Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 579

The DOI matches its Crossref record; 117 of the 117 references it cites were found in a registry or an index.

Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope

Open access
Singh, Anoop, Sharma, Asha, Ahmed, Aamir, Sundramoorthy, Ashok K., Furukawa, Hidemitsu, Arya, Sandeep, Khosla, Ajit·2021·Biosensors
DOI 10.3390/bios11090336
Open this paper in CoChat — free account

Abstract

The electrochemical biosensors are a class of biosensors which convert biological information such as analyte concentration that is a biological recognition element (biochemical receptor) into current or voltage. Electrochemical biosensors depict propitious diagnostic technology which can detect biomarkers in body fluids such as sweat, blood, feces, or urine. Combinations of suitable immobilization techniques with effective transducers give rise to an efficient biosensor. They have been employed in the food industry, medical sciences, defense, studying plant biology, etc. While sensing complex structures and entities, a large data is obtained, and it becomes difficult to manually interpret all the data. Machine learning helps in interpreting large sensing data. In the case of biosensors, the presence of impurity affects the performance of the sensor and machine learning helps in removing signals obtained from the contaminants to obtain a high sensitivity. In this review, we discuss different types of biosensors along with their applications and the benefits of machine learning. This is followed by a discussion on the challenges, missing gaps in the knowledge, and solutions in the field of electrochemical biosensors. This review aims to serve as a valuable resource for scientists and engineers entering the interdisciplinary field of electrochemical biosensors. Furthermore, this review provides insight into the type of electrochemical biosensors, their applications, the importance of machine learning (ML) in biosensing, and challenges and future outlook.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/bios11090336

Cites

117 of 117 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 117 references

Research works printed but not linked: 58

  1. Khosla, A. (2011). Micropatternable Multifunctional Nanocomposite Polymers for Flexible Soft MEMS Applications. [Ph.D. Thesis, Applied Science: School of Engineering Science, Simon Fraser University].
  2. Ahmad, 2020, Hydrothermally synthesiz0ed nickel oxide nanosheets for non-enzymatic electrochemical glucose detection, J. Electrochem. Soc., 167, 107504, 10.1149/1945-7111/ab9757 · doi:10.1149/1945-7111/ab9757
  3. Chullasat, 2011, Ultra trace analysis of small molecule by label-free impedi-metric immunosensor using multilayer modified electrode, Biosens. Bioelectron., 26, 4571, 10.1016/j.bios.2011.05.029 · doi:10.1016/j.bios.2011.05.029
  4. Canbaz, 2014, Electrochemical biosensor based on self-assembled monolayersmodifi ed with gold nanoparticles for detection of HER-3, Anal Chim. Acta., 814, 31, 10.1016/j.aca.2014.01.041 · doi:10.1016/j.aca.2014.01.041
  5. Kim, 2009, An impedimetric biosensor for real-time monitoring of bacterial growth in a microbial fermentor, Sens. Actuators B Chem., 138, 270, 10.1016/j.snb.2009.01.034 · doi:10.1016/j.snb.2009.01.034
  6. Srisomwat, 2020, Pop-up paper electrochemical device for label-free hepatitis B virus DNA detection, Sens. Actuators B Chem., 316, 128077, 10.1016/j.snb.2020.128077 · doi:10.1016/j.snb.2020.128077
  7. Sannini, 2015, An amperometric biosensor for the determination of lactic acid during malolactic fermentation, Chem. Eng. Trans., 44, 283
  8. Ciriello, 2015, Quantification of l-lysine in cheese by a novel amperometric biosensor, Food Chem., 169, 13, 10.1016/j.foodchem.2014.07.141 · doi:10.1016/j.foodchem.2014.07.141
  9. Habtamu, 2015, Miniaturized Enzymatic Biosensor via Biofunctionalization of the Insulator of Nanoelectrode Ensembles, Electroanalysis, 27, 2187, 10.1002/elan.201500115 · doi:10.1002/elan.201500115
  10. Malvano, 2015, Ethanol content in must grape by alcohol dehydrogenase biosensors based on doped Polyaniline modified screen printed electrodes, Chem. Eng. Trans., 43, 37
  11. Tortolini, 2016, Inhibition-based biosensor for atrazine detection, Sens. Actuators B Chem., 224, 552, 10.1016/j.snb.2015.10.095 · doi:10.1016/j.snb.2015.10.095
  12. Grattieri, 2015, Enzymatic Oxygen Microsensor Based on Bilirubin Oxidase Applied to Microbial Fuel Cells Analysis, Electroanalysis, 27, 327, 10.1002/elan.201400543 · doi:10.1002/elan.201400543
  13. Silletti, 2015, An optical biosensor based on a multiarray of enzymes for monitoring a large set of chemical classes in milk, Sens. Actuators B Chem., 215, 607, 10.1016/j.snb.2015.03.092 · doi:10.1016/j.snb.2015.03.092
  14. Chiavaioli, 2015, Sol-Gel-Based Titania-Silica Thin Film Overlay for Long Period Fiber Grating-Based Biosensors, Anal. Chem., 87, 12024, 10.1021/acs.analchem.5b01841 · doi:10.1021/acs.analchem.5b01841
  15. Tardivo, 2015, Parallel optical read-out of micromechanical pillars applied to prostate specific membrane antigen detection, Biosens. Bioelectron., 72, 393, 10.1016/j.bios.2015.05.026 · doi:10.1016/j.bios.2015.05.026
  16. Scarano, 2015, Design of a dual aptamer-based recognition strategy for human matrix metalloproteinase 9 protein by piezoelectric biosensors, Anal. Chim. Acta, 897, 1, 10.1016/j.aca.2015.07.009 · doi:10.1016/j.aca.2015.07.009
  17. Ravalli, 2015, A label-free electrochemical affisensor for cancer marker detection: The case of HER2, Bioelectrochemistry, 106, 268, 10.1016/j.bioelechem.2015.07.010 · doi:10.1016/j.bioelechem.2015.07.010
  18. Benetti, 2015, A surface acoustic wave bio-electronic nose for detection of volatile odorant molecules, Biosens. Bioelectron., 67, 516, 10.1016/j.bios.2014.09.027 · doi:10.1016/j.bios.2014.09.027
  19. Cennamo, N., Giovanni, S.D., Varriale, A., Staiano, M., Di Pietrantonio, F., Notargiacomo, A., Zeni, L., and D’Auria, S. (2015). Easy to use plastic optical fiber-based biosensor for detection of butanal. PLoS ONE, 10. · doi:10.1371/journal.pone.0116770
  20. Compagnone, 2015, Quartz crystal microbalance gas sensor arrays for the quality control of chocolate, Sens. Actuators B Chem., 207, 1114, 10.1016/j.snb.2014.10.049 · doi:10.1016/j.snb.2014.10.049
  21. Zuccaro, 2015, Real-Time Label-Free Direct Electronic Monitoring of Topoisomerase Enzyme Binding Kinetics on Graphene, ACS Nano, 9, 11166, 10.1021/acsnano.5b05709 · doi:10.1021/acsnano.5b05709
  22. Lacina, 2018, Biosensing based on electrochemical impedance spectroscopy: Influence of the often-ignored molecular charge, Electrochem. Commun., 93, 183, 10.1016/j.elecom.2018.07.015 · doi:10.1016/j.elecom.2018.07.015
  23. Raoof, 2015, Genosensor for Point Mutation Detection of P53 Gene PCR Product Using Magnetic Particles, Electroanalysis, 27, 1378, 10.1002/elan.201400660 · doi:10.1002/elan.201400660
  24. Wu, 2017, Gold nanoparticles dotted reduction Graphene oxide Nanocomposite based electrochemical Aptasensor for selective, rapid, sensitive and congener-specific PCB77 detection, Sci. Rep., 7, 1
  25. Rodriguez, 2002, Conductometric uric acid and urea biosensor prepared from electroconductive polyaniline–poly (n-butyl methacrylate) composites, Sens. Actuators B: Chem., 85, 19, 10.1016/S0925-4005(02)00045-X · doi:10.1016/s0925-4005(02)00045-x
  26. Li, 2011, Fabrication and testing of thermally responsive hydrogel-based actuators using polymer heater elements for flexible microvalves, Microfluidics, BioMEMS, and Medical Microsystems IX, Volume 7929, 79290G-1
  27. Ahmad, 2014, High performance cholesterol sensor based on ZnO nanotubes grown on Si/Ag electrodes, Electrochem. Commun., 38, 4, 10.1016/j.elecom.2013.10.028 · doi:10.1016/j.elecom.2013.10.028
  28. Ahmad, 2013, High-performance cholesterol sensor based on the solution-gated field effect transistor fabricated with ZnO nanorods, Biosens. Bioelectron., 45, 281, 10.1016/j.bios.2013.01.021 · doi:10.1016/j.bios.2013.01.021
  29. Wang, 2012, Highly-sensitive cholesterol biosensor based on platinum-gold hybrid functionalized ZnO nanorods, Talanta, 94, 263, 10.1016/j.talanta.2012.03.037 · doi:10.1016/j.talanta.2012.03.037
  30. Batra, 2015, ZnO-CuO composite matrix based reagentless biosensor for detection of total cholesterol, Biosens. Bioelectron., 67, 263, 10.1016/j.bios.2014.08.029 · doi:10.1016/j.bios.2014.08.029
  31. Oh, 2016, An ultrasensitive and incubation-free electrochemicalimmunosensor using a gold-nanocatalyst label mediating outer-spherereaction-philic and inner-sphere-reaction-philic species, Chem. Commun., 52, 5884, 10.1039/C6CC00353B · doi:10.1039/c6cc00353b
  32. Cai, 2016, Ratiometric electrochemical immunoassay based on internalreference value for reproducible and sensitive detection of tumor marker, Biosens. Bioelectron., 81, 173, 10.1016/j.bios.2016.02.066 · doi:10.1016/j.bios.2016.02.066
  33. Zhang, 2016, A novel electrochemical immunosensor based onnonenzymatic Ag@au-Fe3O4 nanoelectrocatalyst for protein biomarkerdetection, Biosens. Bioelectron., 85, 343, 10.1016/j.bios.2016.04.100 · doi:10.1016/j.bios.2016.04.100
  34. Xu, 2007, Biosensor based on self-assembling glucose oxidase and Dendrimer-encapsulated Pt nanoparticles on carbon nanotubes for glucosedetection, Electroanalysis, 19, 717, 10.1002/elan.200603805 · doi:10.1002/elan.200603805
  35. Gupta, 2017, Ultrasensitive transglutaminase based nanosensor for early detection of celiac dis- ease in human, Int. J. Biol. Macromol., 105, 905, 10.1016/j.ijbiomac.2017.07.126 · doi:10.1016/j.ijbiomac.2017.07.126
  36. Neves, 2012, Celiac disease detection using a transglutaminase electro-chemical immunosensor fabricated on nanohybrid screen-printed carbon electrodes, Biosens. Bioelectron., 31, 95, 10.1016/j.bios.2011.09.044 · doi:10.1016/j.bios.2011.09.044
  37. Hianik, 2005, Detection of aptamer—protein interactions using QCM and electrochemical indicator methods, Bioorg. Med. Chem., 15, 291, 10.1016/j.bmcl.2004.10.083 · doi:10.1016/j.bmcl.2004.10.083
  38. Arya, 2019, Comparative study of CuO, CuO@ Ag and CuO@ Ag: La nanoparticles for their photosensing properties, Mater. Res. Express, 6, 116313, 10.1088/2053-1591/ab49ab · doi:10.1088/2053-1591/ab49ab
  39. Marks, R.S., and Abdulhalim, I. (2015). Nanomaterials for Water Management: Signal Amplification for Biosensing from Nanostructures, CRC Press. · doi:10.1201/b18715
  40. Ly, 2011, Diagnosis of Helicobacter pylori bacterial infections using a voltammetric biosensor, J. Microbiol. Methods, 87, 44, 10.1016/j.mimet.2011.07.002 · doi:10.1016/j.mimet.2011.07.002
  41. Wang, J. (2006). Analytical Electrochemistry Hoboken, John Wiley & Sons, Inc.
  42. Elshafey, 2013, Electrochemical impedance immunosensor based on gold nanoparticles—protein G for the detection of cancer marker epidermal growth factor receptor in human plasma and brain tissue, Biosens. Bioelectron., 50, 143, 10.1016/j.bios.2013.05.063 · doi:10.1016/j.bios.2013.05.063
  43. Seven, 2013, Impedimetric biosensor for cancer cell detection, Electrochem. Commun., 37, 36, 10.1016/j.elecom.2013.10.003 · doi:10.1016/j.elecom.2013.10.003
  44. Chowdhury, 2012, Label free polyaniline based impedimetric biosensor for detection of E. coli O157: H7 Bacteria, Sens. Actuators B Chem., 171, 916, 10.1016/j.snb.2012.06.004 · doi:10.1016/j.snb.2012.06.004
  45. Shirsat, 2007, Amperometric glucose biosensor on layer bylayer assembled carbon nanotube and Polypyrrole multilayer film, Electroanalysis, 20, 150, 10.1002/elan.200704028 · doi:10.1002/elan.200704028
  46. Jakhar, 2018, Preparation, characterization and application of urease nanoparticles for construction of an improved potentiometric urea biosensor, Biosens. Bioelectron., 100, 242, 10.1016/j.bios.2017.09.005 · doi:10.1016/j.bios.2017.09.005
  47. Singh, 2021, Promising photocatalytic degradation of methyl orange dye via sol-gel synthesized Ag–CdS@ Pr-TiO2 core/shell nanoparticles, Phys. B Condens. Matter, 616, 413121, 10.1016/j.physb.2021.413121 · doi:10.1016/j.physb.2021.413121
  48. Chung, 2013, Investigations of flexible Ag/AgCl nanocomposite polymer electrodes for suitability in tissue electrical impedance scanning (EIS), J. Electrochem. Soc., 161, B3071, 10.1149/2.018402jes · doi:10.1149/2.018402jes
  49. Singh, A., Arya, S., Khanuja, M., Hafiz, A.K., Datt, R., Gupta, V., and Khosla, A. (2020). Eu doped NaYF 4@ Er: TiO 2 nanoparticles for tunable ultraviolet light based anti-counterfeiting applications. Microsyst. Technol., 1–10. · doi:10.1007/s00542-019-04734-3
  50. Choi, 1998, Evaluation of two types of biosensors for immunoassay of botulinum toxin, J. Biochem. Mol. Biol., 31, 101
  51. Gaffar, 2017, Voltammetric DNA biosensor using gold electrode modified by self assembled monolayer of Thiol for detection of mycobacterium tuberculosis, Procedia Technol., 27, 74, 10.1016/j.protcy.2017.04.034 · doi:10.1016/j.protcy.2017.04.034
  52. Jamal, 2018, A non enzymatic glutamate sensor based on nickel oxide nanoparticle, Microsyst. Technol., 24, 4217, 10.1007/s00542-018-3724-6 · doi:10.1007/s00542-018-3724-6
  53. Wang, 2017, Detection of extremely low concentration waterborne pathogen using a multiplexing selfreferencing SERS microfluidic biosensor, J. Biol. Eng., 11, 9, 10.1186/s13036-017-0051-x · doi:10.1186/s13036-017-0051-x
  54. Olano, 2015, The influence of sky conditions on the standardized calibration of pyranometers and on the measurement of global solar irradiation, Sol. Energy, 121, 116, 10.1016/j.solener.2015.07.031 · doi:10.1016/j.solener.2015.07.031
  55. Massah, 2013, An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing, Arch. Phytopathol. Plant Protect., 46, 1580, 10.1080/03235408.2013.772321 · doi:10.1080/03235408.2013.772321
  56. Bailer, C., Habtegebrial, T., Varanasi, K., and Stricker, D. (2018). Fast Feature Extraction with CNNs with Pooling Layers. arXiv.
  57. Lipton, Z.C., Berkowitz, J., and Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv.
  58. Williams, J.D., and Zweig, G. (2016). End-to-end lstm-based dialog control optimized with supervised and reinforcement learning. arXiv.

Metadata

Authors
Singh, A., Sharma, A., Ahmed, A., Sundramoorthy, A. K., Furukawa, H., Arya, S., & Khosla, A.
Venue
Biosensors
Publisher
MDPI AG
Published
2021-09-14
Volume
11
Issue
9
Pages
336
ISSN
2079-6374 (ISSN-L)
DOI
10.3390/bios11090336
OpenAlex
W3200063015
Cited by
579
References
117
FWCI
23.26
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Japan Society for the Promotion of Science (JP17H01224, JP18H0547, JP19H01122)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope. https://app.cochat.ai/papers/10.3390/bios11090336
The paper
Singh, A., Sharma, A., Ahmed, A., Sundramoorthy, A. K., Furukawa, H., Arya, S., & Khosla, A. (2021). Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope. Biosensors. https://doi.org/10.3390/bios11090336

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.