Machine learning toward high-performance electrochemical sensors.

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.

In Situ Nanocoating on Porous Pyrolyzed Paper Enables Antibiofouling and Sensitive Electrochemical Analyses in Biological Fluids.

Electrochemical detection in complex biofluids is a long-standing challenge as electrode biofouling hampers its sensing performance and commercial translation. To overcome this drawback, pyrolyzed paper as porous electrode coupled with the drop casting of an off-the-shelf polysorbate, that is, Tween 20 (T20), is described here by taking advantage of the in situ formation of a hydrophilic nanocoating (2 nm layer of T20). The latter prevents biofouling while providing the capillarity of samples through paper pores, leveraging redox reactions across both only partially fouled and fresh electrodic surfaces with increasing detection areas. The nanometric thickness of this blocking layer is also essential by not significantly impairing the electron-transfer kinetics. These phenomena behave synergistically to enhance the sensibility that further increases over long-term exposures (4 h) in biological fluids. While the state-of-the-art antibiofouling strategies compromise the sensibility, this approach leads to peak currents that are up to 12.5-fold higher than the original currents after 1 h exposure to unprocessed human plasma. Label-free impedimetric immunoassays through modular bioconjugation by directly anchoring spike protein on gold nanoparticles are also allowed, as demonstrated for the COVID-19 screening of patient sera. The scalability and simplicity of the platform combined with its unique ability to operate in biofluids with enhanced sensibility provide the generation of promising biosensing technologies toward real-world applications in point-of-care diagnostics, mass testing, and in-home monitoring of chronic diseases.

Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests.

Limitations of the recognition elements in terms of synthesis, cost, availability, and stability have impaired the translation of biosensors into practical use. Inspired by nature to mimic the molecular recognition of the anti-SARS-CoV-2 S protein antibody (AbS) by the S protein binding site, we synthesized the peptide sequence of Asn-Asn-Ala-Thr-Asn-COOH (abbreviated as PEP2003) to create COVID-19 screening label-free (LF) biosensors based on a carbon electrode, gold nanoparticles (AuNPs), and electrochemical impedance spectroscopy. The PEP2003 is easily obtained by chemical synthesis, and it can be adsorbed on electrodes while maintaining its ability for AbS recognition, further leading to a sensitivity 3.4-fold higher than the full-length S protein, which is in agreement with the increase in the target-to-receptor size ratio. Peptide-loaded LF devices based on noncovalent immobilization were developed by affording fast and simple analyses, along with a modular functionalization. From studies by molecular docking, the peptide-AbS binding was found to be driven by hydrogen bonds and hydrophobic interactions. Moreover, the peptide is not amenable to denaturation, thus addressing the trade-off between scalability, cost, and robustness. The biosensor preserves 95.1% of the initial signal for 20 days when stored dry at 4 °C. With the aid of two simple equations fitted by machine learning (ML), the method was able to make the COVID-19 screening of 39 biological samples into healthy and infected groups with 100.0% accuracy. By taking advantage of peptide-related merits combined with advances in surface chemistry and ML-aided accuracy, this platform is promising to bring COVID-19 biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point of care, with social and economic impacts being achieved.