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Highly selective, fast detection of specific lung-cancer biomarkers (CMs) in exhaled human breath is vital to the development of enhanced sensing devices. Today, e-nose is a promising approach for the diagnosis of lung cancer. Nevertheless, considerable challenges to early-stage disease diagnostics still remain: e.g. decrease in sensor sensitivities in the presence of water vapor, sensor drift leading to the inability to calibrate exactly, relatively short sensor lifetimes, and difficulty discriminating between multiple diseases. However, there is a wide scope for breath diagnostics techniques, and all advanced electrodes applicable to e-nose devices will benefit them. Here, we present the promising sensing capabilities of bare multi-layer graphene (MLG) as a proof of concept for advanced e-nose devices and demonstrate its utility for biomolecule discrimination of the most common lung CMs (ethanol, isopropanol, and acetone). We report on a comparative study involving exposure of the three CM solutions on flat MLG (f-MLG) and patterned MLG (p-MLG) electrodes, where the electrical conductivity of p-MLG is significantly increased while applying acetone. Based on sensitivity tests, we demonstrate the ability to monitor the electrical response of graphene electrodes employing graphene of various wettabilities. Specifically, the f-MLG electrode displays almost 2 times higher sheet resistance (30 Ω sq-1) compared to the hydrophilic p-MLG (12 Ω sq-1). We show significant sensitivity to selected specific molecules of pristine f-MLG and p-MLG while applying CM solutions with a 1.4 × 105 ppm concentration. Finally, we show the selectivity of f-MLG and p-MLG-based sensors when exposed to 2.0 × 105 ppm solutions containing different CM combinations. Both sensors were selective in particular to acetone, since the presence of acetone leads to a sheet resistance increase. We demonstrate that an advanced e-nose approach integrated with MLG electrodes has significant potential as a design concept for utilization of molecular detection at variable concentrations such as in early-stage disease diagnosis. This early-stage approach will provide convenient and reusable complex monitoring of CMs compared to typical contact sensors which require target analysis and are limited by disposable measuring. Moreover, further integration of the Internet of Things will introduce advanced e-nose devices as a biotechnological innovation for disease resilience with the potential for commercialization.

Multi-layer graphene as a selective detector for future lung cancer biosensing platforms