Parallel experiments in electrochemical CO2 reduction enabled by standardized analytics
Alessandro Senocrate a b, Francesco Bernasconi a c, Peter Kraus a, Nukorn Plainpan a, Jens Trafkowski d, Fabian Tolle d, Thomas Weber d, Ulrich Sauter d, Corsin Battaglia a b c e
a Empa, Swiss Federal Laboratories of Materials Science and Technology, Switzerland
b ETH Zürich, Department of Information Technology and Electrical Engineering, 8093 Zürich, Switzerland
c ETH Zürich, Department of Materials, 8093 Zürich, Switzerland
d Agilent Technologies (Switzerland), 4052 Basel, Switzerland
e EPFL, School of Engineering, Institute of Materials, 1015 Lausanne, Switzerland
Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
#SOLTEC - Solar Technologies for Renewable Fuels and Chemicals: On the Way to Industrial Implementation
Lausanne, Switzerland, 2024 November 12th - 15th
Organizers: Víctor A. de la Peña O'Shea and Miguel García-Tecedor
Invited Speaker, Alessandro Senocrate, presentation 399
DOI: https://doi.org/10.29363/nanoge.matsusfall.2024.399
Publication date: 28th August 2024

The electrochemical reduction of CO2 (eCO2R) is a promising way to convert detrimental CO2 emissions into sustainable fuels and chemicals, and thus to achieve a net zero or net negative carbon economy.1 Depending on catalyst type and reaction conditions, different gaseous and liquid products can be obtained during eCO2R, with their production rates significantly varying over time. Thus, for any catalytic performance analysis, it is key to assess the product distribution online during eCO2R.2,3 However, while gaseous products are readily analyzed online by gas chromatography, liquid products are typically only assessed at the end of the reaction due to the lack of suitable automated liquid sampling and analysis methods. In addition, relevant reaction parameters such as CO2 flow rates, electrolyzer temperatures and flow pressures are seldom recorded, causing the loss of important information on catalysts, electrodes, and electrolyzer behavior.

In this work, we overcome these issues by assembling a comprehensive analytical system coupling online gas and liquid product analysis by gas and liquid chromatography (leveraging a special automated liquid sampling valve) with electrochemical protocols that assess CO2RR performance, electrolyzer cell resistance and electrode surface area. In addition, we record CO2 volumetric flow rates, electrolyzer temperatures, as well as gas and liquid flow pressures.4 To rapidly and reproducibly handle the large and heterogeneous data volume obtained we implement a standardized data pipeline based on our own open-source software,5 which automatically parses the numerous different raw data files, composes a data set following FAIR practices,6 and post-processes and plots the data in a standard way. We validate this analytical system by carrying out eCO2R at 200 mA/cm2 on Cu gas diffusion electrodes, following the changes in selectivity with reaction time for > 12 gaseous and liquid products while recording volumetric flow rates, electrolyzer cell resistance, electrode surface area, electrolyzer temperatures and flow pressures. The modular nature of our analytical system, combined with the standardized data pipeline, allows us to freely increase the number and type of sensors used with minimal impact on the data analysis time, as well as to multiplex our analysis to 8 parallel electrolyzer cells, providing a deep understanding of the function of eCO2R catalysts, electrodes, and electrolyzers, and paving the way to accelerated discoveries.

 

References

[1] Senocrate, Battaglia, J. Energy Storage 2021, 36, 102373.

[2] Wang, de Araújo, Ju, Bagger, Schmies, Kühl, Rossmeisl, Strasser, Nat. Nanotechnol. 2019, 14, 1063. [3] Löffler, Khanipour, Kulyk, Mayrhofer, Katsounaros ACS Catal. 2020, 10, 6735.

[4] Senocrate, Bernasconi, Kraus, Plainpan, Trafkowski, Tolle, Weber, Sauter, Battaglia, Nat. Catal. 2024, 7, 742.

[5] https://dgbowl.github.io

[5] Kraus, Vetsch, Battaglia, Journal of Open Source Software 2022, 7, 4166.

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