Personalized monitoring of disease biomarkers is of great interest in women’s health. However, existing approaches typically involve invasive inspection or bulky equipment, making them challenging to implement at home. Hence, we present a general strategy for label-free and specific detection of disease biomarkers in physiological media using an aptamer-based biosensor. The biosensor is a graphene field-effect transistor that involves immobilizing the aptamer and a biomolecule-permeable polyethylene glycol (PEG) layer on the graphene surface. The aptamer is capable of specifically binding with the target biomarker, thus inducing a change in the sensing responses. The PEG layer can effectively reduce the nonspecific adsorption of nontarget molecules in the solution, and increase the effective Debye screening length in the region directly adjacent to the graphene. In this work, studies of a biosensor with modification of the aptamer and PEG show that cervical carcinoma biomarkers such as tumor necrosis factor-α and interleukin 6 can be sensitively and specifically detected in undiluted physiological media, with detection limits as low as 0.13 pM for TNF-a and 0.20 pM for IL-6. This work presents a significant method for the general application of the biosensor for disease diagnosis in women’s health.
Early diagnosis of diseases would significantly increase the survival rate of cancer patients. However, current screening methods are complex and costly, making them unsuitable for rapid health diagnosis in daily life. Here, we develop a portable platform based on a planar-gate graphene field-effect transistor functionalized with polydopamine self-assembled film (PDA-GFET), capable of identifying colon cancer through the detection of EpCAM protein, which is expressed on colon cancer-derived exosomes, in clinical samples within 10 min. The PDA self-assembled film on the graphene and gate surface enhances the biosensor’s functionalization area while suppressing non-specific adsorption, thereby achieving detection limits as low as 112 particles/mL. In addition, the PDA-GFET-based detection platform was used to identify EpCAM protein in real clinical samples from healthy individuals and colon cancer patients within 10 min, and the two showed significant differences (p < 0.001). Results indicate that the proposed PDA-GFET-based detection platform is expected to be a potential tool for the early diagnosis of colon cancer.
This paper presents the optimization of surface modification for aptameric graphene nanosensors for the measurement of biomarkers in undiluted physiological media. In these sensors, graphene transduces the binding between an aptamer and the intended target biomarker into a measurable signal while being coated with a polyethylene glycol (PEG) nanolayer to minimize nonspecific adsorption of matrix molecules. We perform a systematic study of the aptamer and PEG attachment schemes and parameters, including the impact of the serial or parallel PEG–aptamer attachment scheme, PEG molecular weight and surface density, and aptamer surface density on the sensor behavior, such as the responsivity to biomarker concentration changes, and importantly, they are used for operation in physiological media and have the ability to reject nonspecific binding to interfering molecules. We then use the understanding from this parametric study to identify graphene nanosensor designs that are optimally functionalized with PEG and aptamers to be strongly responsive to target biomarkers and effectively reduce nonspecific adsorption of interferents, thereby enabling sensitive and specific biomarker measurements in undiluted physiological media. The experimental results show that nanosensors that were optimized via serial modification with 5000 Da PEG at 15 mM and a 94 nt DNA aptamer at 500 nM allowed specific measurement of C-reactive protein (CRP) in undiluted human serum with a limit of detection (LOD) down to 27 pM, representing an up to 1000-fold improvement compared to previously reported CRP measurements.
12 May 2026 in Mechanical Systems and Signal Processing