Nanoelectronic Sensors for Real-Time Health and Environmental Monitoring

Nanoelectronics sensors are revolutionizing realtime monitoring applications in both healthcare and environmental domains due to their miniaturized size, high sensitivity, and low power consumption. This paper introduces a novel nanoelectronics sensor framework that combines carbon nanotubes and graphene-based materials with an optimized signal acquisition system for real-time health and environmental monitoring. Extensive experimental evaluation against conventional machine learning classifiers such as SVM, Random Forest, KNN, and Naive Bayes demonstrates the superior performance of the proposed approach. It achieves an accuracy of 96.5%, F1-score of 96.3%, and maintains computational efficiency with only 1.2 seconds of training time and 8 ms inference time. These results indicate not only robust classification capability but also energy and memory efficiency, making the solution ideal for edge computing and wearable applications. To provide portability and maximal adaptability, the system is additionally fabricated using nanolithography onto flexible substrates. In that vein, this research highlights how intelligent nanoelectronic health and environmental monitoring systems driven by precision, scalability, and real-time intelligence can form the building blocks of tomorrow’s smart cities. These challenges of noise, signal degradation, and selective detection are tackled first through hardware-optimized analog front ends and second through nanomaterial functionalization, followed by high-fidelity data acquisition.