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Author Correction: Introducing metal–sulfur active sites in metal–organic frameworks via post-synthetic modification for hydrogenation catalysis

Haomiao Xie
Milad Ahmadi Khoshooei
Omar K. Farha
Amendments and Corrections23 Sept 2025
Nature Chemistry

P: 1
Introducing metal–sulfur active sites in metal–organic frameworks via post-synthetic modification for hydrogenation catalysis

Metal–sulfur motifs are commonly found in enzymatic active sites and heterogeneous catalysis, but they remain underexplored in porous solids. Now, sulfur-based ligands have been incorporated into metal–organic frameworks through post-synthetic modifications. The resulting sulfide MOFs exhibit enhanced catalytic performance in the selective hydrogenation of nitroarenes compared with their parent MOFs containing terminal or bridging chloride and hydroxyl groups.

Haomiao Xie
Milad Ahmadi Khoshooei
Omar K. Farha
Research24 Jul 2025
Nature Chemistry

Volume: 17, P: 1514-1523
Shape-selective sieving layers on an oxide catalyst surface

Templated atomic layer deposition (ALD) is used to create oxide ‘nanocavities’ on the surface of catalyst particles. Subnanometre-nm films containing nanocavities act as sieves for the underlying catalyst, resulting in high selectivities for the smaller of two reactants in competitive oxidations or reductions.

Christian P. Canlas
Junling Lu
Justin M. Notestein
Research28 Oct 2012
Nature Chemistry

Volume: 4, P: 1030-1036
High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

Andrew S. Rosen
Victor Fung
Randall Q. Snurr
ResearchOpen Access17 May 2022
npj Computational Materials

Volume: 8, P: 1-10

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Author Correction: Introducing metal–sulfur active sites in metal–organic frameworks via post-synthetic modification for hydrogenation catalysis

Introducing metal–sulfur active sites in metal–organic frameworks via post-synthetic modification for hydrogenation catalysis

Shape-selective sieving layers on an oxide catalyst surface

High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

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