Superior Non-Invasive Glucose Sensor Using Bimetallic CuNi Nanospecies Coated Mesoporous Carbon.
Ahmed Bahgat RadwanSreedevi ParamparambathJohn-John CabibihanAbdulaziz Khalid Al-AliPeter KasakRana Abdul ShakoorRayaz A MalikSaid A MansourKishor Kumar SadasivuniPublished in: Biosensors (2021)
The assessment of blood glucose levels is necessary for the diagnosis and management of diabetes. The accurate quantification of serum or plasma glucose relies on enzymatic and nonenzymatic methods utilizing electrochemical biosensors. Current research efforts are focused on enhancing the non-invasive detection of glucose in sweat with accuracy, high sensitivity, and stability. In this work, nanostructured mesoporous carbon coupled with glucose oxidase (GOx) increased the direct electron transfer to the electrode surface. A mixed alloy of CuNi nanoparticle-coated mesoporous carbon (CuNi-MC) was synthesized using a hydrothermal process followed by annealing at 700 °C under the flow of argon gas. The prepared catalyst's crystal structure and morphology were explored using X-ray diffraction and high-resolution transmission electron microscopy. The electrocatalytic activity of the as-prepared catalyst was investigated using cyclic voltammetry (CV) and amperometry. The findings show an excellent response time of 4 s and linear range detection from 0.005 to 0.45 mM with a high electrode sensitivity of 11.7 ± 0.061 mA mM cm-2 in a selective medium.
Keyphrases
- blood glucose
- metal organic framework
- high resolution
- electron microscopy
- glycemic control
- crystal structure
- highly efficient
- electron transfer
- label free
- room temperature
- ionic liquid
- reduced graphene oxide
- type diabetes
- blood pressure
- loop mediated isothermal amplification
- gold nanoparticles
- cardiovascular disease
- carbon dioxide
- magnetic resonance
- risk assessment
- magnetic resonance imaging
- carbon nanotubes
- real time pcr
- metabolic syndrome
- computed tomography
- quality improvement
- hydrogen peroxide
- heavy metals
- nitric oxide
- high speed
- sewage sludge
- neural network
- solid state
- clinical evaluation
- quantum dots