Phase-Transition-Coupled Optical-Electrical Dual-Channel Thermochromic Temperature Sensor: Wood-Derived Porous Framework for Data-Driven Wide-Range Quantitative Monitoring
Source abstract
Abstract
The development of multifunctional temperature sensors capable of simultaneous visual indication and quantitative readout remains challenging, particularly within bioderived porous materials that often suffer from structural instability and limited sensing range. Here, we report a dual-channel thermosensing platform constructed within a delignified-wood-derived porous framework, integrating thermochromic microcapsules and vanadium dioxide (VO 2 ) nanoparticles to enable coupled optical and electrical responses across 20–90 °C. The hierarchical porous architecture accommodates both organic and inorganic functional components, allowing reversible phase-transition-assisted electrical conduction alongside nonlinear optical color evolution. To decode the complex spectral–temperature relationship of the thermochromic channel, a data-driven regression strategy was employed to establish a quantitative mapping model. The optical channel achieved a root-mean-square error (RMSE) of 1.79 °C for temperature prediction within the calibrated range, while the electrical channel exhibited reproducible resistance transitions with a temperature deviation within ±1 °C at calibrated points. Ab initio molecular dynamics simulations further elucidated the intrinsic lattice evolution of VO 2 during the phase transition, consistent with the experimentally observed resistance modulation. This work demonstrates a platform strategy based on sustainable porous materials for integrating multiphysics thermosensing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Transition Metal Oxide Nanomaterials · Advanced Sensor Technologies Research · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer and Control Engineering
Northeast Forestry University
Funding
Northeast Forestry University · Harbin Science and Technology Bureau · National Natural Science Foundation of China
References (50)
Show source-provided references
1. 10.1016/j.measen.2023.10069210.1016/j.measen.2023.100692 ↗
2. 10.1016/j.nwnano.2024.10005010.1016/j.nwnano.2024.100050 ↗
3. 10.1021/acsami.4c0246710.1021/acsami.4c02467 ↗
4. 10.3390/designs903007310.3390/designs9030073 ↗
5. 10.1021/acsomega.5c0688910.1021/acsomega.5c06889 ↗
6. 10.1039/D4SD00189C10.1039/D4SD00189C ↗
7. 10.1021/acsnano.3c0424610.1021/acsnano.3c04246 ↗
8. 10.1016/j.jmst.2025.06.03810.1016/j.jmst.2025.06.038 ↗
9. 10.1007/s11998-024-00982-910.1007/s11998-024-00982-9 ↗
10. 10.1021/acsami.3c1868910.1021/acsami.3c18689 ↗
11. 10.1016/j.dyepig.2023.11178310.1016/j.dyepig.2023.111783 ↗
12. 10.1039/D3RA06432H10.1039/D3RA06432H ↗
13. 10.1016/j.dyepig.2024.11239710.1016/j.dyepig.2024.112397 ↗
14. 10.1016/j.eurpolymj.2025.11440810.1016/j.eurpolymj.2025.114408 ↗
15. 10.1039/D4TC04185B10.1039/D4TC04185B ↗
16. 10.1002/aelm.20230069910.1002/aelm.202300699 ↗
17. 10.1038/s41377-024-01560-910.1038/s41377-024-01560-9 ↗
18. 10.1063/5.016493610.1063/5.0164936 ↗
19. 10.3390/nano1407058210.3390/nano14070582 ↗
20. 10.1038/s41598-025-95417-910.1038/s41598-025-95417-9 ↗
21. 10.1002/adma.20160042710.1002/adma.201600427 ↗
22. 10.3390/polym1201016510.3390/polym12010165 ↗
23. 10.1038/s41467-023-38481-x10.1038/s41467-023-38481-x ↗
24. 10.1002/adma.20200289010.1002/adma.202002890 ↗
25. 10.1007/s40820-025-01953-410.1007/s40820-025-01953-4 ↗
26. 10.1038/s41467-024-55025-z10.1038/s41467-024-55025-z ↗
27. 10.1021/acsami.4c0133310.1021/acsami.4c01333 ↗
28. 10.1038/s41467-024-55760-310.1038/s41467-024-55760-3 ↗
29. 10.1016/j.tsep.2023.10233410.1016/j.tsep.2023.102334 ↗
30. 10.1021/acsaom.3c0019810.1021/acsaom.3c00198 ↗
31. 10.3390/rs1520493410.3390/rs15204934 ↗
32. Chen, T.; Guestrin, C. In XGBoost: A Scalable Tree Boosting System , Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; ACM: New York, 2016; pp 785–794.10.1145/2939672.2939785 ↗
33. Pedregosa F. · J. Mach. Learn. Res. · 2011 · 12 · 2825
34. 10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
35. 10.1021/acsami.1c1380210.1021/acsami.1c13802 ↗
36. 10.1021/acssuschemeng.2c0763510.1021/acssuschemeng.2c07635 ↗
37. 10.1021/acsami.3c1170610.1021/acsami.3c11706 ↗
38. 10.1021/acsami.0c2136910.1021/acsami.0c21369 ↗
39. 10.1021/cr980071510.1021/cr9800715 ↗
40. 10.1021/acsami.3c1148410.1021/acsami.3c11484 ↗
41. 10.1021/acs.jcim.2c0062610.1021/acs.jcim.2c00626 ↗
42. 10.1146/annurev-matsci-070218-01001510.1146/annurev-matsci-070218-010015 ↗
43. 10.1021/ac60214a04710.1021/ac60214a047 ↗
44. 10.1021/acsanm.3c0568810.1021/acsanm.3c05688 ↗
45. 10.1038/s41586-018-0337-210.1038/s41586-018-0337-2 ↗
46. 10.1002/smll.20180202510.1002/smll.201802025 ↗
47. 10.1016/0022-4596(71)90091-010.1016/0022-4596(71)90091-0 ↗
48. 10.1038/s41578-020-0195-z10.1038/s41578-020-0195-z ↗
49. 10.1021/acsami.2c0458410.1021/acsami.2c04584 ↗
50. 10.1103/PhysRevB.49.1425110.1103/PhysRevB.49.14251 ↗