Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
Non-invasive monitoring of biomarkers is crucial for the wide adoption of health monitoring and the early detection of health conditions. L-arginine is a conditionally essential amino acid with a variety of significant physiological roles, such as its important role in general physiological homeostasis and the pathogenesis of various conditions like cardiovascular diseases (CVD) and neurodegenerative disorders. However, current methods of detection of L-arginine are unsuitable for continuous monitoring due to their heavy requirements on resources and invasive sampling. Here, we describe the fabrication of an L-arginine-specific electrochemical sensor by integrating a molecularly imprinted polymer (MIP) on a poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) modified laser-induced graphene (LIG) electrode on a flexible substrate. The MIPs function as sensitive and selective synthetic receptors for L-arginine, while the PEDOT:PSS electrodeposited LIG electrode provides low impedance and a high sensitivity detection. The MIP-PEDOT:PSS-LIG platform uses electrochemical impedance spectroscopy (EIS) and demonstrates exceptional analytical performance, achieving a low limit of detection (LOD) (~ 1 nM) and a wide linear dynamic range (1 nM to 1 mM), effectively covering the physiologically relevant concentrations of L-arginine in sweat. The sensor exhibited high selectivity against structurally similar amino acids (lysine, histidine, and citrulline) and maintained robust linearity (R2 ≈ 0.98) when tested in an artificial sweat medium. Furthermore, the device exhibited excellent reusability and stability via controlled electrostatic regeneration, demonstrating robustness and applicability within artificial sweat. In summary, a sensitive and selective MIP is developed, which enables non-invasive sensing of L-arginine with readily made flexible electrodes and provides a promising device for next-generation point-of-care diagnostics and health monitoring.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Comments on this article