Solid-contact ion-selective electrode for the determination of silver ions released from silver sulfadiazine in sodium hyaluronate formulations: evaluation of whiteness and greenness profiles

Potentiometric sensors were designed with a focus on rapid, environmentally friendly, cost-efficient, and highly specific detection. These sensors were specifically tailored for the analysis of silver ions released from silver sulfadiazine (SSD) in combination with sodium hyaluronate (SH) in their combined dosage form. The manufacturing process involved a two-step optimization procedure. Initially, various ionophores were evaluated to enhance the selectivity of the sensors, with Calix [4]arene demonstrating the highest affinity for silver ions. The inclusion of a cation-exchanger in the membrane ensured selective response toward cations namely, Ag⁺ ions from SSD thereby exhibiting permselectivity. In the second optimization phase, a layer of multi-walled carbon nanotubes (MWCNTs) was incorporated between the Calix[4]-containing polymeric membrane and the solid-contact screen-printed electrode (SPE). This MWCNT layer served as an ion-to-electron transducer, improving potential stability by mitigating drift. This stability enhancement is likely due to its hydrophobic nature, which prevents the formation of a water layer at the interface between the electrode surface and the polymeric sensing membrane. The sensor, developed in accordance with IUPAC recommendations, exhibited high selectivity for Ag⁺ ions from SSD in the presence of SH in the pharmaceutical dosage form. The MWCNT-modified sensor achieved high accuracy (99.94% ± 0.413), a linear response in the concentration range of 1.0 × 10⁻⁵ to 1.0 × 10⁻² M, and a detection limit of 4.1 × 10− 6 M. The slope, calculated from the linear portion of the calibration curve, was found to be 61.029 mV/decade, indicating near-Nernstian behavior. To evaluate the environmental and health implications of the proposed method in comparison to a previously reported technique, comprehensive assessment tools including the Analytical Eco-scale, GAPI, AGREE, and RGB12 model were employed for greenness and whiteness profiling.