Graphene is a good candidate for filling the market requirements for cheap, high sensitivity, robust towards contamination, low noise, and low power consumption gas sensors, thanks to its unique properties, i.e., large surface, high mobility, and long-term stability. Inkjet printing is a cheap additive manufacturing method allowing fast, relatively precise and contactless deposition of a wide range of materials; it can be considered therefore the ideal technique for fast deposition of graphene films on thin substrates. In this paper, the sensitivity of graphene-based chemiresistor gas sensors, fabricated through inkjet printing, is investigated using different concentrations of graphene in the inks. Samples have been produced and characterized in terms of response towards humidity, nitrogen dioxide, and ammonia. The presented results highlight the importance of tuning the layer thickness and achieving good film homogeneity in order to maximize the sensitivity of the sensor.

In this paper, the hardware implementation of a burst error channel and a burst erasure channel simulator in Cyclone II Field Programmable Gate Array (FPGA) is proposed. In telecommunications, a burst error channel is a data transmission channel in which errors occur in a contiguous sequence of symbols, such that the first and last symbols are in error and there exists no contiguous subsequence of m correctly received symbols within the error burst. An erasure channel is one in which each transmitted symbol is either received correctly or is corrupted so badly as to be considered erased. When the erasures are clustered together we refer to the channel as a burst erasure channel. Although software simulations are easy to set up to simulate a transmission channel behavior, they are very time consuming. In order to speed up the communication system performance evaluation process and the final parameter optimization design, direct hardware emulation is proposed and presented. The implementation can be easily extended to other FPGA architectures

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In this work we investigate and optimize graphene based inks to achieve a stable and well-controllable jetting behavior using a DoD (Drop on Demand) inject printer which has all the required characteristics of a tool for mass production.