journal article·2024-07-26

Uniform and Dense Hotspots in Au Rough-Nanocube Monolayer for Sensitive and Reproducible SERS Detection

Ze-Run Zhou, Wei-Xi Liu, Zheng-Yuan Yao, Zi-Xing Zheng, Liang Ma, Xiang-Bai Chen, Si-Jing Ding

ACS Applied Nano Materials· Volume 7· Issue 14
Publisher page

Abstract

Plasmonic metal nanocrystals show great potential in surface-enhanced Raman spectroscopy (SERS) detection. However, the fabrication of metal substrates with uniform and dense hotspots for sensitive and reproducible SERS remains a challenge. This study demonstrates a steerable approach to prepare a monolayer composed of Au rough nanocubes (RNCs) derived from Au nanocubes (NCs), which exhibits efficient and reproducible SERS activity for detecting organic pollutants and pesticide residues. The structure-adjustable Au RNCs are prepared by growing Au nanoparticles on Au NCs via a PbS-assisted surface modification. The as-prepared Au RNCs show better SERS activity on detecting dye molecules compared to the initial Au NCs. This improvement is attributed to the dense hotspots generated by the plasmon coupling between the Au NCs and the Au nanoparticles. Subsequently, a uniform Au RNC monolayer is fabricated using an interfacial self-assembly method, thereby exhibiting further improved SERS activity due to the electromagnetic enhancement between the adjacent nanoparticles. The Au RNC monolayer has excellent signal reproducibility with a relative standard deviation of 4.13% on detecting crystal violet owing to the large-area and uniform hotspots. Additionally, the Au RNC monolayer demonstrates excellent SERS efficiency in detecting residual thiram on fruits (10 –11 M, analytical enhancement factor of 1.24 × 10 8 at 1371 cm –1 ), melamine in milk, and dye in wastewater. These capabilities demonstrate the immense potential of the Au RNC monolayer for applications in food safety and environmental monitoring.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Advanced biosensing and bioanalysis techniques · Nanocluster Synthesis and Applications

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

Affiliations

Hubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan 430205, PR China

School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan 430074, PR China

Funding

Wuhan Institute of Technology · Natural Science Foundation of Hubei Province · National Natural Science Foundation of China · Knowledge Innovation Program of Wuhan-Shuguang Project

References (45)

Show source-provided references
1. 10.1038/s42254-020-0171-y10.1038/s42254-020-0171-y ↗
2. 10.1038/natrevmats.2016.2110.1038/natrevmats.2016.21 ↗
3. 10.1039/C4AN01079E10.1039/C4AN01079E ↗
4. 10.1039/C8NR06444J10.1039/C8NR06444J ↗
5. 10.1002/smll.20210075510.1002/smll.202100755 ↗
6. 10.1016/j.cej.2023.14593310.1016/j.cej.2023.145933 ↗
7. 10.1016/j.cej.2023.14153910.1016/j.cej.2023.141539 ↗
8. 10.1021/acs.accounts.6b0038410.1021/acs.accounts.6b00384 ↗
9. 10.1021/acs.accounts.3c0020710.1021/acs.accounts.3c00207 ↗
10. 10.1038/s41596-023-00851-610.1038/s41596-023-00851-6 ↗
11. 10.1002/advs.20220113310.1002/advs.202201133 ↗
12. 10.1021/acs.chemrev.2c0031610.1021/acs.chemrev.2c00316 ↗
13. 10.1016/j.nantod.2020.10106310.1016/j.nantod.2020.101063 ↗
14. 10.1002/adma.20200798810.1002/adma.202007988 ↗
15. 10.1016/j.snb.2022.13278210.1016/j.snb.2022.132782 ↗
16. 10.1039/C3CS60265F10.1039/C3CS60265F ↗
17. 10.1021/acsnano.0c0612310.1021/acsnano.0c06123 ↗
18. 10.1039/C6CS00450D10.1039/C6CS00450D ↗
19. 10.1007/s12274-022-4562-510.1007/s12274-022-4562-5 ↗
20. 10.1016/j.apsusc.2020.14761510.1016/j.apsusc.2020.147615 ↗
21. 10.1016/j.snb.2023.13375910.1016/j.snb.2023.133759 ↗
22. 10.1021/acsanm.2c0535110.1021/acsanm.2c05351 ↗
23. 10.1021/acs.jafc.2c0814710.1021/acs.jafc.2c08147 ↗
24. 10.1021/nn500727w10.1021/nn500727w ↗
25. 10.1039/C9CP02015B10.1039/C9CP02015B ↗
26. 10.1002/adfm.20170183210.1002/adfm.201701832 ↗
27. 10.1002/open.20140200910.1002/open.201402009 ↗
28. 10.1039/D1AY02080C10.1039/D1AY02080C ↗
29. 10.1016/j.foodchem.2021.13127610.1016/j.foodchem.2021.131276 ↗
30. 10.1007/s12274-021-3740-110.1007/s12274-021-3740-1 ↗
31. 10.1039/C5TC02779A10.1039/C5TC02779A ↗
32. 10.1021/acs.analchem.9b0127210.1021/acs.analchem.9b01272 ↗
33. 10.1039/C9CP00138G10.1039/C9CP00138G ↗
34. 10.1021/acs.langmuir.0c0007910.1021/acs.langmuir.0c00079 ↗
35. 10.1039/D3TC01796F10.1039/D3TC01796F ↗
36. 10.1016/j.snb.2023.13393610.1016/j.snb.2023.133936 ↗
37. 10.1515/nanoph-2021-030110.1515/nanoph-2021-0301 ↗
38. 10.1021/acssuschemeng.2c0309610.1021/acssuschemeng.2c03096 ↗
39. 10.1002/adma.20120428310.1002/adma.201204283 ↗
40. 10.1021/acsnano.2c0515010.1021/acsnano.2c05150 ↗
41. 10.1088/1361-6528/abd1aa10.1088/1361-6528/abd1aa ↗
42. 10.1002/admi.20150003110.1002/admi.201500031 ↗
43. 10.1039/D0NR08803J10.1039/D0NR08803J ↗
44. 10.1021/acs.jpclett.0c0111610.1021/acs.jpclett.0c01116 ↗
45. 10.1364/ao.37.00527110.1364/ao.37.005271 ↗