Electrochemical Micro-Sensors for Real-Time Detection of Biomarkers

Electrochemical biosensing micro-devices are translational and mobile analytical micro-systems that enable rapid and label-free analyses of redox biomarkers, bringing benchtop medical diagnostic methods to the point-of-care. However, the selectivity of these electrochemical biosensors towards the biomarkers-of-interest dramatically decreases in the presence of biofluids due to other redox molecules generating masking electrochemical signals, requiring pretreatment steps to filter the interfering molecules and limiting the biosensor’s real-time analysis capabilities. Thus, engineering new bioelectronic interfaces and analytical approaches that will improve the signal-to-noise ratio of the electrochemical signals generated by the biomarkers and can be easily integrated in electrochemical biosensors, will make a significant contribution to real-time measurement of various redox biomarkers in the body in health and disease, and will find utility in a wide range of biomedical applications, from in vivo diagnostics to in situ screening of drugs.

This paper reports a new biosensing approach that lies in the use of an array of microelectrodes that are modified by coating with bioelectronic films and generating a set of complex electrochemical signals that is analyzed using intelligent machine learning algorithms. Compared to the state of the art, our approach will allow, for the first time, analyzing multiple redox biomarkers in biofluids without any pretreatment steps. We utilize the beneficial use of the electrodeposition method to modify electrodes with bioelectronic nano-films (e.g., biopolymer chitosan and reduced graphene oxide) at a high spatiotemporal resolution that enables integrating unique functionalities onto a microfabricated array of electrochemical sensors. The modified array controls the electrochemical signal characteristics based on the diffusion and electron transfer rate coefficients of redox molecules. We use the film-modified multi-electrode arrays to rapidly probe redox biomarkers in biofluids without pretreatment steps in three modes of detection: (1) direct detection of a specific biomarker, (2) indirect detection by influencing masking signals from other redox molecules in the biofluid that interfere with the biomarker’s electrochemical signal, and (3) simultaneous detection of multiple biomarkers using intelligent machine learning algorithms. We demonstrate the proof of concept detection in real-world scenarios: (1) amplification of the antipsychotic clozapine electrochemical signal using a reduced graphene oxide film that enables in situ detection in microliters of whole blood and will provide better schizophrenia treatment outcomes, (2) shifting the interfering signal generated by uric acid using carbon nanotubes encapsulated in a chitosan film improves the in situ detection of the neurotransmitter dopamine, and (3) utilizing an array of microelectrodes modified with various selectivities (chitosan and chitosan-carbon nanotubes matrix) to different neurotransmitters and analyzing the set of signals generated from profiles of neurotransmitters using a machine learning algorithm (partial least square regression) enables their simultaneous differentiation in urine.