RESUMEN
Laser-induced graphene (LIG) has emerged as a highly versatile material with significant potential in the development of electrochemical sensors. In this paper, we investigate the use of LIG and LIG functionalized with ZnO and porphyrins-ZnO as the gate electrodes of the extended gate field effect transistors (EGFETs). The resultant sensors exhibit remarkable sensitivity and selectivity, particularly toward ascorbic acid. The intrinsic sensitivity of LIG undergoes a notable enhancement through the incorporation of hybrid organic-inorganic materials. Among the variations tested, the LIG electrode coated with zinc tetraphenylporphyrin-capped ZnO nanoparticles demonstrates superior performance, reaching a limit of detection of approximately 3 nM. Furthermore, the signal ratio for 5 µM ascorbic acid relative to the same concentration of dopamine exceeds 250. The practical applicability of these sensors is demonstrated through the detection of ascorbic acid in real-world samples, specifically in a commercially available food supplement containing l-arginine. Notably, formulations with added vitamin C exhibit signals at least 25 times larger than those without, underscoring the sensors' capability to discern and quantify the presence of ascorbic acid in complex matrices. This research not only highlights the enhanced performance of LIG-based sensors through functionalization but also underscores their potential for practical applications in the analysis of vitamin-rich supplements.
RESUMEN
Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m2 meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.