Aerosol-jet-printed graphene electrochemical histamine sensors for food safety monitoring

Gomes, Carmen
Claussen, Jonathan
Parate, Kshama
Pola, Cícero
Rangnekar, Sonal
Mendivelso-Perez, Deyny
Smith, Emily
Smith, Emily
Hersam, Mark
Gomes, Carmen
Claussen, Jonathan
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Ames Laboratory
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Mechanical Engineering
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Ames LaboratoryMechanical EngineeringFood Science and Human NutritionVirtual Reality Applications CenterChemistryAgricultural and Biosystems Engineering

Carbon nanomaterials such as graphene exhibit unique material properties including high electrical conductivity, surface area, and biocompatibility that have the potential to significantly improve the performance of electrochemical sensors. Since in-field electrochemical sensors are typically disposable, they require materials that are amenable to low-cost, high-throughput, and scalable manufacturing. Conventional graphene devices based on low-yield chemical vapor deposition techniques are too expensive for such applications, while low-cost alternatives such as screen and inkjet printing do not possess sufficient control over electrode geometry to achieve favorable electrochemical sensor performance. In this work, aerosol jet printing (AJP) is used to create high-resolution (~40 μm line width) interdigitated electrodes (IDEs) on flexible substrates, which are then converted into histamine sensors by covalently linking monoclonal antibodies to oxygen moieties created on the graphene surface through a CO2 thermal annealing process. The resulting electrochemical sensors exhibit a wide histamine sensing range of 6.25–200 ppm (56.25 μM–1.8 mM) and a low detection limit of 3.41 ppm (30.7 μM) within actual tuna broth samples. These sensor metrics are significant since histamine levels over 50 ppm in fish induce adverse health effects including severe allergic reactions (e.g. Scombroid food poisoning). Beyond the histamine case study presented here, the AJP and functionalization process can likely be generalized to a diverse range of sensing applications including environmental toxin detection, foodborne pathogen detection, wearable health monitoring, and health diagnostics.


This is a peer-reviewed, un-copyedited version of an article accepted for publication/published in 2D Materials. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at DOI: 10.1088/2053-1583/ab8919. Posted with permission.