Back

Miniaturized wireless bioelectronics for electrically driven biohybrid robots

Tetsuka, H.; Ma, J.; Hirano, M.

2026-03-12 bioengineering
10.64898/2026.03.09.710657 bioRxiv
Show abstract

Although biohybrid robots offer the potential for soft, adaptive actuation by harnessing living muscle, practical operation in cell culture environments is often limited by the requirement of immersed leads or cumbersome stimulation equipment. Here, we present a thin, miniaturized, wireless bioelectronic stimulator that can electrically drive biohybrid robots while maintaining stability in aqueous cell culture media. Built on a 50-{micro}m liquid crystal polymer (LCP) substrate, the device integrates a planar receiving coil, interconnects, a diode-based rectifier, and a tank capacitor. This enables the device to convert an approximately 4.9-MHz radio-frequency (RF) input into pulsed direct current (DC), which is delivered through integrated stimulation electrodes. The stimulator has a footprint of [~]23 mm2 and a total thickness and mass of [~]100 {micro}m and [~]7 mg, respectively. We integrated the stimulator with a nanopatterned carbon nanotube (CNT)/gelatin hydrogel fin seeded with human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to generate propulsion through fin flapping. By optimizing the thickness of the polydimethylsiloxane (PDMS) encapsulation layer, the density was tuned, and the robot remained freely floating and retained shape integrity during operation. This produced autonomous forward locomotion of [~]70 {micro}m/s. The stimulator generated distance-dependent output voltage pulses of [~]2-6 V and reliably synchronized fin flapping rates of up to 2 Hz without an observable loss of cell attachment or sarcomeric organization. Together, these results establish a compact, media-compatible, wireless, bioelectronic interface suitable for closed-system biohybrid robotics.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Nano Letters
63 papers in training set
Top 0.2%
10.0%
2
Advanced Functional Materials
41 papers in training set
Top 0.2%
10.0%
3
ACS Nano
99 papers in training set
Top 0.3%
8.4%
4
Advanced Materials Technologies
27 papers in training set
Top 0.1%
6.8%
5
Advanced Materials
53 papers in training set
Top 0.4%
6.3%
6
Advanced Science
249 papers in training set
Top 4%
4.8%
7
Lab on a Chip
88 papers in training set
Top 0.3%
4.8%
50% of probability mass above
8
Advanced Healthcare Materials
71 papers in training set
Top 0.7%
3.6%
9
Biofabrication
32 papers in training set
Top 0.2%
3.6%
10
Nature Communications
4913 papers in training set
Top 42%
3.1%
11
ACS Biomaterials Science & Engineering
37 papers in training set
Top 0.3%
2.6%
12
ACS Synthetic Biology
256 papers in training set
Top 1%
1.9%
13
Biomaterials
78 papers in training set
Top 0.4%
1.9%
14
Cell Reports Physical Science
18 papers in training set
Top 0.1%
1.8%
15
Nature Biomedical Engineering
42 papers in training set
Top 0.8%
1.7%
16
Scientific Reports
3102 papers in training set
Top 59%
1.7%
17
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 1%
1.7%
18
PLOS ONE
4510 papers in training set
Top 57%
1.5%
19
Small
70 papers in training set
Top 0.6%
1.5%
20
Science Advances
1098 papers in training set
Top 21%
1.3%
21
Advanced Biology
29 papers in training set
Top 0.5%
1.3%
22
Biosensors and Bioelectronics
52 papers in training set
Top 0.9%
1.3%
23
Nature Nanotechnology
30 papers in training set
Top 0.8%
1.2%
24
ACS Sensors
45 papers in training set
Top 1%
0.9%
25
Biomaterials Science
21 papers in training set
Top 0.6%
0.7%
26
eLife
5422 papers in training set
Top 60%
0.7%
27
ACS Applied Bio Materials
21 papers in training set
Top 1%
0.6%
28
Advanced Materials Interfaces
10 papers in training set
Top 0.4%
0.6%
29
Journal of Neural Engineering
197 papers in training set
Top 2%
0.6%
30
Materials Today Bio
18 papers in training set
Top 0.7%
0.6%