journal article·2023-06Open access

A Fully Integrated Miniaturized Optical Biosensor for Fast and Multiplexing Plasmonic Detection of High‐ and Low‐Molecular‐Weight Analytes

Margherita Bolognesi, Mario Prosa, Michael Toerker, Laura Lopez Sanchez, Martin Wieczorek, Caterina Giacomelli, Emilia Benvenuti, Paola Pellacani, Alexander Elferink, Andreas Morschhauser, Laura Sola, Francesco Damin, Marcella Chiari, Mark Whatton, Etienne Haenni, David Kallweit, Franco Marabelli, Jeroen Peters, Stefano Toffanin

Advanced Materials· Volume 35· Issue 26

Abstract

Abstract Optical biosensors based on plasmonic sensing schemes combine high sensitivity and selectivity with label‐free detection. However, the use of bulky optical components is still hampering the possibility of obtaining miniaturized systems required for analysis in real settings. Here, a fully miniaturized optical biosensor prototype based on plasmonic detection is demonstrated, which enables fast and multiplex sensing of analytes with high‐ and low molecular weight (80 000 and 582 Da) as quality and safety parameters for milk: a protein (lactoferrin) and an antibiotic (streptomycin). The optical sensor is based on the smart integration of: i) miniaturized organic optoelectronic devices used as light‐emitting and light‐sensing elements and ii) a functionalized nanostructured plasmonic grating for highly sensitive and specific localized surface plasmon resonance (SPR) detection. The sensor provides quantitative and linear response reaching a limit of detection of 10−4 refractive index units once it is calibrated by standard solutions. Analyte‐specific and rapid (15 min long) immunoassay‐based detection is demonstrated for both targets. By using a custom algorithm based on principal‐component analysis, a linear dose–response curve is constructed which correlates with a limit of detection (LOD) as low as 3.7 µg mL−1 for lactoferrin, thus assessing that the miniaturized optical biosensor is well‐aligned with the chosen reference benchtop SPR method.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced biosensing and bioanalysis techniques · Biosensors and Analytical Detection · Advanced Biosensing Techniques and Applications

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

Affiliations

Institute of Nanostructured Materials (ISMN) – National Research Council (CNR) Via P. Gobetti 101 Bologna 40129 Italy

Fraunhofer FEP Winterbergstraße 28 01277 Dresden Germany

Plasmore s.r.l Viale Vittorio Emanuele II 4 Pavia 27100 Italy

KODE s.r.l. Lungarno Galileo Galilei 1 Pisa 56100 Italy

Wageningen Food Safety Research – Wageningen University and Research Akkermaalsbos 2 Wageningen 6708 WB The Netherlands

Fraunhofer ENAS Technologie – Campus 3 09126 Chemnitz Germany

SCITEC Istituto di Scienze e Tecnologie Chimiche “G. Natta” – National Research Council (CNR) Via Mario Bianco 9 Milano 20131 Italy

QuadraChem Laboratories Ltd (QCL) Riverside – Forest Row Business Park East Sussex RH18 5DW UK

CSEM Center Muttenz Tramstrasse 99 Muttenz CH‐4132 Switzerland

Physics Department – University of Pavia Via A. Bassi 6 Pavia 27100 Italy

References (52)

Show source-provided references
1. 10.1016/j.cofs.2020.04.00810.1016/j.cofs.2020.04.008 ↗
2. 10.3390/s2115518510.3390/s21155185 ↗
3. 10.1007/s00216-018-1361-710.1007/s00216-018-1361-7 ↗
4. 10.1016/j.trac.2019.11570110.1016/j.trac.2019.115701 ↗
5. Thomas S. · 2021
6. 10.3390/nano1003048010.3390/nano10030480 ↗
7. 10.1515/nanoph-2022-019110.1515/nanoph-2022-0191 ↗
8. 10.1021/cr300337x10.1021/cr300337x ↗
9. 10.3390/bios1207054310.3390/bios12070543 ↗
10. 10.1002/admi.20210113310.1002/admi.202101133 ↗
11. 10.1016/j.bios.2019.11169310.1016/j.bios.2019.111693 ↗
12. 10.3390/mi1207082610.3390/mi12070826 ↗
13. 10.3390/s1919428710.3390/s19194287 ↗
14. 10.1016/j.bios.2014.02.04910.1016/j.bios.2014.02.049 ↗
15. De R. · 2020
16. 10.1126/science.aba262410.1126/science.aba2624 ↗
17. 10.1038/s41467-022-29759-710.1038/s41467-022-29759-7 ↗
18. 10.1038/nmat275110.1038/nmat2751 ↗
19. 10.1002/aenm.20170057510.1002/aenm.201700575 ↗
20. 10.1039/C6EE03599J10.1039/C6EE03599J ↗
21. 10.1021/acsami.8b0646610.1021/acsami.8b06466 ↗
22. 10.1002/adfm.20210492710.1002/adfm.202104927 ↗
23. 10.1007/s11468-013-9499-910.1007/s11468-013-9499-9 ↗
24. 10.1117/1.JBO.19.1.01700610.1117/1.JBO.19.1.017006 ↗
25. 10.1016/j.bios.2005.04.01110.1016/j.bios.2005.04.011 ↗
26. 10.1016/j.bios.2008.10.01310.1016/j.bios.2008.10.013 ↗
27. 10.1111/j.1745-4530.2006.00071.x10.1111/j.1745-4530.2006.00071.x ↗
28. 10.3389/fimmu.2020.0122110.3389/fimmu.2020.01221 ↗
29. 10.1139/bcb-2016-004410.1139/bcb-2016-0044 ↗
30. 10.1016/j.foodchem.2020.12782010.1016/j.foodchem.2020.127820 ↗
31. 10.1039/C4AY00321G10.1039/C4AY00321G ↗
32. 10.1136/vr.10092410.1136/vr.100924 ↗
33. 10.3389/fmicb.2018.0292810.3389/fmicb.2018.02928 ↗
34. 10.1080/0954010022013763710.1080/09540100220137637 ↗
35. 10.1007/s10544-013-9810-810.1007/s10544-013-9810-8 ↗
36. 10.1002/wics.10110.1002/wics.101 ↗
37. 10.1177/147592171038897210.1177/1475921710388972 ↗
38. 10.1023/A:101093340432410.1023/A:1010933404324 ↗
39. M.Jahn Artificial Neural Network Regression Models: Predicting GDP Growth https://www.econstor.eu/handle/10419/182108(accessed: September2022).
40. 10.18637/jss.v015.i0910.18637/jss.v015.i09 ↗
41. K.Khadiev I.Mannapov L.Safina arXiv:1907.06840 2019.
42. 10.18637/jss.v028.i0510.18637/jss.v028.i05 ↗
43. 10.1016/j.neunet.2014.09.00310.1016/j.neunet.2014.09.003 ↗
44. Zhang Z. · Ann. Transl. Med. · 2016 · 4 · 1
45. 10.1109/JPHOT.2020.302485610.1109/JPHOT.2020.3024856 ↗
46. Miyazaki C. M. · 2017
47. 10.1016/j.bios.2014.03.06310.1016/j.bios.2014.03.063 ↗
48. 10.1021/acs.jafc.6b0410610.1021/acs.jafc.6b04106 ↗
49. 10.1080/14737159.2021.196050710.1080/14737159.2021.1960507 ↗
50. T.Wenzl J.Haedrich A.Schaechtele P.Robouch J.Stroka Guidance Document on the Estimation of LOD and LOQ for Measurements in the Field of Contaminants in Feed and Food; EUR 28099 Publications Office of the European Union Luxembourg2016 https://doi.org/10.2787/8931.
51. Desimoni E. · Pharm. Anal. Acta · 2015 · 6 · 355
52. 10.4103/2229-5186.7934510.4103/2229-5186.79345 ↗