This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals.

Despite technological advances in biomolecule detections, evaluation of molecular interactions via potentiometric devices under ion-enriched solutions has remained a long-standing problem. To avoid severe performance degradation of bioelectronics by ionic screening effects, we cover probe surfaces of field effect transistors with a single film of the supported lipid bilayer, and realize respectable potentiometric signals from receptor–ligand bindings irrespective of ionic strength of bulky solutions by placing an ion-free water layer underneath the supported lipid bilayer. High-energy X-ray reflectometry together with the circuit analysis and molecular dynamics simulation discovered biochemical findings that effective electrical signals dominantly originated from the sub-nanoscale conformational change of lipids in the course of receptor–ligand bindings. Beyond thorough analysis on the underlying mechanism at the molecular level, the proposed supported lipid bilayer-field effect transistor platform ensures the world-record level of sensitivity in molecular detection with excellent reproducibility regardless of molecular charges and environmental ionic conditions.

The applicability of nanomechanical devices for computational approaches is reviewed. The focus is on the representation and processing of information based on nanomechanical bits. Several device concepts are discussed ranging from nano‐electromechanical systems in silicon to circuits based on carbon nano‐tube switches, combinations of nanomechanical resonators and traditional transistors, and integration into a computing architecture. The strengths of mechanical systems include their scalability, robustness to external electrical shocks, and their low‐energy consumption. Hence, they may lead the way to new forms of ultradense memory and alternative routes of computing. In conjunction with quantum mechanical single electron circuits, nano‐electromechanical systems may also have potential for quantum computational circuits.

The adverse effects of air pollution on respiratory health make air quality monitoring with high spatial and temporal resolutions essential especially in cities. Despite considerable interest and efforts, the application of various types of sensors is considered immature owing to insufficient sensitivity and cross‐interference under ambient conditions. Here, a fully integrated chemiresistive sensor array (CSA) with parts‐per‐trillion sensitivity is demonstrated with its application for on‐road NO_x monitoring. An analytical model is suggested to describe the kinetics of the sensor responses and quantify molecular binding affinities. Finally, the full characterization of the system is connected to implement on‐road measurements on NO_x vapor with quantification as its ultimate field application. The obtained results suggest that the CSA shows potential as an essential unit to realize an air‐quality monitoring network with high spatial and temporal resolutions.