Data Pipeline for High-Throughput Parallel Electrochemical CO2 Reduction
Nukorn Plainpan a, Alessandro Senocrate a, Francesco Bernasconi a b, Peter Kraus a c, Corsin Battaglia a b d e
a Empa, Swiss Federal Laboratories for Materials Science and Technology, 8600 Dubendorf, Switzerland
b ETH Zürich, Department of Materials, 8093 Zürich, Switzerland
c Technische Universitat Berlin, Centre for Advanced Ceramic Materials, Hardenbergstr. 40, 10623 Berlin, Germany
d ETH Zürich, Department of Information Technology and Electrical Engineering, 8093 Zürich, Switzerland
e EPFL, School of Engineering, Institute of Materials, 1015 Lausanne, Switzerland
Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
#PECCO2 - Advances in (Photo)Electrochemical CO2 Conversion to Chemicals and Fuels
Lausanne, Switzerland, 2024 November 12th - 15th
Organizers: Deepak PANT, Adriano Sacco and juqin zeng
Oral, Nukorn Plainpan, presentation 069
Publication date: 28th August 2024

A promising technology to foster the transition to net-zero carbon emissions is the electrochemical reduction of CO2 (eCO2R), which can convert CO2 into climate-neutral fuel and other economically valuable compounds.[1] Despite the potential of eCO2R, there are numerous areas where electrolyzer and catalyst efficiency and stability could be improved, thereby necessitating further research. eCO2R yields multiple products both gaseous and liquid at room temperature.[2] The results can be influenced by a variety of factors, including the choice of catalyst, substrate type, applied potential, electrolyte pH, and electrolyzer design.[3] The wealth of experimental data, combined with the complexity of the data formats, often result in the loss of various aspects of precious experimental data. This complexity highlights the importance of having a strong framework for data acquisition and analysis. In this work, we introduce our high-throughput experimental setup for eCO2R.[4] Our setup comprises 10 parallel electrolyzers, each controlled individually using a dedicated potentiostat channel. The system is equipped with multiple mass flow controllers and meters, temperature sensors, and pressure sensors to monitor experimental conditions. Online GC and LC are also incorporated to periodically analyze the products from the electrolyzers. Our setup generates heterogeneous but rich data in high volumes. To manage this, we have developed an open-source software package that automatically synchronizes multiplexed data chronologically and automates the data analysis.[5] This software ensures that our data adheres to the FAIR principle (findability, accessibility, interoperability, and reusability). Designed with modularity in mind, our software allows us to introduce additional electrolyzers and diagnostic instruments with minimal impact on the analysis workflow. We anticipate that our setup and workflow will inspire the development of other high-throughput experimental setups for eCO2R, thereby accelerating research in this challenging field.

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