This work presents a novel approach for tailoring molecularly imprinted polymers (MIPs) with a preliminary stage of atom transfer radical polymerization (ATRP), for a more precise definition of the imprinted cavity. A well-defined copolymer of acrylamide and N,N′-methylenebisacrylamide (PAAm-co-PMBAm) was synthesized by ATRP and applied to gold electrodes with the template, followed by a crosslinking reaction. The template was removed from the polymer matrix by enzymatic/chemical action. The surface modifications were monitored via electrochemical impedance spectroscopy (EIS), having the MIP polymer as a non-conducting film designed with affinity sites for CA15-3. The resulting biosensor exhibited a linear response to CA15-3 log concentrations from 0.001 to 100 U/mL in PBS or in diluted fetal bovine serum (1000×) in PBS. Compared to the polyacrylamide (PAAm) MIP from conventional free-radical polymerization, the ATRP-based MIP extended the biosensor’s dynamic linear range 10-fold, improving low concentration detection, and enhanced the signal reproducibility across units. The biosensor demonstrated good sensitivity and selectivity. Overall, the work described confirmed that the process of radical polymerization to build an MIP material influences the detection capacity for the target substance and the reproducibility among different biosensor units. Extending this approach to other cancer biomarkers, the methodology presented could open doors to a new generation of MIP-based biosensors for point-of-care disease diagnosis.

The detection of glucose is crucial for diagnosing diseases such as diabetes and enables timely medical intervention. In this study, a disposable enzymatic screen-printed electrode electrochemical biosensor enhanced with machine learning (ML) for quantifying glucose in serum is presented. The platinum working surface was modified by chemical adsorption with biographene (BGr) and glucose oxidase, and the enzyme was encapsulated in polydopamine (PDP) by electropolymerisation. Electrochemical characterisation and morphological analysis (scanning and transmission electron microscopy) confirmed the modifications. Calibration curves in Cormay serum (CS) and selectivity tests with chronoamperometry were used to evaluate the biosensor’s performance. Non-linear ML regression algorithms for modelling glucose concentration and calibration parameters were tested to find the best-fit model for accurate predictions. The biosensor with BGr and enzyme encapsulation showed excellent performance with a linear range of 0.75–40 mM, a correlation of 0.988, and a detection limit of 0.078 mM. Of the algorithms tested, the decision tree accurately predicted calibration parameters and achieved a coefficient of determination above 0.9 for most metrics. Multilayer perceptron models effectively predicted glucose concentration with a coefficient of determination of 0.828, demonstrating the synergy of biosensor technology and ML for reliable glucose detection.

The assessment of fish welfare is crucial to prevent economic losses in aquaculture and ensure reliable results in research. A quick, non-invasive device to measure cortisol levels in fish farm water facilitates welfare evaluation and corrective actions when compromised. To address this need, an innovative sensor was developed using screen-printed carbon electrodes (SPCEs) functionalized with reduced graphene oxide/Prussian blue nanocubes (rGO/PBNCs) for direct selective detection of cortisol. A molecularly imprinted polymer (MIP) was synthesized on rGO/PBNCs/SPCEs by electropolymerization (ELP) of pyrrole in the presence of cortisol. The polymerization solution was prepared by adding cortisol (5 mM) and pyrrole (0.3 M) to a DMF/PBS (1:4) solution (pH 7.4). Following ELP, the electrodes were washed with PBS, and pyrrole overoxidation was used to extract cortisol from the polymer matrix. For comparison purposes, a non-imprinted polymer (NIP) was also fabricated. The electrodes were characterized using scanning electron microscopy (SEM) and Raman spectroscopy to assess their morphological and chemical features. Under optimized conditions, the sensor showed a linear range from 0.1 nM to 0.1 mM in artificial saltwater. This sensor combines simplicity and affordability while providing reliable detection of chemical and biological compounds.

Alzheimer's disease (AD) is the leading cause of dementia, and early identification of molecular biomarkers in blood offers a promising avenue for diagnosis and monitoring treatment. Tau-441 stands out as a particularly promising biomarker among the potential molecular targets. This research describes the development of a highly sensitive and cost-effective biomimetic sensor, capable of selectively detecting Tau-441 at femtomolar concentrations. This is achieved through the synergistic combination of gold nanoparticles (AuNPs) with molecularly imprinted polymer (MIP) technology. The MIP layer was sensitised by electropolymerising phenylenediamine (o-PDA) in the presence of Tau-441 and AuNPs onto a gold screen-printed electrode (Au-SPE) using cyclic voltammetry (CV). After polymerisation, the entrapped proteins were removed by proteolytic digestion, generating well-defined imprinted cavities within the polymer matrix. Scanning electron microscopy (SEM) and Raman analysis were conducted to monitor the surface modification of the Au-SPE working electrode. The device displayed linear responses to Tau-441 protein within the range 2.0 pg mL^-1 to 200 ng mL^-1, with a limit of detection of 1.51 fg mL^-1. The analytical performance of the device was validated in complex matrices, including Cormay serum and cell media from primary cultures of hippocampal neurons, using a competitive assay. The platform showed high sensitivity, good reproducibility, and reliable performance in biologically relevant media, demonstrating strong robustness. Its excellent analytical characteristics, together with the potential for integration into portable electrochemical devices, make this sensor a promising tool for rapid and accurate point-of-care testing, enhancing the detection and monitoring of AD.