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Bioelectronics and Its Limitless Possibilities

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A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "B：Biology and Biomedicine".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 9419

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Special Issue Editor

Dr. Tianyu Yang

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Guest Editor

School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287, USA
Interests: wearable and implantable bioelectronics; biosensors and diagnostics; thermal-fluid actuators; heat transport in living tissue; thermal management of electronics

Special Issue Information

Dear Colleagues,

Bioelectronics has been developed for numerous applications, including wearable and implantable biomedical devices for biomedicine and healthcare. Through the design and implementation of advanced devices and materials that can interface directly with biological tissues, bioelectronics is pushing beyond the boundaries of traditional medicine and biotechnology. The possibilities for future bioelectronics are limitless. From wireless sensors that continuously monitor and manage internal and external diseases, to neural interfaces that restore lost sensory functions or augment human cognition, bioelectronics holds the promise of more personalized, precise, and proactive healthcare. As the technology advances—propelled by breakthroughs in nanotechnology, AI-driven data analysis, wireless communication, and new biocompatible materials—these innovations will not only unlock new research topics but also significantly advance interdisciplinary collaboration between engineers and scientists from different areas. Accordingly, this Special Issue seeks to showcase research papers, short communications, and review articles that focus on innovative developments in bioelectronics and their limitless possibilities for various applications. Contributions may address innovative theoretical models, novel device architectures, and enhanced integration strategies related to bioelectronics.

Dr. Tianyu Yang
Guest Editor

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Keywords

bio-integrated electronics
biomaterials
electrochemistry
implantable and wearable devices
diagnostics
human–machine interface

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Published Papers (5 papers)

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Research

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29 pages, 5273 KB

Open AccessArticle

Intersession Robust Hybrid Brain–Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism

by Sefa Aydın, Mesut Melek and Levent Gökrem

Micromachines 2025, 16(11), 1264; https://doi.org/10.3390/mi16111264 - 8 Nov 2025

Cited by 1 | Viewed by 1381

Abstract

This study introduces a hybrid Brain–Computer (BCI) system with a robust and secure activation mechanism between sessions, aiming to minimize the negative effects of visual stimulus-based BCI systems on user eye health. The system is based on the integration of Electroencephalography (EEG) signals and Electrooculography (EOG) artefacts, and includes an LED stimulus operating at a frequency of 7 Hz for safe activation and objects moving in different directions. While the LED functions as an activation switch that reduces visual fatigue caused by traditional visual stimuli, moving objects provide command generation depending on the user’s intention. In order to evaluate the stability of the system against physiological and psychological conditions, data were collected from 15 participants in two different sessions. The Correlation Alignment (CORAL) method was applied to the data to reduce the variance between sessions and to increase stability. A Bootstrap Aggregating algorithm was used in the classification processes, and with the CORAL method, the system accuracy rate was increased from 81.54% to 94.29%. Compared to similar BCI approaches, the proposed system offers a safe activation mechanism that effectively adapts to users’ changing cognitive states throughout the day by reducing visual fatigue, despite using a low number of EEG channels, and demonstrates its practicality and effectiveness by performing on par or superior to other systems in terms of high accuracy and robust stability. Full article

(This article belongs to the Special Issue Bioelectronics and Its Limitless Possibilities)

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11 pages, 1576 KB

Open AccessArticle

Proof-of-Concept Development of a Bioelectric Biosensor Using Arduino for Monitoring Dopaminergic Response in Neuroblastoma Cells

by Magdalene Pappa and Spyridon Kintzios

Micromachines 2025, 16(8), 951; https://doi.org/10.3390/mi16080951 - 19 Aug 2025

Cited by 1 | Viewed by 1712

Abstract

This study presents the proof-of-concept design and preliminary implementation of a bioelectric biosensor based on an Arduino platform for real-time monitoring of gel-immobilized N2a neuroblastoma cells using dopamine as a model neurotransmitter. The sensor operates on the principle of bioelectric recognition assay (BERA), and uses a two-electrode set-up as a simple, cost-efficient way to capture electrophysiological responses following dopamine exposure, while at the same time mimicking the in vivo cellular environment. Cellular ohmic resistance was assessed under increasing dopamine concentrations and temperatures (24 °C and 37 °C). The results showed that temperature significantly affected cell responses to increasing dopamine concentrations, possibly because of differences in dopamine diffusion in gel, which may in turn have affected membrane polarization and overall cell electric resistance. Pending further testing against a wider range of dopamine concentrations along with various dopamine agonists/antagonists, as well as optimization in terms of specificity, selectivity, and sensitivity, the biosensor could be applied in bioscreening and neuropharmacological studies in a user-friendly, scalable way. Full article

(This article belongs to the Special Issue Bioelectronics and Its Limitless Possibilities)

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17 pages, 5309 KB

Open AccessArticle

Application of Carbon Nanotube-Based Elastomeric Matrix for Capacitive Sensing in Diabetic Foot Orthotics

by Monisha Elumalai, Andre Childs, Samantha Williams, Gabriel Arguello, Emily Martinez, Alaina Easterling, Dawn San Luis, Swaminathan Rajaraman and Charles M. Didier

Micromachines 2025, 16(7), 804; https://doi.org/10.3390/mi16070804 - 11 Jul 2025

Cited by 2 | Viewed by 1529

Abstract

Diabetic foot ulcers (DFUs) represent a critical global health issue, necessitating the development of advanced smart, flexible, and wearable sensors for continuous monitoring that are reimbursable within foot orthotics. This study presents the design and characterization of a pressure sensor implemented into a shoe insole to monitor diabetic wound pressures, emphasizing the need for a high sensitivity, durability under cyclic mechanical loading, and a rapid response time. This investigation focuses on the electrical and mechanical properties of carbon nanotube (CNT) composites utilizing Ecoflex and polydimethylsiloxane (PDMS). Morphological characterization was conducted using Transmission Electron Microscopy (TEM), Laser Confocal Microscopy, and Scanning Electron Microscopy (SEM). The electrical and mechanical properties of the CNT/Ecoflex- and the CNT/PDMS-based sensor composites were then investigated. CNT/Ecoflex was then further evaluated due to its lower variability performance between cycles at the same pressure, as well as its consistently higher capacitance values across all trials in comparison to CNT/PDMS. The CNT/Ecoflex composite sensor showed a high sensitivity (2.38 to 3.40 kPa⁻¹) over a pressure sensing range of 0 to 68.95 kPa. The sensor’s stability was further assessed under applied pressures simulating human weight. A custom insole prototype, incorporating 12 CNT/Ecoflex elastomeric matrix-based sensors (as an example) distributed across the metatarsal heads, midfoot, and heel regions, was developed and characterized. Capacitance measurements, ranging from 0.25 pF to 60 pF, were obtained across N = 3 feasibility trials, demonstrating the sensor’s response to varying pressure conditions linked to different body weights. These results highlight the potential of this flexible insole prototype for precise and real-time plantar surface monitoring, offering an approachable avenue for a challenging diabetic orthotics application. Full article

(This article belongs to the Special Issue Bioelectronics and Its Limitless Possibilities)

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34 pages, 7670 KB

Open AccessArticle

A Safe and Efficient Brain–Computer Interface Using Moving Object Trajectories and LED-Controlled Activation

by Sefa Aydin, Mesut Melek and Levent Gökrem

Micromachines 2025, 16(3), 340; https://doi.org/10.3390/mi16030340 - 16 Mar 2025

Cited by 4 | Viewed by 3056

Abstract

Nowadays, brain–computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier. Full article

(This article belongs to the Special Issue Bioelectronics and Its Limitless Possibilities)

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Review

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30 pages, 10883 KB

Open AccessEditor’s ChoiceReview

MXene- and MOF-Based Hydrogels: Emerging Platforms for Electrochemical Biosensing and Health Monitoring

by Kandaswamy Theyagarajan, Sairaman Saikrithika and Young-Joon Kim

Micromachines 2026, 17(2), 267; https://doi.org/10.3390/mi17020267 - 20 Feb 2026

Cited by 2 | Viewed by 872

Abstract

Smart healthcare is rapidly emerging as a transformative paradigm, enabling simultaneous health monitoring, therapeutic intervention, and early prediction of disease onset. In this context, electrochemical monitoring systems have attracted growing interest due to their cost-effectiveness, ease of operation, miniaturization and compatibility with wearable platforms. Accordingly, conductive hydrogel-based electrochemical (bio)sensors have gained significant attention for health monitoring owing to their soft mechanical properties, high water content, excellent biocompatibility, and ability to form intimate, conformal interfaces with biological tissues. Their three-dimensional polymeric networks facilitate efficient ion transport and mechanical flexibility, making them particularly suitable for wearable and noninvasive sensing and monitoring applications. However, the intrinsically limited conductivity and catalytic activity of pristine hydrogels often constrain their electrochemical performance. To overcome these limitations, functional nanomaterials such as metal–organic frameworks (MOFs) and MXene (MX) nanosheets have been increasingly integrated into hydrogel matrices to enhance conductivity and electrochemical activity. This review provides a comprehensive and critical comparison of recent advances in MOF- and MX-integrated conductive hydrogels for electrochemical health monitoring. In addition to material design strategies and sensing performance, emerging trends in data-driven sensing aimed at improving signal interpretation and multi-analyte discrimination are systematically discussed. Key challenges related to long-term stability, biocompatibility, scalability, and intelligent system integration are critically assessed, and the future potential of these platforms within closed-loop architectures is highlighted, paving the way for next-generation conductive hydrogel-based electrochemical sensors in smart healthcare applications. Full article

(This article belongs to the Special Issue Bioelectronics and Its Limitless Possibilities)

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