We investigate the effect of water adsorption on the electrical properties of graphene oxide (GO) films using the direct current (DC) measurement and alternating current (AC) complex impedance spectroscopy. GO suspension synthesized by a modified Hummer's method is deposited on Au interdigitated electrodes. The strong electrical interaction of water molecules with GO films was observed through electrical characterizations. The DC measurement results show that the electrical properties of GO films are humidity- and applied voltage amplitude-dependent. The AC complex impedance spectroscopy method is used to analyze the mechanism of electrical interaction between water molecules and GO films in detail. At low humidity, GO films exhibit poor conductivity and can be seen as an insulator. However, at high humidity, the conductivity of GO films increases due to the enhancement of ion conduction. Our systematic research on this effect provides the fundamental supports for the development of graphene devices originating from solution-processed graphene oxide.

Novel capacitive-type humidity sensors with ultrahigh sensitivity based on graphene oxide (GO) combined with Ag nanoparticles (AgNPs) in different concentrations are reported in this study.

Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. However, SLGDA fails to exploit the spatial information. To address this problem, a tensor sparse and low-rank graph-based discriminant analysis (TSLGDA) is proposed in this paper. By regarding the hyperspectral data cube as a third-order tensor, small local patches centered at the training samples are extracted for the TSLGDA framework to maintain the structural information, resulting in a more discriminative graph. Subsequently, dimensionality reduction is performed on the tensorial training and testing samples to reduce data redundancy. Experimental results of three real-world hyperspectral datasets demonstrate that the proposed TSLGDA algorithm greatly improves the classification performance in the low-dimensional space when compared to state-of-the-art DR methods.

A novel quartz crystal microbalance (QCM) humidity sensor using nanodiamond (ND) as sensing films has been presented in this paper. For evaluating the performance of ND-coated QCM humidity sensor, a series of humidity-sensing experiments were carried out by using both oscillating circuit and impedance analysis methods. Based on the large specific surface area and high mechanical modulus, the ND-coated sensor exhibited large humidity-sensing response and good stability. In addition, the resultant sensors displayed low hysteresis, good repeatability, and short response time. The influence of the depositing amount of ND on the sensor's performance has also been discussed. The result indicated that the stability of QCM humidity sensor showed an extreme decrease when the deposition amount of ND on QCM is large enough. The possible reason for this anomalous phenomenon is analyzed and discussed. This study suggests that ND could be a promising candidate material for developing high-performance QCM humidity sensor.