Scalable room temperature incorporation of CO2-selective ångström-scale pores in graphene for carbon capture

Atom-thin porous graphene membranes offer unprecedented carbon capture performance thanks to Å-scale pores that combine ultrahigh permeance with attractive selectivity. However, incorporating a high pore density has until now required elevated-temperature ozone oxidation, while oxidation at room temperature was found to be sluggish, limiting scalability. Herein, we uncover that graphene oxidation by ozone is constrained by mass transfer of ozone and concentration polarization from the accumulation of reaction byproduct at the surface. We overcome this bottleneck using micro-channeled flow reactor that enhances mass transfer, accelerating the oxidation rate, leading to a tenfold higher pore density at room temperature. Centimeter-scale porous graphene with a high density of CO<sub>2</sub>-selective pores is achieved, resulting in CO<sub>2</sub>/N<sub>2</sub> selectivity up to 21 and CO<sub>2</sub> permeance up to 4050 gas permeation units. A brief subsequent room-temperature pore-expansion step further boosts performance. Our fully ambient, scalable protocol eliminates high-temperature equipment and provides a practical route to industrial production of porous graphene membranes for carbon capture.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-11-24
DOI
https://doi.org/10.1038/s41467-025-65336-4
Primary Topic
CO2 Reduction Techniques and Catalysts
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Scalable room temperature incorporation of CO2-selective ångström-scale pores in graphene for carbon capture

Luc Bondaz, Jian Hao, Mounir Mensi, Mojtaba Chevalier et al.
Nature Communications
CO2 Reduction Techniques and Catalysts
article

Scalable room temperature incorporation of CO2-selective ångström-scale pores in graphene for carbon capture

Luc Bondaz, Jian Hao, Mounir Mensi, Mojtaba Chevalier, Ranadip Goswami, Yueqing Shen
article en

Abstract

Atom-thin porous graphene membranes offer unprecedented carbon capture performance thanks to Å-scale pores that combine ultrahigh permeance with attractive selectivity. However, incorporating a high pore density has until now required elevated-temperature ozone oxidation, while oxidation at room temperature was found to be sluggish, limiting scalability. Herein, we uncover that graphene oxidation by ozone is constrained by mass transfer of ozone and concentration polarization from the accumulation of reaction byproduct at the surface. We overcome this bottleneck using micro-channeled flow reactor that enhances mass transfer, accelerating the oxidation rate, leading to a tenfold higher pore density at room temperature. Centimeter-scale porous graphene with a high density of CO<sub>2</sub>-selective pores is achieved, resulting in CO<sub>2</sub>/N<sub>2</sub> selectivity up to 21 and CO<sub>2</sub> permeance up to 4050 gas permeation units. A brief subsequent room-temperature pore-expansion step further boosts performance. Our fully ambient, scalable protocol eliminates high-temperature equipment and provides a practical route to industrial production of porous graphene membranes for carbon capture.

Nature CommunicationsVol. 16(1)
Swiss Institute of Comparative Law (CH), École Polytechnique Fédérale de Lausanne (CH)
National Science Foundation, École Polytechnique Fédérale de Lausanne, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 74%
CO2 Reduction Techniques and Catalysts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.