Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 428

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Exceptional CO2capture in a hierarchically porous carbon with simultaneous high surface area and pore volume

Open access
Srinivas, Gadipelli, Krungleviciute, Vaiva, Guo, Zheng-Xiao, Yildirim, Taner·2014·Energy Environ. Sci.

Abstract

A new type of hierarchically porous carbon (HPC) structures of simultaneously high surface area and high pore volume has been synthesised from carefully controlled carbonization of in-house optimised metal–organic frameworks (MOFs). Changes in synthesis conditions lead to millimetre-sized MOF-5 crystals in a high yield. Subsequent carbonization of the MOFs yield HPCs with simultaneously high surface area, up to 2734 m2 g−1, and exceptionally high total pore volume, up to 5.53 cm3 g−1. In the HPCs, micropores are mostly retained and meso- and macro- pores are generated from defects in the individual crystals, which is made possible by structural inheritance from the MOF precursor. The resulting HPCs show a significant amount of CO2 adsorption, over 27 mmol g−1 (119 wt%) at 30 bar and 27 °C, which is one of the highest values reported in the literature for porous carbons. The findings are comparatively analysed with the literature. The results show great potential for the development of high capacity carbon-based sorbents for effective pre-combustion CO2 capture and other gas and energy storage 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 · 10.1039/c3ee42918k

Cites

6 of 6 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 6 references

Cited by

See all 428

Metadata

Authors
Srinivas, G., Krungleviciute, V., Guo, Z.-X., & Yildirim, T.
Venue
Energy Environ. Sci.
Published
2014
Volume
7
Issue
1
Pages
335-342
ISSN
  • 1754-5692 (ISSN-L)
  • 1754-5706
OpenAlex
W2135487064
Cited by
428
References
6
FWCI
46.94
Type
journal article
Language
en
Open access
hybrid

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: Exceptional CO2capture in a hierarchically porous carbon with simultaneous high surface area and pore volume. https://app.cochat.ai/papers/10.1039/c3ee42918k
The paper
Srinivas, G., Krungleviciute, V., Guo, Z.-X., & Yildirim, T. (2014). Exceptional CO2capture in a hierarchically porous carbon with simultaneous high surface area and pore volume. Energy Environ. Sci. https://doi.org/10.1039/c3ee42918k

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.