ANR ComputationalCarbonCapture

Novel strategies for efficient carbon capture and release by metal-organic frameworks using computational methods

JCJC 2015-2020

The idea behind this project is to develop novel MOFs that can capture CO2 more efficiently compared to existing materials by applying orginal strategies. Specifically, we propose to computationally design MOFs whose affinity for CO2 can be modified under light irradiation or by heating so that the adsorption and desorption can be performed each at the most convenient conditions, allowing to achieve a high energy efficiency and therefore a limited cost. We will study two families of MOFs for this purpose, I) photoactive MOFs, whose pore topology is modified by light or UV treatment and II) spin crossover MOFs, whose interaction with CO2 changes as a result of electronic transition induced by temperature.


Claudio Attaccalite: CNRS researcher @ CINAM, excited states calculations (GW/BSE)

Li-Chiang Lin: Assistant Professor @ OSU,  classical simulations: grand canonical Monte Carlo, molecular dynamics

Alberto Rodriguez Velamazan, beamline responsible @ ILL, spin crossover complexes, magnetic measurements, neutron scattering


Roberta Poloni

Total Grant

278 300,00 euros


Azzam Charaf Eddin:

postdoc funded by ANR



Aseem Rajan Kshirsagar:

PhD student funded by ANR



  1. Long range magnetic order in the porous metal-organic framework Ni(pyrazine)[Pt(CN)4] Phys. Chem. Chem. Phys. 19, 29084 – 29091, 2017