Self-Powered Biomimetic Tactile Sensing with Broad Linear Range via Synchronous Mechano-Electrical Regulation
Source abstract
Abstract
Self-powered flexible pressure sensors based on the mechano-potentiometric conversion mechanism are gaining research interest for robotic tactile sensing and wearables. In contrast to the dynamic mechanical stimuli responsiveness of piezoelectric and triboelectric sensors, they offer the advantage of transducing static mechanical stimuli into sustained voltage outputs. However, sensors employing this mechanism suffer from limitations such as a narrow pressure response range and poor response linearity. Herein, a mechano-potentiometric transducer based on an internal-cavity/microhemisphere-structured solid electrolyte is presented, which achieves a broad linear sensing range in pressure sensing through the synergistic mechano-electrical regulation upon applied pressure. The fabricated sensor exhibits broad (0–350 kPa) and linear (1.66 mV kPa –1, R 2 = 0.995) pressure sensing performance and shows fast response (42.3 ms)/recovery (62.6 ms) and excellent repeatability (>10,000 cycles). To evaluate its spatial pressure sensing capability, the sensor is scaled into a matrix for pressure distribution mapping. Furthermore, integration of the matrix onto a robotic gripper enables the softness recognition of eight distinct silicone samples with a high accuracy of 95.31%, and facilitates feedback control of the gripper’s grasping force based on fruit softness. This work provides a strategy for high-linearity, broad-range self-powered potentiometric sensors for robotic tactile sensing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Muscle activation and electromyography studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Biomimetic Robots and Systems of Ministry of Education, School of Mechatronical Engineering
Beijing Institute of Technology
State Key Laboratory of Nuclear Physics and Technology, School of Physics
Peking University
Department of Advanced Manufacturing and Robotics, College of Engineering
Funding
China Postdoctoral Science Foundation · National Natural Science Foundation of China · Beijing Institute of Technology Research Funds for High-Level Talents
References (47)
Show source-provided references
1. 10.1088/1361-665X/ac0c2e10.1088/1361-665X/ac0c2e ↗
2. 10.1109/TRO.2025.356304610.1109/TRO.2025.3563046 ↗
3. 10.1126/scirobotics.abc813410.1126/scirobotics.abc8134 ↗
4. 10.1002/adfm.20250051910.1002/adfm.202500519 ↗
5. 10.1126/sciadv.abd779510.1126/sciadv.abd7795 ↗
6. 10.1016/j.nanoen.2025.11069710.1016/j.nanoen.2025.110697 ↗
7. 10.1038/s41467-023-42722-410.1038/s41467-023-42722-4 ↗
8. 10.1109/TRO.2020.300323010.1109/TRO.2020.3003230 ↗
9. 10.1126/sciadv.adr930010.1126/sciadv.adr9300 ↗
10. 10.1038/s41467-024-51261-510.1038/s41467-024-51261-5 ↗
11. 10.1126/scirobotics.aaz923910.1126/scirobotics.aaz9239 ↗
12. 10.1016/j.sna.2018.07.00610.1016/j.sna.2018.07.006 ↗
13. 10.1021/acssensors.3c0081810.1021/acssensors.3c00818 ↗
14. 10.1002/admt.20220195910.1002/admt.202201959 ↗
15. 10.1021/acssensors.4c0323810.1021/acssensors.4c03238 ↗
16. 10.1002/smll.20240552010.1002/smll.202405520 ↗
17. 10.1002/adma.20241997010.1002/adma.202419970 ↗
18. 10.1126/science.aaw197410.1126/science.aaw1974 ↗
19. 10.1002/adfm.20180637910.1002/adfm.201806379 ↗
20. 10.1002/adfm.20230624910.1002/adfm.202306249 ↗
21. 10.3390/gels903025710.3390/gels9030257 ↗
22. 10.1002/adfm.20210885610.1002/adfm.202108856 ↗
23. 10.1126/sciadv.aba106210.1126/sciadv.aba1062 ↗
24. 10.1002/adma.20240677810.1002/adma.202406778 ↗
25. 10.1109/JSEN.2024.347617310.1109/JSEN.2024.3476173 ↗
26. 10.1016/j.cej.2022.13691010.1016/j.cej.2022.136910 ↗
27. 10.1016/j.pmatsci.2024.10135610.1016/j.pmatsci.2024.101356 ↗
28. 10.1021/acsnano.4c1415710.1021/acsnano.4c14157 ↗
29. 10.1038/s41467-022-33133-y10.1038/s41467-022-33133-y ↗
30. 10.1016/j.cej.2021.13428310.1016/j.cej.2021.134283 ↗
31. 10.1002/adma.20200597010.1002/adma.202005970 ↗
32. 10.1016/j.nanoen.2022.10795410.1016/j.nanoen.2022.107954 ↗
33. 10.1007/s40820-024-01532-z10.1007/s40820-024-01532-z ↗
34. 10.1002/advs.20240151510.1002/advs.202401515 ↗
35. 10.1002/adma.20220536910.1002/adma.202205369 ↗
36. 10.1002/adma.20230879510.1002/adma.202308795 ↗
37. 10.1002/adma.20241794410.1002/adma.202417944 ↗
38. 10.1016/j.cej.2023.14758610.1016/j.cej.2023.147586 ↗
39. 10.1016/j.cej.2024.15481210.1016/j.cej.2024.154812 ↗
40. 10.1038/s41467-023-42361-910.1038/s41467-023-42361-9 ↗
41. 10.1021/acsnano.1c1053510.1021/acsnano.1c10535 ↗
42. 10.1016/j.nanoen.2018.11.05710.1016/j.nanoen.2018.11.057 ↗
43. 10.1007/s40820-021-00770-910.1007/s40820-021-00770-9 ↗
44. 10.1016/j.jcis.2020.08.02010.1016/j.jcis.2020.08.020 ↗
45. 10.1016/j.jid.2019.06.13710.1016/j.jid.2019.06.137 ↗
46. 10.1021/acsnano.4c0410010.1021/acsnano.4c04100 ↗
47. McInnes, L.; Healy, J.; Melville, J. Umap: Uniform Manifold Approximation and Projection for Dimension Reduction. 2018 arXiv:1802.03426. arXiv.org e-Printarchive. https://doi.org/10.48550/arXiv.1802.03426.