Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 205

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Tuning the Microenvironment in Gas-Diffusion Electrodes Enables High-Rate CO2 Electrolysis to Formate

Xing, Zhuo, Hu, Xun, Feng, Xiaofeng·2021·ACS Energy Letters

Abstract

Electroreduction of CO 2 to formate on Bi-based catalyst is a promising route for CO 2 recycling and sustainable fuel production. The use of gas-diffusion-electrode (GDE) flow cells has generally improved the rate of CO 2 electrolysis, while the local reaction environment in GDEs and its impact on the electrolysis remain to be understood. Here, we report that tuning the microenvironment of Bi-based catalyst in a GDE by adding hydrophobic polytetrafluoroethylene (PTFE) nanoparticles in the catalyst layer can substantially enhance CO 2 electrolysis, achieving a partial current density of 677 mA cm –2 for formate production and 35% single-pass CO 2 conversion at −0.7 V versus RHE under 9-sccm CO 2 gas flow. It is revealed that a moderate hydrophobicity of the catalyst layer can establish a microenvironment with a balance between gaseous CO 2 and liquid electrolyte inside the catalyst layer, which reduces the diffusion layer thickness to accelerate CO 2 mass transport and forms highly active reaction zones near solid–liquid–gas interfaces.

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/acsenergylett.1c00612

Cites

7 of 7 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 7 references

Cited by

See all 205

Metadata

Authors
Xing, Z., Hu, X., & Feng, X.
Venue
ACS Energy Letters
Published
2021-04-07
Volume
6
Issue
5
Pages
1694-1702
ISSN
2380-8195 (ISSN-L)
OpenAlex
W3141753336
Cited by
205
References
7
FWCI
5.90
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: Tuning the Microenvironment in Gas-Diffusion Electrodes Enables High-Rate CO2 Electrolysis to Formate. https://app.cochat.ai/papers/10.1021/acsenergylett.1c00612
The paper
Xing, Z., Hu, X., & Feng, X. (2021). Tuning the Microenvironment in Gas-Diffusion Electrodes Enables High-Rate CO2 Electrolysis to Formate. ACS Energy Letters. https://doi.org/10.1021/acsenergylett.1c00612

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.