journal article·2020-10-27Open access

New Frontiers for Selective Biosensing with Biomembrane-Based Organic Transistors

Claudia Lubrano, Giovanni Maria Matrone, Gennaro Iaconis, Francesca Santoro

ACS Nano· Volume 14· Issue 10

Abstract

Biosensing plays vital roles in multiple fields, including healthcare monitoring, drug screening, disease diagnosis, and environmental pollution control. In recent years, transistor-based devices have been considered to be valid platforms for fast, low-cost sensing of diverse analytes. Without additional functionalization, however, these devices lack selectivity; several strategies have been developed for the direct immobilization of bioreceptors on the transistor surface to improve detection capabilities. In this scenario, organic transistors have gained attention for their abilities to be coupled to biological systems and to detect biomolecules. In this Perspective, we discuss recent developments in organic-transistor-based biosensors, highlighting how their coupling with artificial membranes provides a strategy to improve sensitivity and selectivity in biosensing applications. Looking at future applications, this class of biosensors represents a breakthrough starting point for implementing multimodal high-throughput screening platforms.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Analytical Chemistry and Sensors · Conducting polymers and applications · Advanced biosensing and bioanalysis techniques

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

Affiliations

Tissue Electronics, Istituto Italiano di Tecnologia, 80125 Naples, Italy

Dipartimento di Chimica, Materiali e Produzione Industriale, Università di Napoli Federico II, 80126 Naples, Italy

Department of Medicine, University of Cambridge, Cambridge CB2 1TN, United Kingdom

References (69)

Show source-provided references
1. 10.1042/EBC2015000110.1042/EBC20150001 ↗
2. 10.1016/j.jobcr.2015.12.00210.1016/j.jobcr.2015.12.002 ↗
3. 10.1039/C9MH01544B10.1039/C9MH01544B ↗
4. 10.1016/j.bios.2020.11227410.1016/j.bios.2020.112274 ↗
5. 10.1021/acsnano.0c0282310.1021/acsnano.0c02823 ↗
6. Luz R. A. S. · 2013
7. 10.1016/j.bios.2020.11212910.1016/j.bios.2020.112129 ↗
8. 10.1016/j.talanta.2016.04.04610.1016/j.talanta.2016.04.046 ↗
9. 10.1021/acs.langmuir.5b0341510.1021/acs.langmuir.5b03415 ↗
10. Alberts B. · 2002
11. 10.3390/app909174510.3390/app9091745 ↗
12. Golowasch, J.; Nadim, F. In Encyclopedia of Computational Neuroscience; Jaeger, D., Jung, R., Eds. Springer: Berlin, 2013; pp 1–5.10.1007/978-1-4614-7320-6_32-1 ↗
13. 10.1021/acsnano.9b0091110.1021/acsnano.9b00911 ↗
14. 10.1038/s41598-020-61561-710.1038/s41598-020-61561-7 ↗
15. 10.1021/la950580r10.1021/la950580r ↗
16. 10.1371/journal.pcbi.100299710.1371/journal.pcbi.1002997 ↗
17. 10.1002/adfm.20180703310.1002/adfm.201807033 ↗
18. 10.1021/acs.langmuir.7b0294510.1021/acs.langmuir.7b02945 ↗
19. 10.1007/s00216-020-02449-310.1007/s00216-020-02449-3 ↗
20. 10.1021/acsami.9b1030310.1021/acsami.9b10303 ↗
21. 10.1021/acsnano.0c0133010.1021/acsnano.0c01330 ↗
22. 10.1021/acs.langmuir.0c0080410.1021/acs.langmuir.0c00804 ↗
23. 10.1038/natrevmats.2017.8610.1038/natrevmats.2017.86 ↗
24. 10.1002/adfm.20190451310.1002/adfm.201904513 ↗
25. 10.1002/admt.20200021810.1002/admt.202000218 ↗
26. 10.1002/adfm.20190536110.1002/adfm.201905361 ↗
27. 10.1039/C5TB00916B10.1039/C5TB00916B ↗
28. 10.1038/srep3541910.1038/srep35419 ↗
29. 10.1016/j.snb.2014.01.09710.1016/j.snb.2014.01.097 ↗
30. 10.1016/j.bios.2007.02.01510.1016/j.bios.2007.02.015 ↗
31. 10.1039/C7MH00887B10.1039/C7MH00887B ↗
32. 10.1002/adma.20140437810.1002/adma.201404378 ↗
33. 10.1126/sciadv.aar290410.1126/sciadv.aar2904 ↗
34. 10.1016/j.mee.2017.11.01610.1016/j.mee.2017.11.016 ↗
35. 10.1021/nn305903q10.1021/nn305903q ↗
36. 10.1021/nl071792z10.1021/nl071792z ↗
37. 10.1021/acsami.0c1020110.1021/acsami.0c10201 ↗
38. 10.1126/science.aao675010.1126/science.aao6750 ↗
39. 10.1021/acssensors.0c0069410.1021/acssensors.0c00694 ↗
40. 10.1021/la052687c10.1021/la052687c ↗
41. 10.1021/la970600k10.1021/la970600k ↗
42. 10.1116/1.494530610.1116/1.4945306 ↗
43. 10.1038/s41596-019-0174-210.1038/s41596-019-0174-2 ↗
44. 10.1021/acsami.6b0283710.1021/acsami.6b02837 ↗
45. 10.3390/polym904015010.3390/polym9040150 ↗
46. 10.1021/acsami.8b0044610.1021/acsami.8b00446 ↗
47. 10.1002/adfm.20160212310.1002/adfm.201602123 ↗
48. 10.1002/admt.20190068010.1002/admt.201900680 ↗
49. 10.1039/D0MH00548G10.1039/D0MH00548G ↗
50. 10.1016/j.coviro.2011.10.01210.1016/j.coviro.2011.10.012 ↗
51. 10.1056/NEJMoa121172110.1056/NEJMoa1211721 ↗
52. 10.1056/NEJMoa200101710.1056/NEJMoa2001017 ↗
53. Holmes E. · Virological · 2020
54. Gorbalenya A. E. · Nat. Microbiol. · 2020 · 5 · 53610.1038/s41564-020-0695-z ↗
55. 10.1002/jmv.2568510.1002/jmv.25685 ↗
56. 10.1016/S0140-6736(20)30251-810.1016/S0140-6736(20)30251-8 ↗
57. 10.1038/s41586-020-2180-510.1038/s41586-020-2180-5 ↗
58. 10.1038/nature0214510.1038/nature02145 ↗
59. 10.1016/j.cell.2020.02.05210.1016/j.cell.2020.02.052 ↗
60. 10.1371/journal.pbio.300000310.1371/journal.pbio.3000003 ↗
61. 10.1006/viro.2000.032010.1006/viro.2000.0320 ↗
62. 10.1146/annurev.micro.51.1.15110.1146/annurev.micro.51.1.151 ↗
63. 10.1073/pnas.150868611210.1073/pnas.1508686112 ↗
64. 10.1016/S0140-6736(20)30185-910.1016/S0140-6736(20)30185-9 ↗
65. 10.1056/NEJMc200173710.1056/NEJMc2001737 ↗
66. 10.1016/j.bios.2020.11257810.1016/j.bios.2020.112578 ↗
67. 10.1021/acs.nanolett.0c0197110.1021/acs.nanolett.0c01971 ↗
68. 10.1557/mrs.2020.20610.1557/mrs.2020.206 ↗
69. 10.1101/2020.07.21.21457710.1101/2020.07.21.214577 ↗