journal article·2025-10-29

Li-Ion Synaptic Transistor-Based Nociceptive Afferent Neural Sensing Platform for Fire Scenarios

Lei Zhu, Yi-Yan Cai, Kun-Wen Huang, Zhi-Wei Zheng

ACS Applied Materials & Interfaces· Volume 17· Issue 43
Publisher page

Abstract

In this study, a photothermal pain sensing neural platform for fire scenarios was implemented based on the integration of a multigate Li-ion electrolyte-gated transistor device with a thermoelectric generator and a photoresistor. Peripheral sensitization, desensitization, pain threshold, and other basic nociceptive behaviors of nociceptive afferents were successfully emulated. This photothermal pain sensing platform realized the judgment of the distance of the fire from the incident light of the photothermal source. The secondary injury process and the composite injury sensing process were also mimicked. This work extends the applications of Li-ion synaptic transistors in artificial nervous systems and provides new ideas for the development of practical and feasible pain sensing systems.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: EEG and Brain-Computer Interfaces · Advanced Memory and Neural Computing · CCD and CMOS Imaging Sensors

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Affiliations

School of Electronic Science and Engineering

Xiamen University

Funding

National Natural Science Foundation of China

References (28)

Show source-provided references
1. 10.1109/TED.2020.303976210.1109/TED.2020.3039762 ↗
2. 10.1126/science.aao009810.1126/science.aao0098 ↗
3. 10.1021/acsnano.4c0963210.1021/acsnano.4c09632 ↗
4. 10.3390/ijms2110355310.3390/ijms21103553 ↗
5. 10.1016/j.neuron.2007.07.01610.1016/j.neuron.2007.07.016 ↗
6. 10.1016/j.cell.2009.09.02810.1016/j.cell.2009.09.028 ↗
7. 10.1002/adma.20170432010.1002/adma.201704320 ↗
8. 10.1038/nm.223510.1038/nm.2235 ↗
9. 10.1021/acsnano.3c0835710.1021/acsnano.3c08357 ↗
10. 10.1097/ALN.000000000000213010.1097/ALN.0000000000002130 ↗
11. 10.1016/S1474-4422(10)70143-510.1016/S1474-4422(10)70143-5 ↗
12. 10.3390/su1306340510.3390/su13063405 ↗
13. 10.3390/s2209331010.3390/s22093310 ↗
14. 10.1021/acsami.2c1648110.1021/acsami.2c16481 ↗
15. 10.1039/D3TC03580H10.1039/D3TC03580H ↗
16. 10.1063/5.008253810.1063/5.0082538 ↗
17. 10.1002/admt.20210037310.1002/admt.202100373 ↗
18. 10.1021/acssensors.4c0156810.1021/acssensors.4c01568 ↗
19. 10.1002/admt.20230204710.1002/admt.202302047 ↗
20. 10.1039/D3MH01766D10.1039/D3MH01766D ↗
21. 10.1021/acsaelm.3c0126610.1021/acsaelm.3c01266 ↗
22. 10.1039/D5TC00691K10.1039/D5TC00691K ↗
23. 10.1172/JCI4284310.1172/JCI42843 ↗
24. 10.1002/adfm.20240010510.1002/adfm.202400105 ↗
25. 10.1016/j.pain.2010.09.03010.1016/j.pain.2010.09.030 ↗
26. 10.1038/nrn294710.1038/nrn2947 ↗
27. 10.1038/s41598-017-00939-610.1038/s41598-017-00939-6 ↗
28. 10.1109/TED.2021.307517410.1109/TED.2021.3075174 ↗