Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 264

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Benzimidazole-based hyper-cross-linked poly(ionic liquid)s for efficient CO2 capture and conversion

Sang, Yafei, Huang, Jianhan·2020·Chemical Engineering Journal

Abstract

Abstract Porous materials with high Brunauer-Emmett-Teller (BET) surface area (SBET) and abundant ionic active sites facilitate efficient CO2 capture and conversion. Herein, a kind of novel benzimidazole-based hyper-cross-linked poly(ionic liquid)s (HPILs) were developed via the hyper-cross-linking strategy and the as-prepared HPILs possessed high SBET (485–780 m2/g), plentiful micro/mesoporosity, and abundant ionic active sites. The HPILs showed noteworthy CO2 uptake (79–143 mg/g at 273 K and 1.0 bar), moderate isosteric heats (32–45 kJ/mol), and acceptable CO2/N2 selectivity (IAST: 23–46). Intriguingly, HPILs-Cl-2 exhibited excellent catalytic activity for CO2 cycloaddition and 99% of propylene oxide was successfully converted to cyclic carbonates under mild conditions (70 °C, 0.1 MPa CO2, 9 h). This synthetic strategy is of great importance for the functionalized hyper-cross-linked polymers and the synthesized HPILs are promising for efficient CO2 capture and conversion under mild conditions.

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.cej.2019.123973

Cites

55 of 55 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 55 references

Metadata

Authors
Sang, Y., & Huang, J.
Venue
Chemical Engineering Journal
Publisher
Elsevier BV
Published
2020-04
Volume
385
Pages
123973
ISSN
1385-8947 (ISSN-L)
OpenAlex
W2998635116
Cited by
264
References
55
FWCI
15.63
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: Benzimidazole-based hyper-cross-linked poly(ionic liquid)s for efficient CO2 capture and conversion. https://app.cochat.ai/papers/10.1016/j.cej.2019.123973
The paper
Sang, Y., & Huang, J. (2020). Benzimidazole-based hyper-cross-linked poly(ionic liquid)s for efficient CO2 capture and conversion. Chemical Engineering Journal. https://doi.org/10.1016/j.cej.2019.123973

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.