Published February 18, 2022 | Version Submitted + Supplemental Material
Discussion Paper Open

Selective Electrochemical Reductive Amination of Benzaldehyde at Heterogeneous Metal Surfaces

  • 1. ROR icon Massachusetts Institute of Technology
  • 2. ROR icon California Institute of Technology

Abstract

Ammonia is one of the largest volume commodity chemicals, and electrochemical routes to ammonia utilization are appealing due to increasingly available renewable electricity. In this work, we demonstrate an electrochemical analogue to reductive amination for the synthesis of benzylamine from benzaldehyde and ammonia. Previous works on electrochemical reductive amination generally focus on proof-of-concept outer-sphere routes. We demonstrate an inner-sphere route, opening a large phase space of heterogeneous electrocatalysts that can direct selectivity and drive the reaction. In our system, imine hydrogenation proceeds on a silver electrocatalyst at ambient conditions in methanol with an initial Faradaic efficiency toward the primary amine product of ~80% and partial current greater than 4 mA/cm² at -1.96 V vs. Fc/Fc⁺ (-1.36 V vs. NHE). Silver was selected after evaluating diverse transition metal electrocatalysts, and with density functional theory, we found that the reaction rate on various metals is best described by the charge density distribution above the metal surface, independent of molecular adsorption energies. On silver, the catalyst that promotes amination with the highest Faradaic efficiency and one of the highest partial currents, the rate-determining step was found to be the initial electron transfer to the imine. Overall, this work on the kinetics of electrochemical reductive amination represents a step toward inner-sphere electrochemical reductive amination systems for the synthesis of amines that currently rely on thermochemical reductive amination.

Additional Information

The content is available under CC BY NC ND 4.0 License. This material is based on work supported by the National Science Foundation under Grant No. 1944007. The authors thank Nathan Corbin, Nikifar Lazouski, and Kindle Williams for useful discussions. The authors also thank Andrew Medford, Ben Comer, Robert Warburton, and Aditya Nandy for help with DFT. ZJS and KS also acknowledges a graduate research fellowship from the National Science Foundation under Grant No. 1745302. ZJS acknowledges funding from Chevron through the MIT Energy Initiative. The authors acknowledge the MIT SuperCloud and Lincoln Laboratory Supercomputing Center for providing HPC resources that have contributed to the research results reported within this paper. Author Contributions: Conceptualization, Z.J.S. and K.M.; Resources, Z.J.S. and K.M.; Data curation, Z.J.S.; Software, Z.J.S.; Formal Analysis, Z.J.S.; Supervision, Z.J.S. and K.M.; Funding acquisition, Z.J.S. and K.M.; Validation, Z.J.S. and M.C.; Investigation, Z.J.S.; Visualization, Z.J.S.; Methodology, Z.J.S. and K.M.; Writing – original draft, Z.J.S.; Writing – review & editing, Z.J.S., M.C., K.S., K.M.; Project administration, Z.J.S. and K.M. Data Availability. All raw data and analysis scripts will be available upon request to the corresponding author. There are no competing interests to declare.

Attached Files

Submitted - 10.26434_chemrxiv-2022-2s2z7.pdf

Supplemental Material - si.pdf

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Additional details

Identifiers

Eprint ID
114604
Resolver ID
CaltechAUTHORS:20220505-564477000

Funding

NSF
CBET-1944007
NSF Graduate Research Fellowship
DGE-1745302

Dates

Created
2022-05-06
Created from EPrint's datestamp field
Updated
2023-05-17
Created from EPrint's last_modified field