Graphene nanofluids have emerged as potential alternatives to improve the thermal efficiency of different engineering devices. However, their use in different applications that include polar liquids as base fluids is a problem due to the hydrophobic nature of graphene. In this study, we compared methods that use surfactants with those that employ surface oxidation (surfactant-free) to improve the dispersion of graphene in water and its stability over time. Cetyltrimethylammonium bromide and sodium dodecylbenzenesulfonate (SDBS), at their critical micelle concentration in water, were used as surfactants to prepare commercial graphene dispersions, and the electrochemical method was adopted to synthesize GO. Additionally, the effect of the concentration of electrolyte (sodium sulfate, Na₂SO₄) on oxygen functionalities in GO synthesis was evaluated and associated with nanofluid stability. The nanomaterials were characterized by thermogravimetric analysis, Fourier transform infrared spectroscopy, Raman spectroscopy, and scanning electron microscopy. Nanofluid stability was evaluated using the zeta potential and UV–VIS spectroscopy. The results show that the electrolyte has an important effect on the oxidation degree of the GO obtained: a high electrolyte concentration (0.5 M) produced the highest oxygen functionalities. Finally, the stability of the nanofluid produced with the electrolyte at 0.5 M was similar to that of the graphene nanofluid prepared with SDBS as surfactant. Graphical Abstract

Recently, antibacterial materials have sparked a renewed interest in the fields of biomedical engineering and life sciences. The main purpose of this study was to evaluate the physicochemical properties of TiO2 nanoparticles with anatase phase and an average size of 24.1 ± 4.6 nm, graphene oxide (GO) obtained from the electrochemical method, and TiO2/GO hybrid nanomaterial. Thermogravimetric analysis (TGA) revealed the presence of oxygen functionalities in the GO structure and 23.2 % of TiO2 in the hybrid nanomaterial, as well as a strong interaction between the materials that can be observed in the micrograph of scanning electron microscopy (SEM). Antibacterial tests were performed using the macrodilution method. The results showed that, while GO did not decrease bacterial growth, TiO2 presented high bactericidal activity. In turn, the hybrid TiO2/GO nanomaterial did not show such activity. This result can be explained by the decrease in contact between TiO2 and bacterial cells due to the blocking of the active sites on the TiO2 surface by graphene oxide sheets. These results contribute to the ongoing discussion about the bactericidal properties of graphene oxide.

Piezoelectric nanogenerators (PENGs) are used to monitor physiological signals. Since they are made of piezoelectric materials, zinc oxide can be a promising option for these devices thanks to its biocompatibility. However, its electrical output is not competitive compared to that of lead-based materials. This disadvantage can be reduced by means of doping and morphology control strategies. In this study, PENG was developed using a conductive textile as electrodes and zinc oxide as piezoelectric material. Polyvinylidene fluoride (PVDF) modified with zinc oxide was evaluated as a piezoelectric generator. Zinc oxide was synthetized with two morphologies: rods and sheets. The results show that zinc oxide can improve the electrical output of this PENG, which was evaluated in a universal testing machine. The output voltage of this generator was higher with zinc oxide rods than with sheets. Finally, compared to the unmodified PVDF, the composites modified with rods and sheets produced higher output voltages: 71.4% and 26.3%, respectively.

Electroencephalographic (EEG) signals have been extensively studied for emotion detection and, more recently, as an alternative for biometric identification and authentication. Biometric methods based on physiological signals are a non-conventional approach for personal identification, and their study is currently considered an open research field. However, EEG-based biometric systems face several challenges, including the influence of emotional states, which can affect their performance. This study evaluates the effect of emotional states on the performance of an EEG-based biometric system. Four widely used databases for biometrics and emotion recognition (DEAP, MAHNOB, SEED, and LUMED-2) were selected for analysis. Feature extraction was performed using multiple strategies in the time, frequency, and time–frequency domains. The performance of various classifiers—support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and k-nearest neighbors (K-NN)—was evaluated separately. Furthermore, stacking was used as a classifier fusion method. Explicit modeling of emotional states contributed to improving classifier performance. The best model based on classifier fusion achieved an accuracy of 95.73 ± 1.83%. These results indicate that incorporating information about emotional state into EEG-based biometric systems can contribute to the development of more robust and realistic identification solutions.