Advanced analytical applications require smart materials and sensor systems that are able to adapt or be configured to specific tasks. Based on reversible photochemistry in nanoporous materials, we present a sensor array with a selectivity that is reversibly controlled by light irradiation. The active material of the sensor array, or electronic nose (e-nose), is based on metal-organic frameworks (MOFs) with photoresponsive fluorinated azobenzene groups that can be optically switched between their and state. By irradiation with light of different wavelengths, the - ratio can be modulated. Here we use four - values as defined states and employ a four-channel quartz-crystal microbalance for gravimetrically monitoring the molecular uptake by the MOF films. We apply the photoprogrammable e-nose to the sensing of different volatile organic compounds (VOCs) and analyze the sensor array data with simple machine-learning algorithms. When the sensor array is in a state with all sensors either in the same - or -rich state, cross-sensitivity between the analytes occurs and the classification accuracy is not ideal. Remarkably, the VOC molecules between which the sensor array shows cross-sensitivity vary by switching the entire sensor array from to . By selectively programming the e-nose with light of different colors, each sensor exhibits a different isomer ratio and thus a different VOC affinity, based on the polarity difference between the - and -azobenzenes. In such photoprogrammed state, the cross-sensitivity is reduced and the selectivity is enhanced, so that the e-nose can perfectly identify the tested VOCs. This work demonstrates for the first time the potential of photoswitchable and thus optically configurable materials as active sensing material in an e-nose for intelligent molecular sensing. The concept is not limited to QCM-based azobenzene-MOF sensors and can also be applied to diverse sensing materials and photoswitches.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654041PMC
http://dx.doi.org/10.1039/d1sc05249gDOI Listing

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