Research Highlight

The Liu Group: Electrochemical interfaces

Nanostructure Electrodes for Ambient Methane Activation

Abstract Image

“A solution catalytic cycle of incompatible steps for ambient air oxidation of methane to methanol”, Natinsky, B.; Lu, S.; Copeland, E.◊; Quintana, J.◊; Liu, C.*, ACS Cent. Sci., 2019, 5, 1584−1590.

Graphical abstract: Efficacy analysis of compartmentalization for ambient CH4 activation mediated by a RhII metalloradical in a nanowire array electrode

“Efficacy analysis of compartmentalization for ambient CH4 activation mediated by RhII metalloradical in nanowire array electrode”, Natinsky, B. S.†; Jolly, B. J.†; Dumas, D. M.◊; Liu, C.*; Chem. Sci.2021, DOI: 10.1039/D0SC05700B.

Oxidation of methane to methanol under ambient conditions is attractive yet difficult. Our group has demonstrated that the application of a nanowire array electrode enables efficient Rh-porphyrin mediated CH4-to-CH3OH conversion at ambient conditions, which otherwise proceeds negligibly. As a follow-up to this report, we determined the nanowire array’s efficacy in the context of compartmentalized cascade, which suggests that the nanowire created anaerobic domain may be regarded as a compartment ensuring efficient Rh(II) channeling.

Hybrid Biological-Inorganic Systems for Nitrogen Reduction

figure1

“Electricity-Powered Artificial Root Nodule”, Lu, S.; Guan, X.; Liu, C.*, Nature Commun., 2020, 11, 1505

Biological-inorganic hybrid systems use sustainable energy to efficiently drive chemical reactions. In another effort to exploit a nanowire array electrode’s ability to generate local oxygen gradients, our group constructed an electricity-powered artificial root nodule for nitrogen fixation, which houses both the O2 gradient and symbiotic diazotrophic bacteria found in its natural counterpart.

Machine Learning for Electrocatalysis

Abstract Image

“Machine-Learning Enabled Exploration of Morphology Influence on Wire-Array Electrodes for Electrochemical Nitrogen Fixation”, Hoar, B. B.; Lu, S.; Liu, C.*, J. Phys. Chem. Lett.202011, 4625−4630

Our group recently developed a neural network model that predicts the electrocatalytic activities of N2 reductions on wire array electrodes, shortening the time of morphology optimization by about 1000. This work has implications for real time design and optimization of electrode morphology for catalytic applications.

Previous
Group Highlight: Prof. Alexander Miller
The Miller Group: Organometallic chemistry
Group Highlight: Prof. Dunwei Wang
The Wang Group: Heterogenous catalysis
Group Highlight: Prof . Chong Liu
The Liu Group: Electrochemical interfaces
Group Highlight: Prof. Jeffrey A. Byers
The Byers Group: Organometallic chemistry
Group highlight: Prof. Paula Diaconescu
The Diaconescu Group: Redox switchable catalysis
Group Highlight: Prof. Loi Do
The Loi Do’s group: organometallic chemistry of olefin polymerization