journal article·2025-12-10Open access

Label-Free Detection of Biomolecules by Single-Defect-Spectroscopy at the Aqueous Hexagonal Boron Nitride Interface

Miao Zhang, Cristina Izquierdo Lozano, Stijn van Veen, Lorenzo Albertazzi

ACS Applied Materials & Interfaces· Volume 17· Issue 49

Abstract

Label-free single-molecule detection is essential for studying biomolecules in their native state, yet having materials with intrinsic molecular specificity to identify a broad range of molecules without complex functionalization remains challenging. We present a method that utilizes emissions from selectively activated defects at the aqueous hexagonal boron nitride (hBN) interface to detect biomolecules including lipids, nucleotides, and amino acids. Using spectrally resolved single-molecule localization microscopy, we harvest spatial, spectral, and temporal data of individual events to uncover optical fingerprints of biomolecules. The defect emissions show chemical sensitivity to biomolecules that interact transiently with hBN surface defects. We demonstrate differences in defect emissions activated by various lipids and amino acids. This approach allows us to probe fine chemical differences, as small as the single deprotonation of an amino acid's side chain, and detect dynamics of biomolecules at the interface with exceptional detail. As a proof-of-concept, we demonstrate the classification of five different amino acids at the single-molecule level by exploiting a Random Forest model for analyzing single-defect spectra. Our findings shed light on hBN-biomolecule interactions and highlight the potential of hBN for label-free single-molecule detection with chemical sensitivity.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Plasmonic and Surface Plasmon Research · Nanopore and Nanochannel Transport Studies

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

Affiliations

Department of Biomedical Engineering, and Institute for Complex Molecular Systems

Eindhoven University of Technology

Funding

H2020 Marie Sklodowska-Curie Actions

References (51)

Show source-provided references
1. 10.1016/j.ijbiomac.2022.04.03810.1016/j.ijbiomac.2022.04.038 ↗
2. 10.3389/fmolb.2022.96743410.3389/fmolb.2022.967434 ↗
3. 10.1146/annurev.biochem.77.060706.09310210.1146/annurev.biochem.77.060706.093102 ↗
4. 10.1038/s41557-023-01322-x10.1038/s41557-023-01322-x ↗
5. 10.1088/1361-6633/aa8a0210.1088/1361-6633/aa8a02 ↗
6. 10.1021/acs.chemrev.1c0029010.1021/acs.chemrev.1c00290 ↗
7. 10.1126/science.aar583910.1126/science.aar5839 ↗
8. 10.1126/science.114500210.1126/science.1145002 ↗
9. 10.1021/acssensors.7b0038210.1021/acssensors.7b00382 ↗
10. 10.1038/s41563-020-0616-910.1038/s41563-020-0616-9 ↗
11. 10.1002/aelm.20200117710.1002/aelm.202001177 ↗
12. 10.1038/s41563-020-00850-y10.1038/s41563-020-00850-y ↗
13. 10.1126/sciadv.abg856810.1126/sciadv.abg8568 ↗
14. 10.1038/s41563-023-01658-210.1038/s41563-023-01658-2 ↗
15. 10.1038/s41565-020-0695-410.1038/s41565-020-0695-4 ↗
16. 10.1073/pnas.060964310410.1073/pnas.0609643104 ↗
17. 10.1021/jacs.4c0356210.1021/jacs.4c03562 ↗
18. 10.1038/nmeth.352810.1038/nmeth.3528 ↗
19. 10.1364/AO.58.00224810.1364/AO.58.002248 ↗
20. 10.1021/acsami.6b0283710.1021/acsami.6b02837 ↗
21. 10.1039/C7NR05639G10.1039/C7NR05639G ↗
22. 10.1016/0001-8686(91)80037-K10.1016/0001-8686(91)80037-K ↗
23. 10.1021/acsomega.1c0466110.1021/acsomega.1c04661 ↗
24. 10.1140/epje/i2008-10407-310.1140/epje/i2008-10407-3 ↗
25. 10.1371/journal.pone.015845710.1371/journal.pone.0158457 ↗
26. Möhwald H. · 1995 · 161
27. 10.1126/sciadv.abe713810.1126/sciadv.abe7138 ↗
28. 10.1046/j.0022-2720.2001.00982.x10.1046/j.0022-2720.2001.00982.x ↗
29. 10.1021/ja100351w10.1021/ja100351w ↗
30. 10.1016/j.bbamem.2022.18386910.1016/j.bbamem.2022.183869 ↗
31. 10.1042/bse057003310.1042/bse0570033 ↗
32. 10.1016/S0006-3495(98)77580-010.1016/S0006-3495(98)77580-0 ↗
33. 10.1016/j.bbamem.2016.08.00510.1016/j.bbamem.2016.08.005 ↗
34. 10.1021/ja304792p10.1021/ja304792p ↗
35. 10.1085/jgp.20070984810.1085/jgp.200709848 ↗
36. 10.1038/s41598-022-13738-510.1038/s41598-022-13738-5 ↗
37. 10.1021/acs.jpcc.0c0854510.1021/acs.jpcc.0c08545 ↗
38. 10.1039/C8CP05093G10.1039/C8CP05093G ↗
39. 10.1021/acs.langmuir.9b0312110.1021/acs.langmuir.9b03121 ↗
40. 10.1186/1472-6769-5-210.1186/1472-6769-5-2 ↗
41. 10.1371/journal.pone.008063510.1371/journal.pone.0080635 ↗
42. 10.1103/PhysRevApplied.11.04100110.1103/PhysRevApplied.11.041001 ↗
43. 10.1021/acs.nanolett.6b0488910.1021/acs.nanolett.6b04889 ↗
44. 10.1021/acsanm.0c0304710.1021/acsanm.0c03047 ↗
45. van der Maaten L. · J. Mach. Learn. Res. · 2008 · 9 · 2579
46. 10.1023/A:101093340432410.1023/A:1010933404324 ↗
47. More, A. S.; Rana, D. P. In Review of Random Forest Classification Techniques to Resolve Data Imbalance , 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), 2017; pp 72–78.10.1109/ICISIM.2017.8122151 ↗
48. 10.1093/bioinformatics/btaa63510.1093/bioinformatics/btaa635 ↗
49. 10.1093/bioinformatics/btu20210.1093/bioinformatics/btu202 ↗
50. RandomForestClassifier. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html.
51. RandomizedSearchCV. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html#randomizedsearchcv.