Large-scale graphene films are available, which enables the integration of graphene field-effect transistor (G-FET) arrays on chips. However, the transfer characteristics are not identical but diverse over the array. Optical microscopy is widely used to inspect G-FETs, but quantitative evaluation of the optical images is challenging as they are not classified. Here, we implemented a deep-learning-based semantic image segmentation algorithm. Through a neural network, every pixel was assigned to graphene, electrode, substrate, or contaminants, with exceeding a success rate of 80%. We also found that the drain current and transconductance correlated with the coverage of graphene films.

The interfacial adhesion energy between graphene and underlying substrates is considerably important for robust graphene biosensors because water molecules can intercalate underneath graphene when submerged, possibly detaching graphene from substrates. This study investigated the robustness of graphene field-effect transistor arrays fabricated on hydrophobic and hydrophilic SiO2 substrates. Although the graphene sheets delaminated from hydrophilic substrates within minutes of submersion in a buffer solution, they remained stable on hydrophobic substrates for several days. This result agreed with the estimated thermodynamic work of adhesion in water, which improved significantly from -17.3 to 17.7 mJ/m2 through the hydrophobization process of the substrates.

In field-effect transistor (FET) biosensors, charge screening in electrolyte solutions limits sensitivity, thereby restricting the applicability of FET sensors. This is particularly pronounced in graphene FET (GFET) biosensors, where the bare graphene surface possesses a strongly negative charge, which impedes the high sensitivity of GFETs owing to nonlinear electrolytic screening at the interfaces between graphene and liquid. In this study, we counteracted the negative surface charge of graphene by decorating positively charged compounds and demonstrated the sensing of C-reactive protein (CRP) with surface-charge-modulated GFETs (SCM-GFETs). We integrated multiple SCM-GFETs with anti-CRP antibodies and non-functionalized GFETs into a chip, and measured differentials to eliminate background changes to improve measurement reliability. The FET response corresponded to the fluorescence images, which visualized the specific adsorption of CRP. The estimated dissociation constant was consistent with previously reported values; this supports the conclusion that the results are attributed to specific adsorption. Conversely, the signal in GFETs without decoration was obscured by noise because of nonlinear electrolytic screening, further emphasizing the significance of surface-charge modulation. The limit of detection (LOD) of the system was determined to be 2.9 nM. This value has the potential to be improved through further optimization of the surface charges to align with specific applications. Our devices effectively circumvent nonlinear electrolytic screening, opening the door for further advancements in GFET biosensor technology.

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