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; 80 of the 80 references it cites were found in a registry or an index.

Carbon black as an outstanding and affordable nanomaterial for electrochemical (bio)sensor design

Arduini, Fabiana, Cinti, Stefano, Mazzaracchio, Vincenzo, Scognamiglio, Viviana, Amine, Aziz, Moscone, Danila·2020·Biosensors and Bioelectronics

Abstract

Advances in cutting-edge technologies including nanotechnology, microfluidics, electronic engineering, and material science have boosted a new era in the design of robust and sensitive biosensors. In recent years, carbon black has been re-discovered in the design of electrochemical (bio)sensors thanks to its interesting electroanalytical properties, absence of treatment requirement, cost-effectiveness (c.a. 1 €/Kg), and easiness in the preparation of stable dispersions. Herein, we present an overview of the literature on carbon black-based electrochemical (bio)sensors, highlighting current trends and possible challenges to this rapidly developing area, with a special focus on the fabrication of carbon black-based electrodes in the realisation of sensors and biosensors (e.g. enzymatic, immunosensors, and DNA-based).

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 · 10.1016/j.bios.2020.112033

Cites

80 of 80 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 80 references

Cited by

See all 298

Metadata

Authors
Arduini, F., Cinti, S., Mazzaracchio, V., Scognamiglio, V., Amine, A., & Moscone, D.
Venue
Biosensors and Bioelectronics
Publisher
Elsevier BV
Published
2020-05
Volume
156
Pages
112033
ISSN
0956-5663 (ISSN-L)
OpenAlex
W3003631595
Cited by
298
References
80
FWCI
14.60
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: Carbon black as an outstanding and affordable nanomaterial for electrochemical (bio)sensor design. https://app.cochat.ai/papers/10.1016/j.bios.2020.112033
The paper
Arduini, F., Cinti, S., Mazzaracchio, V., Scognamiglio, V., Amine, A., & Moscone, D. (2020). Carbon black as an outstanding and affordable nanomaterial for electrochemical (bio)sensor design. Biosensors and Bioelectronics. https://doi.org/10.1016/j.bios.2020.112033

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.