Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 350

The DOI matches its Crossref record.

Solution Chemistry of Self-Assembled Graphene Nanohybrids for High-Performance Flexible Biosensors

Choi, Bong Gill, Park, HoSeok, Park, Tae Jung, Yang, Min Ho, Kim, Joon Sung, Jang, Sung-Yeon, Heo, Nam Su, Lee, Sang Yup, Kong, Jing, Hong, Won Hi·2010·ACS Nano

Abstract

We report the preparation of free-standing flexible conductive reduced graphene oxide/Nafion (RGON) hybrid films by a solution chemistry that utilizes self-assembly and directional convective-assembly. The hydrophobic backbone of Nafion provided well-defined integrated structures, on micro- and macroscales, for the construction of hybrid materials through self-assembly, while the hydrophilic sulfonate groups enabled highly stable dispersibility ( approximately 0.5 mg/mL) and long-term stability (2 months) for graphene. The geometrically interlocked morphology of RGON produced a high degree of mechanical integrity in the hybrid films, while the interpenetrating network constructed favorable conduction pathways for charge transport. Importantly, the synergistic electrochemical characteristics of RGON were attributed to high conductivity (1176 S/m), facilitated electron transfer (ET), and low interfacial resistance. Consequently, RGON films obtained the excellent figure of merit as electrochemical biosensing platforms for organophosphate (OP) detection, that is, a sensitivity of 10.7 nA/microM, detection limit of 1.37 x 10(-7) M, and response time of <3 s. In addition, the reliability of RGON biosensors was confirmed by a fatigue test of 100 bending cycles. The strategy described here provides insight into the fabrication of graphene and hybrid nanomaterials from a material perspective, as well as the design of biosensor platforms for practical device applications.

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 · 10.1021/nn100145x

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.

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

Cited by

Metadata

Authors
Choi, B. G., Park, H., Park, T. J., Yang, M. H., Kim, J. S., Jang, S.-Y., Heo, N. S., Lee, S. Y., Kong, J., & Hong, W. H.
Venue
ACS Nano
Published
2010-04-08
Volume
4
Issue
5
Pages
2910-2918
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W1996168846
Cited by
350
References
0
FWCI
34.14
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.