Detection of Aromatic Compounds with Inkjet-Printed Polymers

© 2024 EPFL

© 2024 EPFL

Congratulations to Dr. Mohammad Kiaee, former PhD student at LMIS1, for his new publication entitled "Near-Room-Temperature Detection of Aromatic Compounds with Inkjet-Printed Plasticized Polymer Composites" in the journal "ACS Sensors".

Chemiresistive gas sensors composed of a thermoplastic polymer matrix and conductive fillers offer various advantages for detecting volatile organic compounds (VOCs), including low power consumption due to near-room-temperature operation, high sensitivity, and inherent selectivity toward VOCs. However, such sensors have a slow response time as the polymer matrix often has a glass transition temperature (Tg) higher than the sensor operating temperature slowing the analyte diffusion to/from the polymer. A plasticizer lowers polymer Tg to match the sensor operation temperature, reducing its response time. In this study, the effect of a plasticizer diethylene glycol dibenzoate (DEGDB) on the sensing properties of polystyrene (PS)-carbon black (CB) composite is investigated to obtain sensors with a fast response time and high sensitivity to VOCs. The sensors are fabricated via drop-on-demand inkjet printing, providing a high degree of control over the sensory film morphology and reproducibility. A designof- experiment (DoE) approach is adopted to find the optimum ink and print parameters with a minimum number of experiments. As a result, sensors with 30 times faster response time and 25 times higher effective sensitivity are obtained while operating near room temperature (27 °C). Furthermore, the sensors show high sensitivity toward aromatic hydrocarbons (toluene, benzene, and ethylbenzene), with a sub-10 ppm limit of detection (LoD) and a negligible sensitivity toward humidity. Our results show the potential of PS-DEGDB-CB composite as a selective and cost-effective sensory material compatible with large-scale manufacturing techniques for selective near-room-temperature detection of toxic VOCs.

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This study is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Project “MEMS 4.0″, ERC-2016-ADG, grant agreement No. 742685).