Cited by 762Electrochemical sensors and biosensorsAnalytical Chemistry and SensorsAdvanced biosensing and bioanalysis techniques

A Comprehensive Review of Glucose Biosensors Based on Nanostructured Metal-Oxides

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Rahman, Md. Mahbubur, Ahammad, A. J. Saleh, Jin, Joon-Hyung, Ahn, Sang Jung, Lee, Jae-Joon·2010·Sensors

Abstract

Nanotechnology has opened new and exhilarating opportunities for exploring glucose biosensing applications of the newly prepared nanostructured materials. Nanostructured metal-oxides have been extensively explored to develop biosensors with high sensitivity, fast response times, and stability for the determination of glucose by electrochemical oxidation. This article concentrates mainly on the development of different nanostructured metal-oxide [such as ZnO, Cu(I)/(II) oxides, MnO2, TiO2, CeO2, SiO2, ZrO2, and other metal-oxides] based glucose biosensors. Additionally, we devote our attention to the operating principles (i.e., potentiometric, amperometric, impedimetric and conductometric) of these nanostructured metal-oxide based glucose sensors. Finally, this review concludes with a personal prospective and some challenges of these nanoscaled sensors.

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  • The main advantage of third-generation sensors is high selectivity because the working potential is identical to that of the enzyme and less prone to interference.

    “The main advantage is the high selectivity since the working potential is identical to that of the enzyme and, therefore, less prone to any interference 47,48.”

    From Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices (2022) · cites this paper as [48]

    The Amperometric Glucose Sensor section (page not provided) describes direct electron transfer in third-generation sensors and reports good selectivity and freedom from uric-acid and ascorbic-acid interference for one ZnO-nanorod sensor. It does not say that the working potential is identical to the enzyme’s potential or attribute the reduced interference to that mechanism; it also notes protection by a Nafion membrane. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Authors
Rahman, M. M., Ahammad, A. J. S., Jin, J.-H., Ahn, S. J., & Lee, J.-J.
Affiliations
  • Konkuk University (KR)
  • Korea Research Institute of Standards and Science (KR)
Corresponding author
Lee, Jae-Joon
Venue
Sensors
Publisher
MDPI AG
Published
2010-05-12
Volume
10
Issue
5
Pages
4855-4886
ISSN
1424-8220 (ISSN-L)
OpenAlex
W2147818124
Cited by
762
References
2
Citations by year
2026: 20 · 2025: 35 · 2024: 35 · 2023: 55 · 2022: 44 · 2021: 64 · 2020: 50 · 2019: 78 · 2018: 58 · 2017: 56 · 2016: 74 · 2015: 55 · 2014: 52 · 2013: 31 · 2012: 36
FWCI
1.35
Type
journal article
Language
en
Open access
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation
  • National Research Foundation of Korea
  • Ministry of Education and Human Resources Development
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
  • Physical Sciences › Chemical Engineering › Bioengineering › Analytical Chemistry and Sensors
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced biosensing and bioanalysis techniques
Keywords
glucose biosensor, metal oxide nanostructures, electrochemical glucose oxidation, zinc oxide, copper oxide, manganese dioxide, titanium dioxide, cerium oxide, zirconia, silica, potentiometric sensors, amperometric sensor, impedimetric sensor, conductometric sensors, nanomaterials
MeSH terms
Glucose, Metals, Oxides, Biosensing Techniques, Nanostructures, Electrochemical Techniques

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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.

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