Sergey V. Shleev Head of Laboratory Dr.Sci. (Chemistry), Professor INB, room 503
	(495) 660-34-30, ext. 339 s.shleev@fbras.ru

Keywords
bioelectronics, biosensors, biological power sources, biofuel cells, biosupercapacitors, bionanocomposites, bioresorbable materials, non-invasive biomedical analysis, optical, electrical, thermal, and electrochemical sensors.

The Bioelectronics Laboratory is an interdisciplinary research unit established in 2025 within the framework of the Russian Science Foundation megagrant program under the leadership of Prof. Sergey V. Shleev (D.Sc. in Chemistry). The laboratory conducts research at the interface of biochemistry, electrochemistry, materials science, biomedical analysis and technology, aiming to both understand fundamental biological processes and develop next-generation bioelectronic devices.

Research Focus
The main research areas include:

• Fundamental bioelectrochemical studies of proteins, enzymes, organelles, and cells, with particular emphasis on electron transfer mechanisms, catalytic activity of redox enzymes, and biochemical reaction kinetics.
• Development of advanced functional materials, including nanobiocomposites, biodegradable and implantable materials, and engineered biointerfaces with tailored electrochemical and catalytic properties.
• Design and implementation of next-generation bioelectronic devices, such as biosensors, biofuel cells, and biosupercapacitors for medical diagnostics, physiological monitoring, and autonomous energy supply.

Technological Scope
The laboratory performs a full-cycle development of biomedical technologies, ranging from individual bioelectronic components to integrated modular multisensor diagnostic systems for rapid and autonomous health monitoring.

Methods and Approaches
The research is based on a combination of advanced analytical and biophysical techniques, including:

• Electrochemical methods (amperometry, potentiometry, voltammetry, impedance spectroscopy)
• UV–Vis spectroscopy
• Surface and interface engineering
• Data-driven analysis and machine learning approaches

Achievements
An innovative non-invasive method for hemoglobin determination has been developed, combining integrated multiparametric biomedical monitoring with advanced data analysis techniques. Unlike conventional approaches that rely on a limited set of variables, this methodology leverages multisensor data fusion to substantially enhance the accuracy and robustness of hemoglobin estimation. At the core of this technology lies the use of machine learning algorithms, particularly neural networks, which enable the identification of complex, nonlinear relationships between physiological signals and hemoglobin concentration.

A unique software system architecture has been implemented, comprising a server, desktop, and mobile applications. The server (ASP.NET Web Forms) provides a REST API for data storage, user authentication, and communication with client applications. The desktop application (WPF) enables connection to medical devices via a COM port and supports processing and management of measurement results. The mobile application (.NET MAUI) allows users to view historical data, generate trend graphs, and synchronize data with the server.