RESUMO
Sweat lactate, a promising biomarker for assessing physical performance and health conditions, calls for noninvasive, convenient, and affordable detection methods. This study leverages molecularly imprinted polymers (MIPs) as a synthetic biorecognition element for lactate detection due to their affordability and high stability. Traditional MIPs-based electrochemical sensors often require external redox probes such as ferricyanide/ferrocyanide in the solution to signal the binding between analytes and MIPs, which restricts their applicability. To address this, our study introduces an innovative approach utilizing a layer of Prussian blue (PB) nanoparticles as the internal redox probe on screen-printed carbon electrodes (SPCE), followed by a layer of electropolymerized MIP (eMIP) for molecular recognition, enabling reagent-free lactate detection. The real-time growth of eMIP and the processes of template elution and lactate rebinding were examined and validated using electrochemical surface plasmon resonance (EC-SPR) spectroscopy. The sensor's performance was thoroughly investigated using Differential Pulsed Voltammetry (DPV) and Electrochemical Impedance Spectroscopy (EIS) with samples spiked in 0.1 M KCl solution and artificial sweat. The developed sensors demonstrated a fast and selective response to lactate, detecting concentrations from 1 to 35 mM with a Limit of Detection (LOD) of 0.20 mM, defined by a signal-to-noise ratio of 3 in the DPV measurements. They also exhibited excellent reproducibility, reusability, and a shelf life of up to 10 months under ambient conditions. These eMIP/PB/SPCE-based lactate sensors show considerable potential as point-of-care (POC) devices for sweat lactate detection, and the technology could be adapted for reagent-free detection of a broad spectrum of molecules.
RESUMO
Molecularly imprinted polymers (MIPs), often called "synthetic antibodies", are highly attractive as artificial receptors with tailored biomolecular recognition to construct biosensors. Electropolymerization is a fast and facile method to directly synthesize MIP sensing elements in situ on the working electrode, enabling ultra-low-cost and easy-to-manufacture electrochemical biosensors. However, due to the high dimensional design space of electropolymerized MIPs (e-MIPs), the development of e-MIPs is challenging and lengthy based on trial and error without proper guidelines. Leveraging machine learning techniques in building the quantitative relationship between synthesis parameters and corresponding sensing performance, e-MIPs' development and optimization can be facilitated. We herein demonstrate a case study on the synthesis of cortisol-imprinted polypyrrole for cortisol detection, where e-MIPs are fabricated with 72 sets of synthesis parameters with replicates. Their sensing performances are measured using a 12-channel potentiostat to construct the subsequent data-driven framework. The Gaussian process (GP) is employed as the mainstay of the integrated framework, which can account for various uncertainties in the synthesis and measurements. The Sobol index-based global sensitivity is then performed upon the GP surrogate model to elucidate the impact of e-MIPs' synthesis parameters on sensing performance and interrelations among parameters. Based on the prediction of the established GP model and local sensitivity analysis, synthesis parameters are optimized and validated by experiment, which leads to remarkable sensing performance enhancement (1.5-fold increase in sensitivity). The proposed framework is novel in biosensor development, which is expandable and also generally applicable to the development of other sensing materials.
