Center for Molecular Modeling - S. Ravichandran

High-performance hydrophobic MOFs for selective acetone capture under humid conditions

louis — Tue, 20 Jan 2026 10:05:26 +0000

S. Grigoletto, K. Karami, I. Maye, A. Padunnappattu, S. Ravichandran, M. Wahiduzzaman, L. Vanduyfhuys, V. Van Speybroeck, M. Thommes, J.F.M. Denayer, N. Stock, G. Maurin

Journal of Materials Chemistry A

13, 26401-26412

2025

Abstract

Capturing acetone, a major indoor air pollutant, under humid conditions is a longstanding challenge in materials science. The key obstacle lies in finding porous adsorbents that simultaneously exhibit strong affinity for acetone and intrinsic hydrophobicity, a rare and elusive pairing. Leveraging the structural and chemical versatility of metal-organic frameworks (MOFs), we first explored a diverse set of MOFs using force field Monte Carlo and density-functional theory calculations. This computational strategy identified CAU-11(Al) as a top performer: a hydrophobic, small pore MOF that enables both high acetone affinity and uptake at trace concentrations with excellent selectivity over water. Experimental validation through gas-phase pulse chromatography, adsorption measurements, and breakthrough studies confirmed the outstanding performance of this sorbent under competitive acetone/water conditions. These results position CAU-11(Al) as a promising material for real-world acetone capture in humid indoor environments.

Gold Open Access

DOI

http://dx.doi.org/

Private attachment

d5ta03712c.pdf

Reaching quantum accuracy in predicting adsorption properties for ethane/ethene in ZIF-8 at the low pressure regime

leen — Tue, 11 Jun 2024 12:18:41 +0000

S. Ravichandran, M. Najafi, R. Goeminne, J. F. M. Denayer, V. Van Speybroeck, L. Vanduyfhuys

Journal of Chemical Theory and Computation

20, 12, 5225-5240

2024

Abstract

Nanoporous materials in the form of metal−organicframeworks such as zeolitic imidazolate framework-8 (ZIF-8) arepromising membrane materials for the separation of hydrocarbonmixtures. To compute the adsorption isotherms in suchadsorbents, grand canonical Monte Carlo simulations have provento be very useful. The quality of these isotherms depends on theaccuracy of adsorbate−adsorbent interactions, which are mostlydescribed using force fields owing to their low computational cost.However, force field predictions of adsorption uptake often showdiscrepancies from experiments at low pressures, providing theneed for methods that are more accurate. Hence, in this work, wepropose and validate two novel methodologies for the ZIF-8/ethane and ethene systems; a benchmarking methodology toevaluate the performance of any given force field in describing adsorption in the low-pressure regime and a refinement procedure torescale the parameters of a force field to better describe the host−guest interactions and provide for simulation isotherms with closeagreement to experimental isotherms. Both methodologies were developed based on a reference Henry coefficient, computed withthe PBE-MBD functional using the importance sampling technique. The force field rankings predicted by the benchmarkingmethodology involve the comparison of force field derived Henry coefficients with the reference Henry coefficients and ranking theforce fields based on the disparities between these Henry coefficients. The ranking from this methodology matches the rankingsmade based on uptake disparities by comparing force field derived simulation isotherms to experimental isotherms in the low-pressure regime. The force field rescaling methodology was proven to refine even the worst performing force field in UFF/TraPPE.The uptake disparities of UFF/TraPPE improved from 197% and 194% to 11% and 21% for ethane and ethene, respectively. Theproposed methodology is applicable to predict adsorption across nanoporous materials and allows for rescaled force fields to reachquantum accuracy without the need for experimental input.

DOI

https://doi.org/10.1021/acs.jctc.4c00293

Private attachment

ravichandran-et-al-2024-reaching-quantum-accuracy-in-predicting-adsorption-properties-for-ethane-ethene-in-zeolitic.pdf

Manuscript 1 (2).pdf

High-Throughput Screening of Covalent Organic Frameworks for Carbon Capture Using Machine Learning

sven — Tue, 14 May 2024 14:50:15 +0000

J. De Vos, S. Ravichandran, S. Borgmans, L. Vanduyfhuys, P. Van der Voort, S.M.J. Rogge, V. Van Speybroeck

Chemistry of Materials

36, 9, 4315-4330

2024

Abstract

Postcombustion carbon capture provides a high-potential pathway to reduce anthropogenic CO₂ emissions in the short term. In this respect, nanoporous materials, such as covalent organic frameworks (COFs), offer a promising platform as adsorbent beds. However, due to the modular nature of COFs, an almost unlimited number of structures can possibly be synthesized. To efficiently identify promising materials and reveal performance trends within the COF material space, we present a computational high-throughput screening of 268,687 COFs for their ability to efficiently and selectively separate CO₂ from the flue gas of power plants using a pressure swing adsorption process. Furthermore, we demonstrate that our screening can be significantly accelerated using machine learning to identify a set of promising materials. These are subsequently characterized with high accuracy, taking into account the effects of competitive adsorption and coadsorption. Our screening reveals that imide, (keto)enamine, and (acyl)hydrazone COFs are particularly interesting for carbon capture. Additionally, the best-performing materials are 3D COFs possessing strong CO₂ adsorption sites between aromatic rings at opposite sides of pores with a diameter of 1.0 nm. In 2D COFs, a significant influence of the framework chemistry, such as imide linkages or fluoro groups, is observed. Our design rules can guide experimental researchers to construct high-performing COFs for CO₂ capture.

Gold Open Access

DOI

http://dx.doi.org/10.1021/acs.chemmater.3c03230

pH Sensitivity Estimation in Potentiometric Metal Oxide pH Sensors Using the Principle of Invariance

siddharth — Sat, 27 Apr 2024 09:29:42 +0000

S. Ravichandran, C. Thiagarajan, P. Senthil Kumar

International Journal of Chemical Engineering

vol. 2021, Article ID 5551259, 18 pages

2021

Published while none of the authors were employed at the CMM

DOI

http://dx.doi.org/

Liquid Densities and Excess Quantities for the Green Esterification Process

siddharth — Sat, 27 Apr 2024 09:22:17 +0000

A. Rajendran, R. Anantharaj, D. John Paul Mark Reji, S. Nagaraj, S. Ravichandran

Journal of Chemical & Engineering Data

2019

Published while none of the authors were employed at the CMM

DOI

http://dx.doi.org/

New frontiers in modeling adsorption in metal-organic frameworks accounting for flexibility and accurate intermolecular interactions

leen — Thu, 18 Sep 2025 07:36:03 +0000

V. Van Speybroeck, R. Goeminne, S. Ravichandran, T. Verstraelen, L. Vanduyfhuys

ISBN/ISSN:

Keynote

Conference / event / venue

6th European Conference on Metal Organic Frameworks and Porous Polymers

Heraklion, Crete-Greece

Sunday, 21 September, 2025 to Wednesday, 24 September, 2025

Abstract

EUROMOF2025_VanSpeybroeck_final.pdf

Conference reference

EUROMOF 2025

Reaching quantum accuracy in predicting adsorption properties for ethane/ethene in ZIF-8 at the low-pressure regime

siddharth — Sat, 27 Apr 2024 12:20:44 +0000

S. Ravichandran, L. Vanduyfhuys, V. Van Speybroeck

ISBN/ISSN:

Poster

Conference / event / venue

FEARS 2023

Ghent, Belgium

Thursday, 26 October, 2023

Reaching quantum accuracy in predicting adsorption properties for ethane/ethene in ZIF-8 at the low-pressure regime

siddharth — Sat, 27 Apr 2024 12:12:21 +0000

S. Ravichandran, L. Vanduyfhuys, V. Van Speybroeck

ISBN/ISSN:

Poster

Conference / event / venue

EUROMOF 2023

Granada,Spain

Sunday, 24 September, 2023 to Wednesday, 27 September, 2023

Going beyond a pure force field approach to predict adsorption properties in MOFs in the low pressure regime with quantum accuracy

mieke — Thu, 18 Apr 2024 14:39:00 +0000

S. Ravichandran, R. Goeminne, L. Vanduyfhuys, V. Van Speybroeck

ISBN/ISSN:

Poster

Conference / event / venue

MOFSIM2024

Montpellier, France

Wednesday, 10 April, 2024 to Friday, 12 April, 2024

Conference reference

MOFSIM2024

High-throughput screening of covalent organic frameworks for carbon capture using machine learning

mieke — Thu, 18 Apr 2024 14:34:48 +0000

J. De Vos, S. Ravichandran, S. Borgmans, L. Vanduyfhuys, P. Van der Voort, S.M.J. Rogge, V. Van Speybroeck

ISBN/ISSN:

Poster

Conference / event / venue

MOFSIM2024

Montpellier, France

Wednesday, 10 April, 2024 to Friday, 12 April, 2024

Conference reference

MOFSIM2024