journal article·2025-07-08

Electrophysiological Sensing with Graphene Electronic Tattoos for Saline and Underwater Environments

Ning Liu, Wendy Rossi, Dmitry Kireev, Deji Akinwande

ACS Applied Electronic Materials· Volume 7· Issue 13
Publisher page

Abstract

Hazardous and unprotected underwater operations are the leading causes of diver fatalities; biosensors can offer early warning and partial protection, thereby mitigating this risk to some extent. However, the complexity and high cost of current biosensors limit their widespread adoption, emphasizing the need for effective, cost-efficient, and multifunctional physiological monitoring solutions. In this study we developed a noninvasive water-resistant graphene electrophysiological sensor capable of detecting multiple physiological signals in a saline environment, addressing the limitations of existing underwater monitoring technologies. By employing an atomically thin graphene interface and a simple coating method for waterproofing, we achieved the collection of electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) signals on the skin surface. Additionally, we demonstrate the accurate assessment of startle events via EDA signals, which could be critical for assessing diver stress and preventing accidents. The water-resistant graphene biosensor exhibited outstanding electrical properties, with impedance as low as ∼2.4 kΩ·cm 2 at 10 kHz, an EMG signal-to-noise ratio of above 21 dB, and an EDA signal response speed of up to 0.125 μS/(s·cm 2 ), all tested in saline conditions. This study provides compelling evidence for the significant potential of water-resistant graphene electrophysiological sensors in saline environments, offering a solution to enhance diver safety and reduce accident risks.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Gas Sensing Nanomaterials and Sensors · Advanced Chemical Sensor Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Electrical and Computer Engineering

The University of Texas at Austin

Department of Biomedical Engineering

University of Massachusetts Amherst

Funding

Cockrell School of Engineering, University of Texas at Austin

References (44)

Show source-provided references
1. 10.1242/jeb.06958310.1242/jeb.069583 ↗
2. Germonpré P. · Int. SportMed. J. · 2006 · 7 · 1
3. Buzzacott P. · Diving Hyperb. Med. · 2009 · 39 · 205
4. 10.1016/j.bbe.2024.01.00410.1016/j.bbe.2024.01.004 ↗
5. 10.28920/dhm50.1.54-6510.28920/dhm50.1.54-65 ↗
6. 10.1111/j.1475-097X.2011.01070.x10.1111/j.1475-097X.2011.01070.x ↗
7. 10.1183/09031936.93.0301010110.1183/09031936.93.03010101 ↗
8. 10.1109/ACCESS.2019.289474010.1109/ACCESS.2019.2894740 ↗
9. 10.28920/dhm53.3.268-28010.28920/dhm53.3.268-280 ↗
10. Walker C. R. · 2017 · 10163 · 11
11. Sayer M. · SPUMS J. · 2005 · 35 · 117
12. Strauss M. B. · 2004
13. 10.1088/2058-8585/abad8910.1088/2058-8585/abad89 ↗
14. 10.1109/TBME.2014.230929310.1109/TBME.2014.2309293 ↗
15. 10.1002/adfm.20210722610.1002/adfm.202107226 ↗
16. 10.1038/s41528-023-00244-510.1038/s41528-023-00244-5 ↗
17. 10.1021/acsami.9b2340010.1021/acsami.9b23400 ↗
18. 10.1002/admt.20210143510.1002/admt.202101435 ↗
19. 10.1002/adma.20230563010.1002/adma.202305630 ↗
20. 10.1038/s41467-022-34406-210.1038/s41467-022-34406-2 ↗
21. 10.1126/science.110289610.1126/science.1102896 ↗
22. 10.1088/2053-1583/ab4c0f10.1088/2053-1583/ab4c0f ↗
23. Mohapatra S. · 2018
24. 10.1038/s41565-018-0163-610.1038/s41565-018-0163-6 ↗
25. 10.1002/adma.20221219010.1002/adma.202212190 ↗
26. 10.1038/s41563-018-0249-410.1038/s41563-018-0249-4 ↗
27. 10.1016/j.cej.2022.13663110.1016/j.cej.2022.136631 ↗
28. 10.1038/s41596-020-00489-810.1038/s41596-020-00489-8 ↗
29. 10.1021/acsnano.7b0218210.1021/acsnano.7b02182 ↗
30. 10.1038/s41565-022-01145-w10.1038/s41565-022-01145-w ↗
31. 10.1038/s41699-022-00324-610.1038/s41699-022-00324-6 ↗
32. Lüderitz B. · Hellenic J. Cardiol. · 2009 · 50 · 3
33. Hsu, C. H.; Wu, S. H. Robust Signal Synthesis of the 12-Lead ECG Using 3-Lead Wireless ECG Systems. In 2014 IEEE International Conference on Communications (ICC); IEEE: 2014; pp 3517–3522.10.1109/ICC.2014.6883866 ↗
34. Garagiola U. · Diagnostic tools. · 2015
35. 10.1016/S1350-4533(99)00054-510.1016/S1350-4533(99)00054-5 ↗
36. 10.1007/s11517-007-0168-z10.1007/s11517-007-0168-z ↗
37. 10.1152/japplphysiol.90984.200810.1152/japplphysiol.90984.2008 ↗
38. 10.1061/(ASCE)ME.1943-5479.000037310.1061/(ASCE)ME.1943-5479.0000373 ↗
39. 10.3389/fphys.2021.76738610.3389/fphys.2021.767386 ↗
40. Posada-Quintero, H. F.; Derrick, B. J.; Winstead-Derlega, C.; Gonzalez, S. I.; Ellis, M. C.; Freiberger, J. J.; Chon, K. H. Time-Varying Spectral Index of Electrodermal Activity to Predict Central Nervous System Oxygen Toxicity Symptoms in Divers: Preliminary Results. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); IEEE: 2021; pp 1242–1245.10.1109/EMBC46164.2021.9629924 ↗
41. 10.1007/s10286-015-0282-110.1007/s10286-015-0282-1 ↗
42. 10.1111/j.1469-8986.1994.tb01040.x10.1111/j.1469-8986.1994.tb01040.x ↗
43. 10.1093/occmed/kqm00510.1093/occmed/kqm005 ↗
44. 10.1038/s41699-018-0064-410.1038/s41699-018-0064-4 ↗