The Effect of Sensor Response Measurement Techniques on the Sensitivity of Organic Field‐Effect Transistors to NO 2
Source abstract
Abstract
Abstract Nitrogen dioxide sensors are important for environmental monitoring, and OFETs‐based devices feature high sensitivity, low production cost and power consumption. While transient pulsed saturation measurements are the most common approach to measure the sensor response, here we systematically compare it with the periodic transfer curves measurement method in both linear and saturation regimes for C8‐BTBT OFETs sensitive to NO 2 . We show that the sensitivity strongly depends on the measurement routine, governed by competition for deep trap sites between the electrically injected holes and NO 2 ‐induced doping. The transfer curves method reveals that mobility change dominates in the saturation regime, while threshold voltage shift dominates in the linear regime, confirming deep traps role as key sensing receptor sites. Pulsed measurements, especially in the linear regime, yielded the highest sensitivity (218 ± 18%/ppm) by combining low charge density with low duty cycle kinetics to maximize the initial deep trap availability. DFT calculations support preferential hole transfer from NO 2 to the trap states. Altogether confirming that minimizing trap filling by injected charge (i.e., lower current density operation) enhances OFET sensitivity. This dependence on the measurement routine persists even for OFETs containing metalloporphyrin receptor layers. These findings provide guidelines for optimizing OFET sensor design and operation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Analytical Chemistry and Sensors · Organic Electronics and Photovoltaics · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Enikolopov Institute of Synthetic Polymeric Materials of Russian Academy of Science Profsoyuznaya st. 70 Moscow 117393 Russian Federation
NRC “Kurchatov Institute” Novatorov Str. 7A‐1 Moscow 119421 Russian Federation
Funding
Russian Science Foundation · Russian Foundation for Basic Research · Ministry of Science and Higher Education of the Russian Federation
References (63)
Show source-provided references
1. 10.1039/D0TC03132A10.1039/D0TC03132A ↗
2. 10.1002/cplu.20190027710.1002/cplu.201900277 ↗
3. 10.1016/j.trac.2018.11.02710.1016/j.trac.2018.11.027 ↗
4. 10.1038/s41598-021-88569-x10.1038/s41598-021-88569-x ↗
5. 10.1134/S156009042105015810.1134/S1560090421050158 ↗
6. 10.1007/s43939-022-00023-510.1007/s43939-022-00023-5 ↗
7. 10.1016/j.pnsc.2022.01.00110.1016/j.pnsc.2022.01.001 ↗
8. 10.1088/1752-7155/7/1/01710410.1088/1752-7155/7/1/017104 ↗
9. 10.1021/acsanm.2c0047410.1021/acsanm.2c00474 ↗
10. 10.3390/bios1006005710.3390/bios10060057 ↗
11. 10.1016/j.orgel.2010.02.00710.1016/j.orgel.2010.02.007 ↗
12. 10.1002/adma.20120413410.1002/adma.201204134 ↗
13. 10.1002/adma.20140537810.1002/adma.201405378 ↗
14. 10.1002/adfm.20170001810.1002/adfm.201700018 ↗
15. 10.1016/j.orgel.2021.10608310.1016/j.orgel.2021.106083 ↗
16. 10.1021/acs.langmuir.7b0246610.1021/acs.langmuir.7b02466 ↗
17. 10.1039/D0MH01420F10.1039/D0MH01420F ↗
18. 10.1039/D2TC05083H10.1039/D2TC05083H ↗
19. 10.1021/acsami.0c0080310.1021/acsami.0c00803 ↗
20. Huang W. · Adv. Mater. · 2017 · 29
21. 10.1016/j.cplett.2018.02.04310.1016/j.cplett.2018.02.043 ↗
22. 10.1021/acsami.0c0481010.1021/acsami.0c04810 ↗
23. 10.1038/nmat503510.1038/nmat5035 ↗
24. 10.1063/1.158135210.1063/1.1581352 ↗
25. 10.1063/1.196843710.1063/1.1968437 ↗
26. 10.1103/PhysRevB.77.16531110.1103/PhysRevB.77.165311 ↗
27. Lee B. · Phys. Rev. B · 2010 · 82 · 3
28. 10.1002/adma.20100427810.1002/adma.201004278 ↗
29. 10.1002/adma.20070268810.1002/adma.200702688 ↗
30. 10.1002/adma.20120045510.1002/adma.201200455 ↗
31. 10.1016/j.orgel.2015.05.02210.1016/j.orgel.2015.05.022 ↗
32. 10.1021/acsami.8b1542710.1021/acsami.8b15427 ↗
33. 10.1016/j.snb.2020.12860910.1016/j.snb.2020.128609 ↗
34. 10.1016/j.matlet.2021.12937010.1016/j.matlet.2021.129370 ↗
35. 10.1039/C9NR06096K10.1039/C9NR06096K ↗
36. 10.1021/ja074841i10.1021/ja074841i ↗
37. 10.1143/APEX.3.12160110.1143/APEX.3.121601 ↗
38. 10.1016/j.orgel.2019.01.01010.1016/j.orgel.2019.01.010 ↗
39. 10.1186/s11671-019-3007-x10.1186/s11671-019-3007-x ↗
40. 10.1016/j.orgel.2013.09.01810.1016/j.orgel.2013.09.018 ↗
41. 10.1001/jama.2012.12610.1001/jama.2012.126 ↗
42. 10.1134/S107042802406013710.1134/S1070428024060137 ↗
43. 10.1002/aelm.20180007610.1002/aelm.201800076 ↗
44. 10.1063/1.343271610.1063/1.3432716 ↗
45. 10.1063/1.47852210.1063/1.478522 ↗
46. 10.1063/1.46309610.1063/1.463096 ↗
47. 10.1002/jcc.2175910.1002/jcc.21759 ↗
48. 10.1002/wcms.8110.1002/wcms.81 ↗
49. G. A.Andrienko Chemcraft‐graphical software for visualization of quantum chemistry computations. Version 1.8 build 6822024.
50. 10.1002/pssa.20170086210.1002/pssa.201700862 ↗
51. 10.1039/C6CP06919C10.1039/C6CP06919C ↗
52. 10.1002/adfm.20100156010.1002/adfm.201001560 ↗
53. 10.1002/adma.20150564110.1002/adma.201505641 ↗
54. 10.1016/S0925-4005(00)00541-410.1016/S0925-4005(00)00541-4 ↗
55. 10.1016/j.snb.2016.12.11610.1016/j.snb.2016.12.116 ↗
56. 10.1002/adma.20170319210.1002/adma.201703192 ↗
57. 10.1063/1.276763310.1063/1.2767633 ↗
58. 10.1039/C8TC02447B10.1039/C8TC02447B ↗
59. 10.1021/acssensors.9b0175110.1021/acssensors.9b01751 ↗
60. 10.1002/pssr.20200008610.1002/pssr.202000086 ↗
61. 10.1039/C8NR01275J10.1039/C8NR01275J ↗
62. 10.1016/j.orgel.2021.10615910.1016/j.orgel.2021.106159 ↗
63. 10.1007/s11431-020-1785-610.1007/s11431-020-1785-6 ↗