Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 298

The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index.

Employing Conductive Metal–Organic Frameworks for Voltammetric Detection of Neurochemicals

Ko, Michael, Mendecki, Lukasz, Eagleton, Aileen M., Durbin, Claudia G., Stolz, Robert M., Meng, Zheng, Mirica, Katherine A.·2020·Journal of the American Chemical Society

Abstract

This paper describes the first implementation of an array of two-dimensional (2D) layered conductive metal–organic frameworks (MOFs) as drop-casted film electrodes that facilitate voltammetric detection of redox active neurochemicals in a multianalyte solution. The device configuration comprises a glassy carbon electrode modified with a film of conductive MOF (M 3 HXTP 2; M = Ni, Cu; and X = NH, 2,3,6,7,10,11-hexaiminotriphenylene (HITP) or O, 2,3,6,7,10,11-hexahydroxytriphenylene (HHTP)). The utility of 2D MOFs in voltammetric sensing is measured by the detection of ascorbic acid (AA), dopamine (DA), uric acid (UA), and serotonin (5-HT) in 0.1 M PBS (pH = 7.4). In particular, Ni 3 HHTP 2 MOFs demonstrated nanomolar detection limits of 63 ± 11 nM for DA and 40 ± 17 nM for 5-HT through a wide concentration range (40 nM–200 μM). The applicability in biologically relevant detection was further demonstrated in simulated urine using Ni 3 HHTP 2 MOFs for the detection of 5-HT with a nanomolar detection limit of 63 ± 11 nM for 5-HT through a wide concentration range (63 nM–200 μM) in the presence of a constant background of DA. The implementation of conductive MOFs in voltammetric detection holds promise for further development of highly modular, sensitive, selective, and stable electroanalytical devices.

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/jacs.9b13402

Cites

114 of 114 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 114 references

Metadata

Authors
Ko, M., Mendecki, L., Eagleton, A. M., Durbin, C. G., Stolz, R. M., Meng, Z., & Mirica, K. A.
Venue
Journal of the American Chemical Society
Published
2020-03-10
Volume
142
Issue
27
Pages
11717-11733
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W3011450457
Cited by
298
References
114
FWCI
12.57
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

How to cite

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

This page
CoChat. (2026). Citation check: Employing Conductive Metal–Organic Frameworks for Voltammetric Detection of Neurochemicals. https://app.cochat.ai/papers/10.1021/jacs.9b13402
The paper
Ko, M., Mendecki, L., Eagleton, A. M., Durbin, C. G., Stolz, R. M., Meng, Z., & Mirica, K. A. (2020). Employing Conductive Metal–Organic Frameworks for Voltammetric Detection of Neurochemicals. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.9b13402

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.