Nonenzymatic Sweat Wearable Uric Acid Sensor Based on N-Doped Reduced Graphene Oxide/Au Dual Aerogels.
Yao ChenGuanglei LiWenjing MuXinhao WanDanfeng LuJie GaoDan WenPublished in: Analytical chemistry (2023)
Sweat wearable sensors enable noninvasive and real-time metabolite monitoring in human health management but lack accuracy and wearable applicability. The rational design of sensing electrode materials will be critical yet challenging. Herein, we report a dual aerogel-based nonenzymatic wearable sensor for the sensitive and selective detection of uric acid (UA) in human sweat. The three-dimensional porous dual-structural aerogels composed of Au nanowires and N-doped graphene nanosheets (noted as N-rGO/Au DAs) provide a large active surface, abundant access to the target, rapid electron transfer pathways, and a high intrinsic activity. Thus, a direct UA electro-oxidation is demonstrated at the N-rGO/Au DAs with a much higher activity than those at the individual gels (i.e., Au and N-rGO). Moreover, the resulting sensing chip displays high performance with a good anti-interfering ability, long-term stability, and excellent flexibility toward the UA detection. With the assistance of a wireless circuit, a wearable sensor is successfully applied in the real-time UA monitoring on human skin. The obtained result is comparable to that evaluated by high-performance liquid chromatography. This dual aerogel-based nonenzymatic biosensing platform not only holds considerable promise for the reliable sweat metabolite monitoring but also opens an avenue for metal-based aerogels as flexible electrodes in wearable sensing.
Keyphrases
- reduced graphene oxide
- uric acid
- gold nanoparticles
- heart rate
- metabolic syndrome
- high performance liquid chromatography
- human health
- electron transfer
- loop mediated isothermal amplification
- quantum dots
- risk assessment
- endothelial cells
- label free
- highly efficient
- mass spectrometry
- climate change
- nitric oxide
- rheumatoid arthritis
- simultaneous determination
- machine learning
- room temperature
- solid phase extraction
- big data