The formaldehyde detection is important for protecting human health and controlling environment pollution. Many metal oxide sensors have been developed for the formaldehyde detection in the last decade. The NiO sensor is considered as the most sensitive one, which is able to detect very low concentration of formaldehyde (<1 ppm). But it needs a high operating temperature. Presently, graphene has attracted much attention for sensor applications. Its high surface-to-volume ratio possesses the potential ability to detect the presence of a single interacting molecule and its high carrier mobility ensures low electrical noise and low power consumption. However, pristine graphene is chemically inert and shows weak adsorption of formaldehyde molecules. To obtain stronger adsorption ability, thereby a higher sensitivity, it is necessary to functionalize or pretreat graphene. In this study, we design a new hybrid sensor, in which graphene acts as a highly conductive network and NiO as a sensitive layer for formaldehyde. This hybrid sensor combines the advantages of both materials. Comparing to the pure graphene sensor and the usual NiO sensor, the hybrid sensor demonstrates better sensing performances, such as faster response and recovery, importantly, operating at room temperature. This sensor could be easily integrated with complementary metal oxide semiconductor (CMOS) chip in the future.
Multifrequency test can maximize differences between the failure state and the normal state of the analog circuit's response,and Neural Networks(NNs)have the ability to solve complex classification problems.An efficient approach for diagnosing faults in analog circuits is presented.It is based on the advantages of both multifrequency test and NNs.The sensitivity analysis is used to generate and choose the multifrequency test vectors of the Circuit Under Tes(tCUT).Fault features of the test point in CUT are extracted and fused.NNs are used to classify the features in a variety of state for the detection and location of faulty components in CUT.The experimental results show that this method is very effective and highly practical for fault diagnosis of analog circuits.
