journal article·2023Open access

Electrochemically-gated graphene broadband microwave waveguides for ultrasensitive biosensing

Patrik Gubeljak, Tianhui Xu, Lorenzo Pedrazzetti, Oliver J. Burton, Luca Magagnin, Stephan Hofmann, George G. Malliaras, Antonio Lombardo

Nanoscale· Volume 15· Issue 37

Abstract

Identification of non-amplified DNA sequences and single-base mutations is essential for molecular biology and genetic diagnostics. This paper reports a novel sensor consisting of electrochemically-gated graphene coplanar waveguides coupled with a microfluidic channel. Upon exposure to analytes, propagation of electromagnetic waves in the waveguides is modified as a result of interactions with the fringing field and modulation of graphene dynamic conductivity resulting from electrostatic gating. Probe DNA sequences are immobilised on the graphene surface, and the sensor is exposed to DNA sequences which either perfectly match the probe, contain a single-base mismatch or are unrelated. By monitoring the scattering parameters at frequencies between 50 MHz and 50 GHz, unambiguous and reproducible discrimination of the different strands is achieved at concentrations as low as one attomole per litre (1 aM). By controlling and synchronising frequency sweeps, electrochemical gating, and liquid flow in the microfluidic channel, the sensor generates multidimensional datasets. Advanced data analysis techniques are utilised to take full advantage of the richness of the dataset. A classification accuracy >97% between all three sequences is achieved using different Machine Learning models, even in the presence of simulated noise and low signal-to-noise ratios. The sensor exceeds state-of-the-art sensitivity of field-effect transistors and microwave sensors for the identification of single-base mismatches.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced biosensing and bioanalysis techniques · Plasmonic and Surface Plasmon Research · Microwave and Dielectric Measurement Techniques

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

Affiliations

Cambridge Graphene Centre, Department of Engineering, University of Cambridge, UK

Department of Engineering, University of Cambridge, UK

Department of Electronic and Electrical Engineering, University College London, London, UK

Dipartimento di Chimica, Materiali e Ingegneria Chimica “Giulio Natta”, Politecnico di Milano, Italy

London Centre for Nanotechnology, University College London, UK

Funding

Engineering and Physical Sciences Research Council

References (57)

Show source-provided references
1. Sadighbayan · TrAC, Trends Anal. Chem. · 2020 · 133 · 11606710.1016/j.trac.2020.116067 ↗
2. Bitounis · Adv. Mater. · 2013 · 25 · 225810.1002/adma.201203700 ↗
3. Prattis · Trends Biotechnol. · 2021 · 39 · 106510.1016/j.tibtech.2021.01.005 ↗
4. Schedin · Nat. Mater. · 2007 · 6 · 65210.1038/nmat1967 ↗
5. Li · Sens. Actuators, B · 2017 · 253 · 75910.1016/j.snb.2017.06.129 ↗
6. L.Fritea , M.Tertis , R.Sandulescu and C.Cristea , Enzyme Nanoarchitectures: Enzymes Armored with Graphene , Academic Press , 2018 , vol. 609 , pp. 293–33310.1016/bs.mie.2018.05.010 ↗
7. Zhang · Sci. Rep. · 2015 · 5 · 831110.1038/srep08311 ↗
8. Bharti · Anal. Chim. Acta · 2020 · 1097 · 18610.1016/j.aca.2019.11.005 ↗
9. Seo · ACS Nano · 2020 · 14 · 513510.1021/acsnano.0c02823 ↗
10. Mohanty · Nano Lett. · 2008 · 8 · 446910.1021/nl802412n ↗
11. Novoselov · Science · 2004 · 306 · 66610.1126/science.1102896 ↗
12. Xu · Nat. Commun. · 2017 · 8 · 1490210.1038/ncomms14902 ↗
13. Campos · ACS Sens. · 2019 · 4 · 28610.1021/acssensors.8b00344 ↗
14. Das · Nat. Nanotechnol. · 2008 · 3 · 21010.1038/nnano.2008.67 ↗
15. Fernández-Rossier · Phys. Rev. B: Condens. Matter Mater. Phys. · 2007 · 75 · 20544110.1103/PhysRevB.75.205441 ↗
16. Hwang · Nat. Commun. · 2020 · 11 · 154310.1038/s41467-020-15330-9 ↗
17. Hwang · Proc. Natl. Acad. Sci. U. S. A. · 2016 · 113 · 708810.1073/pnas.1603753113 ↗
18. Nakatsuka · Science · 2018 · 362 · 31910.1126/science.aao6750 ↗
19. Artis · IEEE Microw. Mag. · 2015 · 16 · 8710.1109/MMM.2015.2393997 ↗
20. Guarin · IEEE Microw. Mag. · 2015 · 16 · 7110.1109/MMM.2015.2394024 ↗
21. Grenier · IEEE Trans. Microwave Theory Tech. · 2013 · 61 · 202310.1109/TMTT.2013.2255885 ↗
22. Schmalz · IEEE Trans. Microwave Theory Tech. · 2017 · 65 · 180710.1109/TMTT.2017.2650915 ↗
23. Jain · Sci. Rep. · 2021 · 11 · 1477510.1038/s41598-021-94139-y ↗
24. Tamra · IEEE J. Electromagn., RF Microw. Med. Biol. · 2019 · 3 · 22410.1109/JERM.2019.2898318 ↗
25. Yang · Biosens. Bioelectron. · 2012 · 31 · 34910.1016/j.bios.2011.10.044 ↗
26. Kim · Biosens. Bioelectron. · 2013 · 50 · 36210.1016/j.bios.2013.06.034 ↗
27. Zhang · Adv. Mater. · 2022 · 34 · 210666610.1002/adma.202106666 ↗
28. Schwierz · Nat. Nanotechnol. · 2010 · 5 · 48710.1038/nnano.2010.89 ↗
29. P.Gubeljak , L.Pedrazzetti , O. J.Burton , L.Magagnin , S.Hofmann , G. G.Malliaras and A.Lombardo , 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA) , 2022 , pp. 1–6
30. Awan · 2D Mater. · 2016 · 3 · 01501010.1088/2053-1583/3/1/015010 ↗
31. Allen · SID Symp. Dig. Tech. Pap. · 2021 · 52 · 98710.1002/sdtp.14855 ↗
32. Graphene on the pilot line · Nat. Mater. · 2021 · 20 · 57310.1038/s41563-021-00999-0 ↗
33. Banerjee · Sensors · 2021 · 21 · 125310.3390/s21041253 ↗
34. Irkham · Sensors · 2023 · 23 · 224010.3390/s23042240 ↗
35. Liu · Briefings Bioinf. · 2017 · 20 · 128010.1093/bib/bbx165 ↗
36. Soares · Mater. Chem. Front. · 2021 · 5 · 565810.1039/D1QM00665G ↗
37. Bamatraf · Prog. Electromagn. Res. Lett. · 2022 · 102 · 12710.2528/PIERL21110905 ↗
38. Turgul · Sens. Actuators, A · 2018 · 277 · 6510.1016/j.sna.2018.03.041 ↗
39. Tang · Biomed. Opt. Express · 2020 · 11 · 536210.1364/BOE.400487 ↗
40. Sherbet · Biophys. Struct. Mech. · 1983 · 10 · 12110.1007/BF00537554 ↗
41. Lin · Adv. Funct. Mater. · 2013 · 23 · 230110.1002/adfm.201202672 ↗
42. Wong · Anal. Chem. · 2006 · 78 · 213810.1021/ac0509096 ↗
43. Chen · Biosens. Bioelectron. · 2020 · 156 · 11212810.1016/j.bios.2020.112128 ↗
44. Guo · J. Nanosci. Nanotechnol. · 2011 · 11 · 525810.1166/jnn.2011.3885 ↗
45. Dong · Adv. Mater. · 2010 · 22 · 164910.1002/adma.200903645 ↗
46. Cai · ACS Nano · 2014 · 8 · 263210.1021/nn4063424 ↗
47. Osman · IEEE Instrum. Meas. Mag. · 2021 · 24 · 2310.1109/MIM.2021.9436102 ↗
48. Shirmohammadi · IEEE Instrum. Meas. Mag. · 2021 · 24 · 8410.1109/MIM.2021.9400955 ↗
49. A.Niculescu-Mizil and R.Caruana
50. L.Mi , H.Wang , Y.Tian , H.He and N.Shavit , Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate , 2019 , https://arxiv.org/abs/1910.04858
51. J.Gawlikowski , C. R. N.Tassi , M.Ali , J.Lee , M.Humt , J.Feng , A.Kruspe , R.Triebel , P.Jung and R.Roscher
52. B.Lakshminarayanan , A.Pritzel and C.Blundell
53. Tymoczko · ACS Appl. Mater. Interfaces · 2014 · 6 · 2185110.1021/am5027902 ↗
54. Burton · J. Phys. Chem. C · 2019 · 123 · 1625710.1021/acs.jpcc.9b03808 ↗
55. FormFactor , Infinity Probe®Mechanical Layout Rules , 2022
56. Cusati · Sci. Rep. · 2017 · 7 · 510910.1038/s41598-017-05069-7 ↗
57. Anzi · 2D Mater. · 2018 · 5 · 02501410.1088/2053-1583/aaab96 ↗