J
Jens K. Nørskov
Researcher at Technical University of Denmark
Publications - 723
Citations - 181092
Jens K. Nørskov is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Catalysis & Density functional theory. The author has an hindex of 184, co-authored 706 publications receiving 146151 citations. Previous affiliations of Jens K. Nørskov include Aarhus University & Fritz Haber Institute of the Max Planck Society.
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Journal ArticleDOI
Understanding the Reactivity of Layered Transition-Metal Sulfides: A Single Electronic Descriptor for Structure and Adsorption
TL;DR: The d-band center, εd, of the edge-most metal site at 0 ML sulfur coverage is a general electronic descriptor for both structure and adsorption energies, which are known to describe catalytic activity.
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Water Dissociative Adsorption on NiO(111): Energetics and Structure of the Hydroxylated Surface
Wei Zhao,Michal Bajdich,Michal Bajdich,Spencer J. Carey,Aleksandra Vojvodic,Aleksandra Vojvodic,Jens K. Nørskov,Jens K. Nørskov,Charles T. Campbell +8 more
TL;DR: In this paper, the enthalpy of dissociative adsorption of water is measured on NiO(111)-2 × 2 at 300 K using single-crystal adaption calorimetry.
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Nanoscale limitations in metal oxide electrocatalysts for oxygen evolution.
Venkatasubramanian Viswanathan,Venkatasubramanian Viswanathan,Venkatasubramanian Viswanathan,Katie L. Pickrahn,Alan C. Luntz,Stacey F. Bent,Jens K. Nørskov,Jens K. Nørskov +7 more
TL;DR: The theoretical analysis deriving a relation between the critical thickness and the location of valence band maximum relative to the limiting potential of the electrochemical surface process sets the optimum size of the nanoparticle oxide electrocatalyst and this provides an important nanostructuring requirement for metal oxide Electrocatalyst design.
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Predicting Chemical Reaction Barriers with a Machine Learning Model
Aayush R. Singh,Aayush R. Singh,Brian A. Rohr,Brian A. Rohr,Joseph A. Gauthier,Joseph A. Gauthier,Jens K. Nørskov,Jens K. Nørskov,Jens K. Nørskov +8 more
TL;DR: In this article, a machine learning approach was used to predict the most expensive and most important parameter in a catalyst's affinity for a reaction, i.e., the reaction barrier.
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Selectivity of Synthesis Gas Conversion to C2+ Oxygenates on fcc(111) Transition-Metal Surfaces
Julia Schumann,Julia Schumann,Andrew J. Medford,Andrew J. Medford,Jong Suk Yoo,Zhi-Jian Zhao,Zhi-Jian Zhao,Pallavi Bothra,Pallavi Bothra,Ang Cao,Felix Studt,Frank Abild-Pedersen,Jens K. Nørskov,Jens K. Nørskov +13 more
TL;DR: In this paper, a combined density functional theory and descriptor based microkinetic model approach was used to predict production rate volcanos for higher oxygenate formation on transition-metal surfaces.