The DOI matches its Crossref record.
Nonenzymatic Glucose Detection Using Mesoporous Platinum
Abstract
Roughness of nanoscopic dimensions can be used to selectively enhance the faradaic current of a sluggish reaction. Using this principle, we constructed mesoporous structures on the surfaces of pure platinum electrodes responding even more sensitively to glucose than to common interfering species, such as L-ascorbic acid and 4-acetamidophenol. Good sensitivities, as high as 9.6 microA cm(-2) mM(-1), were reproducibly observed in the presence of high concentration of chloride ion. The selectivities, sensitivities, and stabilities determined experimentally have demonstrated the potential of mesoporous platinum as a novel candidate for nonenzymatic glucose sensors.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Recent advances in electrochemical non-enzymatic glucose sensors – A review
Hwang, D.-W., Lee, S., Seo, M., & Chung, T. D. (2018) · Analytica Chimica Acta · doi:10.1016/j.aca.2018.05.051 · Cited by 788
Self-citationJiang, L.-C., & Zhang, W.-D. (2010) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2009.10.038 · Cited by 667
Mesoporous materials and electrochemistry
Walcarius, A. (2013) · Chemical Society Reviews · doi:10.1039/c2cs35322a · Cited by 623
A Critical Review of Glucose Biosensors Based on Carbon Nanomaterials: Carbon Nanotubes and Graphene
Zhu, Z., Garcia-Gancedo, L., Flewitt, A. J., Xie, H., Moussy, F., & Milne, W. I. (2012) · Sensors · doi:10.3390/s120505996 · Cited by 549
An improved sensitivity non-enzymatic glucose sensor based on a CuO nanowire modified Cu electrode
Zhuang, Z., Su, X., Yuan, H., Sun, Q., Xiao, D., & Choi, M. M. F. (2008) · The Analyst · doi:10.1039/b712970j · Cited by 481
Highly Ordered Platinum‐Nanotubule Arrays for Amperometric Glucose Sensing
Yuan, J. H., Wang, K., & Xia, X. H. (2005) · Advanced Functional Materials · doi:10.1002/adfm.200400321 · Cited by 432
CuO Nanospheres Based Nonenzymatic Glucose Sensor
Reitz, E., Jia, W., Gentile, M., Wang, Y., & Lei, Y. (2008) · Electroanalysis · doi:10.1002/elan.200804327 · Cited by 346
Metadata
- Authors
- Park, S., Chung, T. D., & Kim, H. C.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2003-05-15
- Volume
- 75
- Issue
- 13
- Pages
- 3046-3049
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2029927391
- Cited by
- 594
- References
- 0
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.
