability. Analysis of FTIR patterns and UV-VIS spectroscopy con

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The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of