Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 275

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

3D-Printed Graphene/Polylactic Acid Electrodes Promise High Sensitivity in Electroanalysis

Manzanares Palenzuela, C. Lorena, Novotný, Filip, Krupička, Petr, Sofer, Zdeněk, Pumera, Martin·2018·Analytical Chemistry

Abstract

Additive manufacturing provides a unique tool for prototyping structures toward electrochemical sensing, due to its ability to produce highly versatile, tailored-shaped devices in a low-cost and fast way with minimized waste. Here we present 3D-printed graphene electrodes for electrochemical sensing. Ring- and disc-shaped electrodes were 3D-printed with a Fused Deposition Modeling printer and characterized using cyclic voltammetry and scanning electron microscopy. Different redox probes K 3 Fe(CN) 6:K 4 Fe(CN) 6, FeCl 3, ascorbic acid, Ru(NH 3 ) 6 Cl 3, and ferrocene monocarboxylic acid) were used to assess the electrochemical performance of these devices. Finally, the electrochemical detection of picric acid and ascorbic acid was carried out as proof-of-concept analytes for sensing applications. Such customizable platforms represent promising alternatives to conventional electrodes for a wide range of sensing 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/acs.analchem.8b00083

Cites

1 of 1 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 1 references

Cited by

See all 275

Metadata

Authors
Manzanares Palenzuela, C. L., Novotný, F., Krupička, P., Sofer, Z., & Pumera, M.
Venue
Analytical Chemistry
Published
2018-04-16
Volume
90
Issue
9
Pages
5753-5757
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
OpenAlex
W2799843285
Cited by
275
References
1
FWCI
12.04
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: 3D-Printed Graphene/Polylactic Acid Electrodes Promise High Sensitivity in Electroanalysis. https://app.cochat.ai/papers/10.1021/acs.analchem.8b00083
The paper
Manzanares Palenzuela, C. L., Novotný, F., Krupička, P., Sofer, Z., & Pumera, M. (2018). 3D-Printed Graphene/Polylactic Acid Electrodes Promise High Sensitivity in Electroanalysis. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.8b00083

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.