journal article·2024-12-27

Advanced Detection of SARS-CoV-2 and Omicron Variants via MXene-Graphene Hybrid Biosensors Utilizing Nucleic Acid Probes

Jiaoli Li, Yuwei Zhang, Congjie Wei, Yanxiao Li, Zhekun Peng, Hsin-Yin Chuang, Logan Pearce, Adrianus Boon, Yue-Wern Huang, DongHyun Kim, Risheng Wang, Chenglin Wu

ACS Applied Nano Materials· Volume 7· Issue 24
Publisher page

Abstract

Low-cost biosensors that can rapidly and widely detect viruses are critical for faster diagnosis and treatment decision-making, especially for infections. The commonly used field-effect transistor is sensitive to the biomarker’s detection but struggles with precise detection, particularly of nontargets such as ions and proteins. To overcome this limitation, we developed a field-effect transistor biosensor design based on MXene-graphene materials to increase the accuracy and sensitivity of virus detection. Based on the hybridization process between two complementary DNA strands, single-stranded nucleic acids were immobilized on the sensing surface via 3-aminopropyltriethoxysilane and glutaraldehyde to capture the nucleic acids of the target virus. The addition of the MXene layer provides a reduced system capacitance and tuned bandgap compared to that of stand-alone graphene. This tuning can significantly enhance the sensitivity of the developed platform. The SARS-CoV-2 and its Omicron variant were used to validate the developed biosensor. The results showed high accuracy with detection limits as low as 1 × 10 –21 and 1 × 10 –22 mol/L (60.2 and 6.02 copies/L equivalently), for SARS-CoV-2 and its Omicron variant, respectively. Twenty-four clinical tests were also conducted using the developed ssDNA-MXene-graphene biosensors with patients’ nasopharyngeal swab samples. The biosensor’s results closely matched those of the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) detection but with a significantly shorter detection time, demonstrating the sensors’ real-time, in situ, practical application. This result also demonstrates the promising future of MXene-based biosensors for virus detection using nucleic acid probes.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: MXene and MAX Phase Materials · Advanced biosensing and bioanalysis techniques · Energy Harvesting in Wireless Networks

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

Affiliations

Zachry Department of Civil and Environmental Engineering

Texas A&M University

Department of Chemistry

Missouri University of Science and Technology

National Energy Technology Laboratory (NETL), NETL Support Contractor

Department of Electrical and Computer Engineering

Department of Biological Sciences

Division of Infectious Diseases, Department of Internal Medicine

School of Medicine, Washington University in St. Louis

Funding

Division of Civil, Mechanical and Manufacturing Innovation · Division of Chemical, Bioengineering, Environmental, and Transport Systems

References (56)

Show source-provided references
1. 10.1021/acsnano.0c0282310.1021/acsnano.0c02823 ↗
2. 10.1017/S095026882000140510.1017/S0950268820001405 ↗
3. 10.1016/S1473-3099(20)30195-X10.1016/S1473-3099(20)30195-X ↗
4. 10.22146/jp.6343010.22146/jp.63430 ↗
5. 10.1136/bmj.n35910.1136/bmj.n359 ↗
6. 10.1126/science.abm342510.1126/science.abm3425 ↗
7. 10.1039/D3RA02560H10.1039/D3RA02560H ↗
8. 10.1039/D3NR04583H10.1039/D3NR04583H ↗
9. 10.1007/s00216-020-02889-x10.1007/s00216-020-02889-x ↗
10. 10.1016/j.measurement.2022.11220210.1016/j.measurement.2022.112202 ↗
11. 10.1021/acssensors.8b0034410.1021/acssensors.8b00344 ↗
12. 10.1038/ncomms586610.1038/ncomms5866 ↗
13. 10.1016/j.apsusc.2021.14916910.1016/j.apsusc.2021.149169 ↗
14. 10.1021/acs.nanolett.8b0381810.1021/acs.nanolett.8b03818 ↗
15. 10.1021/acs.analchem.1c0109410.1021/acs.analchem.1c01094 ↗
16. 10.1039/C9NR08542D10.1039/C9NR08542D ↗
17. 10.1039/C6NR01333C10.1039/C6NR01333C ↗
18. 10.1002/adma.20150465710.1002/adma.201504657 ↗
19. 10.1016/j.jelechem.2020.11466910.1016/j.jelechem.2020.114669 ↗
20. 10.1016/j.aca.2022.33965310.1016/j.aca.2022.339653 ↗
21. 10.1007/s00604-019-4000-x10.1007/s00604-019-4000-x ↗
22. 10.1016/j.bios.2021.11360010.1016/j.bios.2021.113600 ↗
23. 10.1016/j.sna.2022.11374010.1016/j.sna.2022.113740 ↗
24. 10.1021/acsomega.0c0542110.1021/acsomega.0c05421 ↗
25. 10.1016/j.apsusc.2020.14691510.1016/j.apsusc.2020.146915 ↗
26. 10.1016/j.bios.2022.11403910.1016/j.bios.2022.114039 ↗
27. 10.1016/j.proeng.2014.11.64710.1016/j.proeng.2014.11.647 ↗
28. 10.1016/j.apsusc.2014.03.13010.1016/j.apsusc.2014.03.130 ↗
29. 10.1016/j.talanta.2019.12030510.1016/j.talanta.2019.120305 ↗
30. 10.1109/TMAG.2012.222432710.1109/TMAG.2012.2224327 ↗
31. 10.1016/j.snb.2013.10.11010.1016/j.snb.2013.10.110 ↗
32. 10.1088/0957-4484/23/35/35570310.1088/0957-4484/23/35/355703 ↗
33. 10.1016/j.carbon.2015.11.02710.1016/j.carbon.2015.11.027 ↗
34. 10.1063/1.504776110.1063/1.5047761 ↗
35. 10.1021/nl061702a10.1021/nl061702a ↗
36. 10.1038/s41467-019-10982-810.1038/s41467-019-10982-8 ↗
37. 10.3390/ma1421629210.3390/ma14216292 ↗
38. 10.1038/s41598-019-45664-410.1038/s41598-019-45664-4 ↗
39. 10.1016/j.flatc.2019.10012810.1016/j.flatc.2019.100128 ↗
40. 10.1038/srep0440710.1038/srep04407 ↗
41. 10.1021/jp004368u10.1021/jp004368u ↗
42. 10.1038/s41467-020-15330-910.1038/s41467-020-15330-9 ↗
43. 10.1103/PhysRevB.99.08542910.1103/PhysRevB.99.085429 ↗
44. 10.1039/C7NR00088J10.1039/C7NR00088J ↗
45. 10.1039/C7TA05721K10.1039/C7TA05721K ↗
46. 10.1002/aelm.20160025510.1002/aelm.201600255 ↗
47. 10.1021/acsami.8b1072910.1021/acsami.8b10729 ↗
48. 10.1021/acsnano.0c0339110.1021/acsnano.0c03391 ↗
49. 10.1002/admt.20220178710.1002/admt.202201787 ↗
50. 10.1016/j.talanta.2021.12320310.1016/j.talanta.2021.123203 ↗
51. 10.1016/j.snb.2022.13221710.1016/j.snb.2022.132217 ↗
52. 10.2147/IJN.S19873410.2147/IJN.S198734 ↗
53. 10.1016/j.bios.2022.11409810.1016/j.bios.2022.114098 ↗
54. 10.1088/1367-2630/ac2e8210.1088/1367-2630/ac2e82 ↗
55. 10.1007/s00210-012-0786-310.1007/s00210-012-0786-3 ↗
56. Centers for Disease Control and Prevention · 202