Visual Detection Strategy Based on Size-Gradient Coffee Rings on a Superslippery Platform
Source abstract
Abstract
Superwetting surfaces have attracted considerable attention in analytical fields due to their unique ability to concentrate target analytes within evaporating droplets, characterized by a constant contact angle and a centripetal mobile contact line. While previous studies have underscored the resistance of superslippery surfaces to the coffee-ring effect, our research presents a novel strategy that dynamically introduces the coffee-ring effect on such surfaces for visual detection. By harnessing the synergistic interaction between the coffee-ring effect and the mobile contact line properties of the superslippery surface, our work leverages affinity recognition between the functionalized surface and the target analyte within the droplet to dynamically create defects for contact line pinning, thereby generating coffee rings on the superslippery surface and enabling qualitative visual detection based on their distinct patterns. Moreover, we establish a linear relationship where the coffee-ring’s diameter decreased with increasing initial target concentration, facilitating quantitative detection. As a proof of concept, we demonstrate the naked-eye detection capability of our strategy using streptavidin dilutions ranging from 10 –7 to 10 –11 M and Escherichia coli concentrations from 10 8 to 10 4 CFU/mL, visualized with suspended micro-/nanoparticles. To the best of our knowledge, it is the first time that a superslippery surface is utilized to dynamically create pinning defects for coffee ring formation, enabling both qualitative and quantitative detection. This strategy offers a user-friendly and easily interpretable method, making it particularly advantageous for point-of-care testing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Nanomaterials and Printing Technologies · Color Science and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine
Linyi University
. Linyi People’s Hospital
Funding
Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China · National Natural Science Foundation of China · Natural Science Foundation of Shandong Province
References (46)
Show source-provided references
1. 10.1016/j.nantod.2023.10200810.1016/j.nantod.2023.102008 ↗
2. 10.1002/smll.20220646310.1002/smll.202206463 ↗
3. 10.1002/adma.20110461810.1002/adma.201104618 ↗
4. 10.1002/adfm.20100273310.1002/adfm.201002733 ↗
5. 10.1039/C8CS00915E10.1039/C8CS00915E ↗
6. Sherin T. · 2024 · 10 · s40964
7. 10.1002/dro2.10310.1002/dro2.103 ↗
8. 10.1016/j.jmst.2023.10.04210.1016/j.jmst.2023.10.042 ↗
9. 10.1016/j.cis.2023.10294810.1016/j.cis.2023.102948 ↗
10. 10.1016/j.porgcoat.2024.10846010.1016/j.porgcoat.2024.108460 ↗
11. 10.1038/s41893-019-0421-010.1038/s41893-019-0421-0 ↗
12. 10.1021/acsami.4c0187410.1021/acsami.4c01874 ↗
13. 10.1021/acsami.0c0674310.1021/acsami.0c06743 ↗
14. 10.1021/acsami.0c2207310.1021/acsami.0c22073 ↗
15. 10.1038/nature1044710.1038/nature10447 ↗
16. 10.1002/anie.20150938510.1002/anie.201509385 ↗
17. 10.1016/j.cej.2020.12706410.1016/j.cej.2020.127064 ↗
18. 10.1021/acsami.8b2035210.1021/acsami.8b20352 ↗
19. 10.1021/am301238810.1021/am3012388 ↗
20. 10.1007/s11998-017-0011-x10.1007/s11998-017-0011-x ↗
21. 10.1016/j.cej.2019.04.21510.1016/j.cej.2019.04.215 ↗
22. 10.1021/acsami.5b0845210.1021/acsami.5b08452 ↗
23. 10.1016/j.cej.2018.03.13710.1016/j.cej.2018.03.137 ↗
24. 10.1021/acsami.3c1411210.1021/acsami.3c14112 ↗
25. 10.1016/j.bios.2020.11256610.1016/j.bios.2020.112566 ↗
26. 10.1016/j.cej.2024.15263610.1016/j.cej.2024.152636 ↗
27. 10.1002/adfm.20241644710.1002/adfm.202416447 ↗
28. 10.1021/acsami.6b1614110.1021/acsami.6b16141 ↗
29. 10.1038/s41467-020-16329-y10.1038/s41467-020-16329-y ↗
30. 10.1002/adma.20150625110.1002/adma.201506251 ↗
31. 10.1021/acsanm.0c0074510.1021/acsanm.0c00745 ↗
32. 10.1038/nphoton.2011.22210.1038/nphoton.2011.222 ↗
33. 10.1002/adfm.20180044810.1002/adfm.201800448 ↗
34. 10.1002/adma.20150211510.1002/adma.201502115 ↗
35. 10.1002/anie.20200383910.1002/anie.202003839 ↗
36. 10.1038/nature1044710.1038/nature10447 ↗
37. 10.1002/cphc.20190009810.1002/cphc.201900098 ↗
38. 10.1126/science.aai897710.1126/science.aai8977 ↗
39. 10.1021/acsami.6b1614110.1021/acsami.6b16141 ↗
40. 10.1038/ncomms274610.1038/ncomms2746 ↗
41. 10.1002/adma.20130541610.1002/adma.201305416 ↗
42. 10.1038/3982710.1038/39827 ↗
43. 10.1364/OL.42.00393610.1364/OL.42.003936 ↗
44. 10.2147/IJN.S18437910.2147/IJN.S184379 ↗
45. 10.1007/s41048-018-0079-610.1007/s41048-018-0079-6 ↗
46. 10.1021/acssensors.9b0260010.1021/acssensors.9b02600 ↗