journal article·2021-08-27Open access

MXene Nanosheet-Based Microneedles for Monitoring Muscle Contraction and Electrostimulation Treatment

Yen-Chang Yang, Yen-Tzu Lin, Jiashing Yu, Huan-Tsung Chang, Ting-Yu Lu, Tzu-Yen Huang, Anant Preet, Ya-Ju Hsu, Ligang Wang, Tzu-En Lin

ACS Applied Nano Materials· Volume 4· Issue 8

Abstract

MXenes belong to a large family of two-dimensional layered transition-metal carbides and nitrides. MXene nanosheets integrate the fascinating advantages of high electronic conductivity, excellent biocompatibility, and acid/base resistance. Herein, we demonstrate a “hospital-on-a-chip” system with multifunctional microneedle electrodes for biosensing and electrostimulation using highly stable MXene nanosheets. This system consists of integrated microchip biosensors for an efficient diagnosis and medical treatment elements for therapies, thus resembling a miniaturized hospital. Microneedles are composed of dozens of micron-sized needles that can be used as an effective and painless transdermal patch to puncture the dead skin barrier for drug delivery or biosensing purposes since they are directly in contact with the dermal layer inside the human body. The wearable MXene nanosheet-based microneedles can sense the tiny electric potential difference generated from the human eye movements or muscle contraction from the human arm. Therefore, the diseases associated with neuromuscular abnormalities such as myasthenia gravis can be monitored. Consequently, the transcutaneous electrical nerve stimulation treatment can be applied according to the feedback of the micro-biosensors. In addition, MXene microneedles can offer an electrically controlled drug delivery platform and the function of enhancing blood coagulation. Finally, MXene nanosheet-based microneedles provide an interesting platform for wearable micro-biosensors and offer an essential part of the hospital-on-a-chip system.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: MXene and MAX Phase Materials · Advanced Sensor and Energy Harvesting Materials · Graphene and Nanomaterials Applications

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

Affiliations

Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan

Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan

Department of Chemistry, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan

International Thyroid Surgery Center, Department of Otolaryngology-Head and Neck Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80756, Taiwan

Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Institute of Biological Chemistry, Academia Sinica, Nankang, Taipei 115, Taiwan

Group of Energy Materials, École Polytechnique FÉdÉrale de Lausanne (EPFL), Valais Wallis, Sion CH-1950, Switzerland

Innovation Research Institute of Energy and Power, North China Electric Power University, Beinong Rd 2, Beijing 102206, China

Funding

Ministry of Education · Ministry of Science and Technology, Taiwan · Higher Education Sprout Project, National Yang Ming Chiao Tung University

References (50)

Show source-provided references
1. 10.1021/acs.inorgchem.8b0289010.1021/acs.inorgchem.8b02890 ↗
2. 10.1021/acsnano.6b0018110.1021/acsnano.6b00181 ↗
3. 10.1016/j.apsusc.2020.14691510.1016/j.apsusc.2020.146915 ↗
4. 10.1007/s11665-018-3223-z10.1007/s11665-018-3223-z ↗
5. 10.3389/fbioe.2019.0029510.3389/fbioe.2019.00295 ↗
6. 10.1016/j.msec.2020.11121210.1016/j.msec.2020.111212 ↗
7. 10.1039/d0ta06094a10.1039/d0ta06094a ↗
8. 10.1016/j.trac.2019.11564310.1016/j.trac.2019.115643 ↗
9. 10.1021/acsami.9b2176510.1021/acsami.9b21765 ↗
10. 10.1016/j.bios.2017.01.01610.1016/j.bios.2017.01.016 ↗
11. 10.1038/srep3642210.1038/srep36422 ↗
12. 10.1021/acsami.7b1105510.1021/acsami.7b11055 ↗
13. 10.1002/smll.20190119010.1002/smll.201901190 ↗
14. 10.1016/j.bios.2019.11153110.1016/j.bios.2019.111531 ↗
15. 10.1016/j.bios.2018.10.01610.1016/j.bios.2018.10.016 ↗
16. 10.1002/adfm.20191050410.1002/adfm.201910504 ↗
17. 10.1016/j.trac.2018.05.02110.1016/j.trac.2018.05.021 ↗
18. 10.1039/d0nr01444c10.1039/d0nr01444c ↗
19. 10.1021/jacs.7b0781810.1021/jacs.7b07818 ↗
20. 10.1038/nnano.2010.4410.1038/nnano.2010.44 ↗
21. 10.2105/AJPH.2016.30327010.2105/AJPH.2016.303270 ↗
22. 10.1007/s12551-020-00770-w10.1007/s12551-020-00770-w ↗
23. 10.1016/j.jelekin.2019.01.00210.1016/j.jelekin.2019.01.002 ↗
24. 10.1080/0264041930873001010.1080/02640419308730010 ↗
25. 10.1002/adhm.20150001210.1002/adhm.201500012 ↗
26. 10.1039/c6an02625g10.1039/c6an02625g ↗
27. 10.1016/j.bios.2018.08.01010.1016/j.bios.2018.08.010 ↗
28. 10.1039/c9ra10374k10.1039/c9ra10374k ↗
29. 10.1021/acssensors.9b0028810.1021/acssensors.9b00288 ↗
30. 10.1109/TBME.2007.90649210.1109/TBME.2007.906492 ↗
31. 10.1016/j.apsb.2019.03.00710.1016/j.apsb.2019.03.007 ↗
32. 10.1146/annurev-chembioeng-060816-10151410.1146/annurev-chembioeng-060816-101514 ↗
33. 10.1002/adma.20190574010.1002/adma.201905740 ↗
34. 10.1016/j.ensm.2019.09.02610.1016/j.ensm.2019.09.026 ↗
35. 10.1109/TBCAS.2015.249294410.1109/TBCAS.2015.2492944 ↗
36. 10.1186/s12984-018-0390-y10.1186/s12984-018-0390-y ↗
37. 10.1021/acsphotonics.7b0143910.1021/acsphotonics.7b01439 ↗
38. 10.1021/acs.nanolett.6b0433910.1021/acs.nanolett.6b04339 ↗
39. 10.1038/nature1397010.1038/nature13970 ↗
40. 10.1016/j.bspc.2019.10173810.1016/j.bspc.2019.101738 ↗
41. 10.1007/978-3-319-59590-0_2210.1007/978-3-319-59590-0_22 ↗
42. 10.1016/j.cjche.2020.04.01410.1016/j.cjche.2020.04.014 ↗
43. 10.1007/s10971-018-4761-410.1007/s10971-018-4761-4 ↗
44. 10.1039/c5ra04340a10.1039/c5ra04340a ↗
45. 10.1016/j.snb.2017.08.16510.1016/j.snb.2017.08.165 ↗
46. Wang, Y. L.; Su, A. W. Y.; Han, T. Y.; Lin, C. L.; Hsu, L. C. EMG Based Rehabilitation Systems - Approaches for ALS Patients in Different Stages. In Proceedings - IEEE International Conference on Multimedia and Expo; IEEE Computer Society: 2015; Vol. 2015.10.1109/ICME.2015.7177398 ↗
47. 10.1002/mus.2043510.1002/mus.20435 ↗
48. 10.3389/fnbot.2019.0000710.3389/fnbot.2019.00007 ↗
49. 10.1016/j.eswa.2009.10.01710.1016/j.eswa.2009.10.017 ↗
50. 10.1109/TBME.2018.283455510.1109/TBME.2018.2834555 ↗