Caracterização de sensor quimiorresistivo nanoestruturado para soluções catiônicas utilizadas em flotação mineral

Abstract

In the iron mining industry, reverse cationic flotation is used as a mineral concentration technique, employing surfactants such as ether-amine and amide-amine as quartz collectors. Monitoring these reagents in industrial effluents is essential both for efficient process control and for mitigating environmental impacts. However, conventional techniques used for this purpose present limitations related to high cost, operational complexity, and analysis time, which justifies the search for alternative approaches based on highly sensitive, low-cost nanoengineered chemical sensors with real-time response. This work presents a chemiresistive sensor based on buckypaper of functionalized multi-walled carbon nanotubes (f-MWCNTs) for the detection and discrimination between amide-amine and ether-amine solutions, as well as salts similar to those found in flotation effluents (pH 9.5–9.8, 30 ppm). The device was characterized by scanning electron microscopy (SEM), Raman spectroscopy, electrical chemiresistive response tests, binding energy calculations using density functional theory (DFT), and statistical analysis through principal component analysis (PCA). Raman analysis revealed distinct doping patterns: amide-amine compensated the NaOH-induced p-type doping in the outer layers (no shift in G'outer) and induced a redshift of 31 cm⁻¹ in G'inner, while ether-amine showed a smaller redshift of 14 cm⁻¹ in G'inner. In electrical tests, the sensor exhibited a higher chemiresistive response to amide-amine solutions, with a variation of 5.29% relative to deionized water, whereas ether-amine showed a response of only 0.42%. DFT calculations indicated binding energies of −1.04 eV for amide-amine and −0.71 eV for ether-amine, confirming the stronger interfacial affinity of amide-amine with the nanotubes. PCA enabled dimensionality reduction of the experimental data, allowing clear discrimination between the analytes based on response intensity (33.96% of the variance) and sensor recovery kinetics (53.93% of the variance). The results obtained from these analyses demonstrate the high capability of the sensor to detect and discriminate chemical analytes containing ether-amine and amide-amine.