journal article·2019-10-23

Manipulating “Hot Spots” from Nanometer to Angstrom: Toward Understanding Integrated Contributions of Molecule Number and Gap Size for Ultrasensitive Surface-Enhanced Raman Scattering Detection

Hui Lu, Li Zhu, Yu Lu, Jingting Su, Ruohu Zhang, Yiping Cui

ACS Applied Materials & Interfaces· Volume 11· Issue 42
Publisher page

Abstract

Narrower gaps between metal nanoparticles (so-called "hot spots") in surface-enhanced Raman scattering (SERS) substrates contribute to stronger electromagnetic (EM) enhancement; however, the accompanying steric effect hinders analyte molecules entering hot spots to access the benefit. To comprehensively understand integrated contributions of the gap size and molecule number accommodated in hot spots and then optimize design of SERS substrates, the thermal shrinking method was employed to manipulate hot spots and the "hottest zone" was defined to evaluate the integrated contributions to SERS intensity of the two factors. In the conventional shrink-adsorption mode, the contributions of the molecule number and gap size are competitive when the gap width is comparable with the target molecule size, which leads to oscillating behavior of SERS intensity versus gap size, and it is analyte molecule size dependent. This result suggests that engineering hot spots should be target molecule directed to achieve ultrasensitive detection. In the proposed adsorption-shrink mode, the contributions of the molecule number and gap size are synergistic, which makes the detection ability of the adsorption-shrink mode attains a single-molecule (SM) level. Excellent performance of the adsorption-shrink SERS strategy benefits detection of trace level pollutants in complex environments. Detection ranges for contaminants with different metal affinity, such as thiram, malachite green (MG), and formaldehyde, are as low as parts per billion, even down to parts per trillion.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Advanced biosensing and bioanalysis techniques · Biosensors and Analytical Detection

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

Affiliations

Advanced Photonics Center, School of Electronic Science & Engineering, Southeast University, Nanjing 210096, Jiangsu, China

Funding

Ministry of Education of the People's Republic of China · Natural Science Foundation of Jiangsu Province · National Natural Science Foundation of China

References (61)

Show source-provided references
1. 10.1002/anie.20120574810.1002/anie.201205748 ↗
2. 10.1002/smll.20080033710.1002/smll.200800337 ↗
3. 10.1021/acs.chemrev.6b0055210.1021/acs.chemrev.6b00552 ↗
4. 10.1039/C7CC05979E10.1039/C7CC05979E ↗
5. 10.1021/acs.chemrev.7b0066810.1021/acs.chemrev.7b00668 ↗
6. 10.1126/science.275.5303.110210.1126/science.275.5303.1102 ↗
7. 10.1103/PhysRevLett.78.166710.1103/PhysRevLett.78.1667 ↗
8. 10.1021/ja506361d10.1021/ja506361d ↗
9. 10.1039/c3nr02924g10.1039/c3nr02924g ↗
10. 10.1016/j.jphotochemrev.2014.09.00110.1016/j.jphotochemrev.2014.09.001 ↗
11. 10.1021/acs.accounts.6b0040910.1021/acs.accounts.6b00409 ↗
12. 10.1016/j.trac.2015.08.01210.1016/j.trac.2015.08.012 ↗
13. 10.1021/acsami.8b1732510.1021/acsami.8b17325 ↗
14. 10.1021/acsami.7b1746110.1021/acsami.7b17461 ↗
15. 10.1002/smll.20180417710.1002/smll.201804177 ↗
16. 10.1002/advs.20190092510.1002/advs.201900925 ↗
17. 10.1039/C7CS00238F10.1039/C7CS00238F ↗
18. 10.1126/science.115949910.1126/science.1159499 ↗
19. 10.1039/C7CS00786H10.1039/C7CS00786H ↗
20. 10.1021/acsami.5b1139910.1021/acsami.5b11399 ↗
21. 10.1021/acsnano.6b0527610.1021/acsnano.6b05276 ↗
22. 10.1021/acs.analchem.6b0225610.1021/acs.analchem.6b02256 ↗
23. 10.1002/adom.20150050910.1002/adom.201500509 ↗
24. 10.1021/jacs.8b0150110.1021/jacs.8b01501 ↗
25. 10.1002/anie.20180174810.1002/anie.201801748 ↗
26. 10.1021/acs.analchem.7b0492910.1021/acs.analchem.7b04929 ↗
27. 10.1039/C7SC03536E10.1039/C7SC03536E ↗
28. 10.1002/adfm.20170481810.1002/adfm.201704818 ↗
29. 10.1021/acs.jafc.7b0307510.1021/acs.jafc.7b03075 ↗
30. 10.1002/smll.20180551610.1002/smll.201805516 ↗
31. 10.1038/nmat259610.1038/nmat2596 ↗
32. 10.1002/ppsc.20130018710.1002/ppsc.201300187 ↗
33. 10.1039/C8CS00787J10.1039/C8CS00787J ↗
34. 10.1021/nl102317210.1021/nl1023172 ↗
35. 10.1039/c3nr03021k10.1039/c3nr03021k ↗
36. 10.1126/science.aah524310.1126/science.aah5243 ↗
37. 10.1021/acs.nanolett.5b0367210.1021/acs.nanolett.5b03672 ↗
38. 10.1021/acsnano.6b0458410.1021/acsnano.6b04584 ↗
39. 10.1364/PRJ.6.00007710.1364/PRJ.6.000077 ↗
40. 10.1039/C6RA25173K10.1039/C6RA25173K ↗
41. 10.1002/adma.20090229410.1002/adma.200902294 ↗
42. 10.1038/natrevmats.2016.2110.1038/natrevmats.2016.21 ↗
43. 10.1021/ja501951v10.1021/ja501951v ↗
44. 10.1038/ncomms622810.1038/ncomms6228 ↗
45. 10.1021/nl803811g10.1021/nl803811g ↗
46. 10.1038/nature1165310.1038/nature11653 ↗
47. 10.1126/science.122482310.1126/science.1224823 ↗
48. 10.1038/ncomms1149510.1038/ncomms11495 ↗
49. 10.1021/acs.jpcc.6b0364410.1021/acs.jpcc.6b03644 ↗
50. 10.1021/jp068790810.1021/jp0687908 ↗
51. 10.1021/jp054732v10.1021/jp054732v ↗
52. 10.1002/adma.20170289310.1002/adma.201702893 ↗
53. 10.1016/j.trac.2016.06.01710.1016/j.trac.2016.06.017 ↗
54. 10.1007/s00604-013-1115-310.1007/s00604-013-1115-3 ↗
55. Federal Register. Environmental Protection Agency. 2015, 80, 16302–16306.
56. 10.1016/j.neurobiolaging.2009.07.01310.1016/j.neurobiolaging.2009.07.013 ↗
57. 10.1002/adma.20170227510.1002/adma.201702275 ↗
58. 10.1002/smll.20120057210.1002/smll.201200572 ↗
59. 10.1039/C7CC07951F10.1039/C7CC07951F ↗
60. 10.1038/s41570-018-0031-910.1038/s41570-018-0031-9 ↗
61. 10.1021/jz500334610.1021/jz5003346 ↗