Study of the correlation between sensing performance and surface morphology of inkjet-printed aqueous graphene-based chemiresistors for NO2 detection.

The extremely high sensitivity to the external environment and the high specific surface area, as well as the absence of bulk phenomena that could interfere with the response signal, make graphene highly attractive for the applications in the field of sensing. Among the various methods for producing graphene over large areas, liquid phase exfoliation (LPE) appears to be very promising, especially if combined with inkjet printing (IJP), which offers several advantages, including the selective and controlled deposition of small ink volumes and the versatility of the exploitable inks and substrates. Herein we present a feasibility study of chemiresistive gas sensors inkjet-printed onto paper substrates, in which a LPE graphene suspension dispersed in a water/isopropanol (H2O/IPA) mixture is used as sensing ink. The device performances, in terms of relative conductance variations, upon exposure to NO2 at standard ambient temperature and pressure, are analysed. In addition, we examine the effect of the substrate morphology and, more specifically, of the ink/substrate interaction on the device performances, by comparing the response of different chemiresistors fabricated by dispensing the same suspension also onto Al2O3 and Si/SiO2 substrates and carrying out a supportive atomic force microscopy analysis. The results prove the possibility to produce sensor devices by means of a wholly environmentally friendly, low-cost process that meets the requests coming from the increasing field of paper-based electronics and paving the way towards a flexible, green-by-design mass production.

Compression of SAR raw data through range focusing and variable-rate trellis-coded quantization.

There is an ever-growing interest in the compression of SAR data because of the huge resources required for storage and transmission. This is especially true for spaceborne sensors, given the limited capacity of the downlink channel. Unfortunately, SAR data lack the useful properties on which compression algorithms rely; indeed, these are present in the focused images, but focusing is too complex for on-board implementation at this time. Poggi et al. (2000) proposed to perform on the satellite only the low-complexity range focusing, which increases the data correlation and better concentrates their energy. These properties were then exploited by adopting a variable-rate vector quantizer, with a clear performance improvement with respect to reference techniques. However, vector quantization (VQ) is too complex for actual on-board implementation, and therefore, here we replace VQ with trellis-coded VQ. To limit complexity, only small vectors are used, which reduces VQ's ability to exploit data dependencies; on the other hand, trellis coding allows one to encode large blocks of data at once, and to obtain a better partition of the input space. Experiments on real SAR data show that the overall performance is comparable to that of Poggi et al., but the complexity is much lower, making on-board implementation possible.