Cyanogel‐Transformed Porous Palladium and Iron Framework Intermixed with rGO for Wearable Hydrogen Sensing
Source abstract
Abstract
Abstract Wearable hydrogen (H 2 ) sensing is necessary to monitor the H 2 leakage in its transportation and storage, of which ppm‐concentration detection limit and fast response at room temperature are highly desired. Here, a wearable H 2 sensing working at room temperature is developed with palladium and iron framework intermixed with reduced graphene oxide (rGO//Pd–Fe FW), which is synthesized by combined Pd–Fe cyanogel immobilized with graphene oxide as precursor and in situ reduction. As‐prepared rGO//Pd–Fe FW is observed with porous FW structure composed of interconnected Pd–Fe nanoparticles, in which rGO is evenly intermixed. Beneficially, rGO//Pd‐Fe FW exhibits 2 ppm low detection limit and 2 s fast response (1 v/v% H 2 ) at room temperature. Such excellent H 2 sensing performance may be attributed to the synergistic effect of the optimized Pd–Fe FW's catalytic activity, boosted electron transfers between Pd hydride and rGO, and enriched adsorption sites over porous FW's surface. Practically, the perceptron learning algorithm combined with principal component analysis is conducted to identify the H 2 leakage, and the wearable H 2 sensing devices are built by integrating rGO//Pd–Fe FW over the paper and flexible printed circuit board with reliable sensing responses.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology and College of Electronic Information and Optical Engineering Nankai University Tianjin 300350 P. R. China
Funding
National Natural Science Foundation of China
References (35)
Show source-provided references
1. 10.1126/science.adh887210.1126/science.adh8872 ↗
2. 10.1002/smll.20210627910.1002/smll.202106279 ↗
3. 10.1007/s41918-022-00159-110.1007/s41918-022-00159-1 ↗
4. 10.1021/acssensors.2c0112910.1021/acssensors.2c01129 ↗
5. 10.1002/smll.20210687410.1002/smll.202106874 ↗
6. 10.1021/acsami.0c0643510.1021/acsami.0c06435 ↗
7. 10.1007/s12274-022-5077-910.1007/s12274-022-5077-9 ↗
8. 10.1002/smll.20231184010.1002/smll.202311840 ↗
9. 10.1002/smll.20230265210.1002/smll.202302652 ↗
10. 10.1021/acssensors.4c0058910.1021/acssensors.4c00589 ↗
11. 10.1002/smll.20220802610.1002/smll.202208026 ↗
12. 10.1002/adma.20200592910.1002/adma.202005929 ↗
13. 10.1021/acssensors.3c0270210.1021/acssensors.3c02702 ↗
14. 10.1021/acssensors.0c0104010.1021/acssensors.0c01040 ↗
15. 10.1038/s41586-019-1603-710.1038/s41586-019-1603-7 ↗
16. 10.1016/j.ijhydene.2019.12.04810.1016/j.ijhydene.2019.12.048 ↗
17. 10.1021/acsanm.1c0134810.1021/acsanm.1c01348 ↗
18. 10.1021/acssensors.1c0268110.1021/acssensors.1c02681 ↗
19. 10.1021/acsnano.3c0158210.1021/acsnano.3c01582 ↗
20. 10.1016/j.snb.2021.13094410.1016/j.snb.2021.130944 ↗
21. 10.1016/j.jhazmat.2020.12305410.1016/j.jhazmat.2020.123054 ↗
22. 10.1016/j.cej.2022.14125810.1016/j.cej.2022.141258 ↗
23. 10.1021/acsnano.9b0782610.1021/acsnano.9b07826 ↗
24. 10.1021/acsnano.7b0798510.1021/acsnano.7b07985 ↗
25. 10.1021/jacs.5b0386510.1021/jacs.5b03865 ↗
26. 10.1016/j.jcis.2022.09.13910.1016/j.jcis.2022.09.139 ↗
27. 10.1016/j.ijhydene.2024.03.03710.1016/j.ijhydene.2024.03.037 ↗
28. 10.1021/acssensors.3c0165910.1021/acssensors.3c01659 ↗
29. 10.1016/j.snb.2021.13003510.1016/j.snb.2021.130035 ↗
30. 10.1002/adma.20230352310.1002/adma.202303523 ↗
31. 10.1002/smll.20230148510.1002/smll.202301485 ↗
32. 10.1007/s12598-023-02310-310.1007/s12598-023-02310-3 ↗
33. 10.1002/smll.20210687410.1002/smll.202106874 ↗
34. 10.1021/nn102803q10.1021/nn102803q ↗
35. 10.1016/j.ijhydene.2016.02.02310.1016/j.ijhydene.2016.02.023 ↗