journal article·2026-07-03Open access

Non‐Line‐of‐Sight Passive Ammonia Sensor Loaded With MXene/In 2 O 3 Composites for Agricultural Products Quality Deterioration Detection

Guoping Hu, Lin He, Fanli Meng, Daming Dong, Guolong Shi

Abstract

ABSTRACT Passive radio‐frequency identification technology (RFID) shows potential for non‐line‐of‐sight (NLoS) sensing, but electromagnetic attenuation and reflection losses caused by dielectric absorption and multipath effects reduce detection accuracy and stability. In this work, a flexible passive RFID sensor for NLoS ammonia detection is developed by integrating a spiral antenna with an LC interdigital electrode. MXene is modified with In 2 O 3 nanoparticles to form a gas‐sensitive layer with enhanced surface activity and electronic modulation, thereby strengthening the ammonia‐induced frequency response. Moreover, structural separation of electromagnetic coupling and sensing functions mitigates the nonuniform distribution of coupled electromagnetic energy in the sensing tag. The experimental results demonstrate response characteristics under NLoS dielectric interference, with an S 11 amplitude attenuation of Δ|S 11 | = 9.19 dB and an ammonia‐induced resonance frequency shift of Δ | f | = 0.48 MHz. On this basis, an S 11 amplitude‐frequency signal separation strategy is innovatively proposed to decouple NLoS dielectric interference signals from gas‐induced response signals, thereby clarifying the frequency perturbation effect of dielectric interference on ammonia sensing signals. Combined with electromagnetically decoupled signals, dielectric interference compensation is performed under conditions of 25°C and 40% RH. NLoS dielectric compensation stabilizes ammonia‐induced frequency shifts, suppresses interference, and enables reliable, rapid, nondestructive detection of quality deterioration in sealed fresh‐food packaging.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: RFID technology advancements · Electromagnetic wave absorption materials · Microwave and Dielectric Measurement Techniques

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

Affiliations

School of Artificial Intelligence Anhui Agricultural University Hefei Anhui China

School of Information Science and Engineering Northeastern University Shenyang China

Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China

Key Laboratory of Agricultural Sensors Ministry of Agriculture and Rural Hefei China

Funding

National Natural Science Foundation of China

References (58)

Show source-provided references
1. 10.1016/j.tifs.2025.10488810.1016/j.tifs.2025.104888 ↗
2. 10.1016/j.tifs.2025.10540110.1016/j.tifs.2025.105401 ↗
3. 10.1016/j.foodcont.2025.11169210.1016/j.foodcont.2025.111692 ↗
4. 10.1016/j.foodchem.2025.14280510.1016/j.foodchem.2025.142805 ↗
5. 10.1016/j.foodchem.2025.14442610.1016/j.foodchem.2025.144426 ↗
6. 10.1016/j.compag.2023.10808210.1016/j.compag.2023.108082 ↗
7. 10.1007/s00217‐023‐04320‐y10.1007/s00217‐023‐04320‐y ↗
8. 10.1016/j.saa.2025.12606910.1016/j.saa.2025.126069 ↗
9. 10.1002/advs.20251683310.1002/advs.202516833 ↗
10. 10.1016/j.sna.2025.11692610.1016/j.sna.2025.116926 ↗
11. 10.1038/s41467‐019‐08675‐310.1038/s41467‐019‐08675‐3 ↗
12. 10.1038/s41378‐025‐01010‐510.1038/s41378‐025‐01010‐5 ↗
13. 10.1038/s43016‐023‐00750‐910.1038/s43016‐023‐00750‐9 ↗
14. 10.1021/acs.nanolett.4c0667610.1021/acs.nanolett.4c06676 ↗
15. 10.1038/s41467‐024‐51279‐910.1038/s41467‐024‐51279‐9 ↗
16. 10.1002/advs.20230180710.1002/advs.202301807 ↗
17. 10.1002/advs.20241134610.1002/advs.202411346 ↗
18. 10.1016/j.snb.2025.13804510.1016/j.snb.2025.138045 ↗
19. 10.1021/acssensors.3c0120010.1021/acssensors.3c01200 ↗
20. 10.1007/s40820‐024‐01599‐810.1007/s40820‐024‐01599‐8 ↗
21. 10.1016/j.measurement.2025.12010510.1016/j.measurement.2025.120105 ↗
22. 10.1002/advs.20250929310.1002/advs.202509293 ↗
23. 10.1002/advs.20250078610.1002/advs.202500786 ↗
24. 10.1021/acsnano.4c1114910.1021/acsnano.4c11149 ↗
25. 10.1038/s41467‐020‐15092‐410.1038/s41467‐020‐15092‐4 ↗
26. 10.1002/advs.20230399810.1002/advs.202303998 ↗
27. 10.1002/advs.20251179410.1002/advs.202511794 ↗
28. 10.1021/acsanm.3c0163710.1021/acsanm.3c01637 ↗
29. 10.1021/acssensors.9b0012710.1021/acssensors.9b00127 ↗
30. 10.1002/adfm.20231064110.1002/adfm.202310641 ↗
31. 10.1021/acssensors.5c0021510.1021/acssensors.5c00215 ↗
32. 10.1002/adfm.20252418210.1002/adfm.202524182 ↗
33. 10.1016/j.tifs.2024.10464610.1016/j.tifs.2024.104646 ↗
34. 10.1109/JSEN.2023.325357110.1109/JSEN.2023.3253571 ↗
35. 10.1002/advs.20210555310.1002/advs.202105553 ↗
36. 10.1016/j.carbpol.2020.11646710.1016/j.carbpol.2020.116467 ↗
37. 10.1016/j.cej.2025.16035410.1016/j.cej.2025.160354 ↗
38. 10.1002/advs.20251044510.1002/advs.202510445 ↗
39. 10.1016/j.jechem.2024.11.06210.1016/j.jechem.2024.11.062 ↗
40. 10.1016/j.apsusc.2025.16501410.1016/j.apsusc.2025.165014 ↗
41. 10.1039/D0NA00318B10.1039/D0NA00318B ↗
42. 10.1016/j.snb.2022.13191810.1016/j.snb.2022.131918 ↗
43. 10.1021/acssensors.9b0130810.1021/acssensors.9b01308 ↗
44. 10.1109/TIE.2020.298824310.1109/TIE.2020.2988243 ↗
45. 10.1016/j.sna.2024.11616710.1016/j.sna.2024.116167 ↗
46. 10.1038/s41467‐023‐39213‐x10.1038/s41467‐023‐39213‐x ↗
47. 10.1016/j.compag.2024.10872010.1016/j.compag.2024.108720 ↗
48. 10.1016/j.coco.2025.10256910.1016/j.coco.2025.102569 ↗
49. 10.1016/j.jmst.2025.01.08610.1016/j.jmst.2025.01.086 ↗
50. 10.1016/j.cej.2025.16437010.1016/j.cej.2025.164370 ↗
51. 10.1016/j.cej.2025.16429010.1016/j.cej.2025.164290 ↗
52. 10.1016/j.ceramint.2025.10.13410.1016/j.ceramint.2025.10.134 ↗
53. 10.1007/s12598‐024‐03037‐510.1007/s12598‐024‐03037‐5 ↗
54. 10.1016/j.snb.2017.03.16410.1016/j.snb.2017.03.164 ↗
55. 10.1016/j.jfca.2025.10741710.1016/j.jfca.2025.107417 ↗
56. 10.1016/j.cej.2025.16691810.1016/j.cej.2025.166918 ↗
57. 10.1109/TIM.2023.330043310.1109/TIM.2023.3300433 ↗
58. 10.1109/JSEN.2024.345994010.1109/JSEN.2024.3459940 ↗