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There have been extensive studies on conducting polymers and their use as chemical sensors especially for detection of odours. Among them, polyaniline (PANI) stands out due to its higher environmental stability and lower price. PANI is known to be sensitive to ammonia because of the high affinity of protonated N atoms on doped-PANI backbone to NH3. However, due to its lack of solution processability it is difficult to fabricate thin films of PANI. We have developed a vapour-phase deposition polymerisation (VDP) technique to in situ polymerise a very thin Nafion-doped PANI sensing layer on the flexible substrate. The conductivity of the sensor is reduced on exposure to ammonia even in a humid environment. The sensor shows sensitive, rapid and reversible response to very low concentrations of ammonia vapour in the range of 250-1500 ppb at room temperature. Moreover, the sensor shows a reversible increase in conductivity upon increasing humidity. Hence, a low-cost and flexible sensor for ammonia and humidity detection at room temperature is realised using a VDP method which is compatible with common solution-deposition techniques. The sensor can be used in smart tags for monitoring of perishable goods during the transportation chain.

Olfactory receptor neurons in the mammalian olfactory system transduce the odor information into electrical signals and project these into the olfactory bulb. In the biological system the huge redundancy and the massive conver- gence of the olfactory receptor neurons to the olfactory bulb are thought to enhance the sensitivity and selectivity of the system. To explore this concept, modular polymeric chemical sensor array consisting of up to 65,536 sensors belonging to tens of different types was built. To interface such a large sen- sor array, a topological array configuration with n rows and m columns was adopted in order to reduce the number of wiring connections, and 16 subarray modules are simultaneously read to make the acquisition speed comparable to biological olfaction. The matrix array configuration is not without conse- quence. It introduces electrical cross-talk which makes the response of each sensor dependent also on the responses of other sensors in the array. An analy- sis of the electrical cross-talk and the solution adopted to compensate for it are here presented together with some discussions on advantages and limitations of the scanning algorithm. Preliminary experiments were carried out on the prototype to test its capability in terms of odor discrimination, classification, and binary mixtures segmentation. The data were analyzed using multivariate methods and the results indicate that the large scale array developed can emu- late several aspects found in biological olfaction.

In Europe, 239 million tons of organic wastes are produced per year by restaurants, hotels, markets, fisheries and other small to medium-size agri-food industries. The specific management of such wastes involves costly treatments and potential hygiene issues on-site. ORION aims at allowing most of these SMEs to manage their organic waste in situ in order to decrease the treatment costs and increase onsite hygiene conditions. Wastes will be valorized to produce bio-energy and a residue usable as a fertilizer. ORION relies on an interdisciplinary group of research centers in order to achieve the technical goals of the project, in particular the preparation and optimization of wastes to be treated as substrate for biomethanization, choice of appropriate captors and sensors for the bioprocess monitoring (digestate and gas), design of digester systems, digestion assays in bench-scale and pilot scale digesters, use of biogas energy, post-treatment and possible applications of the residues, e.g. as soil fertilizers., Proceedings of the 21st European Biomass Conference and Exhibition, 3-7 June 2013, Copenhagen, Denmark, pp. 1271-1274

A neural architecture, based on several self-organising maps, is presented which counteracts the parameter drift problem for an array of conducting polymer gas sensors when used for odour sensing. The neural architecture is named mSom, where m is the number of odours to be recognised, and is mainly constituted of m maps; each one approximates the statistical distribution of a given odour. Competition occurs both within each map and between maps for the selection of the minimum map distance in the euclidean space. The network (mSom) is able to adapt itself to new changes of the input probability distribution by repetitive self-training processes based on its experience. This architecture has been tested and compared with other neural architectures, such as RBF and Fuzzy ARTMAP. The network shows long-term stable behaviour, and is completely autonomous during the testing phase, where re-adaptation of the neurons is needed due to the changes of the input probability distribution of the given data set.