This paper studies the exponential stabilization of delayed chaotic neural networks (DCNNs) using what is called periodically intermittent control. An exponential stability criterion for the controlled neural networks, together with its simplified version, is established by using the Lyapunov function and Halanay inequality. The feasible region of control parameters is estimated in a rigorous way. Theoretical results and numerical simulations show that the continuous-time DCNN can be stabilized by intermittent feedback control with nonzero duration.

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As one of the most prevalent diseases in the world, timely early intervention for periodontitis is a great challenge because the indicator is imperceptible. The exhaled H2S is considered to be a promising biomarker for fast and invasive periodontitis screening; however, the high-performance H2S gas sensor with excellent selectivity and sensitivity which is applicable to the oral cavity remains technically challenging. Herein, a self-assembled monolayer (SAM)-functionalized Au/In2O3 nanofiber (NF) sensor for H2S exhalation analysis was developed to flexibly and effectively modulate the selectivity of the sensor. Through optimizing the specific binding capacity to H2S by systematic adjustment with terminal groups and alkyl chains of SAMs, the sensing performance of the SAM-functionalized Au/In2O3 NF sensor is greatly enhanced. In the optimal (Au/In2O3-MPTES) sensor, the functionalization of the MPTES molecule could achieve significant response enhancement because of the stronger interaction between the sulfhydryl group at the end of the MPTES and H2S. Density functional theory simulation supports the proposed selective sensing mechanism via the analysis of adsorption energy and charge density distribution. The sensor exhibited a high response to H2S (1505.3–10 ppm) at an operating temperature of 100 °C with a low practical detection limit of 10 ppb and 13–145 fold enhanced selectivity. Furthermore, the Au/In2O3-MPTES sensor was successfully applied to distinguish the breath of healthy individuals and patients with severe periodontitis. This study provides novel design insights for the development of highly selective gas sensors for clinical aids in the diagnosis and detection of oral diseases such as periodontitis.