Electrode Lifecycle Enhancement through Computational Testing and Research Automation

[mgramlich]

The development of catalysts for alkaline electrolysis (AEL), such as Raney-Nickel electrodes, is a time-consuming task which involves a lot of manual work and a large number of long experiments. To speed up the electrochemical experiments, this project aimed to explore both, lab automation techniques and machine learning. Lab automation is intended to decrease the time needed for the setup of one experiment. Machine Learning, for which the approach of Bayesian Optimization was used, is meant to reduce the measurement time, which is needed to characterize a certain catalyst material. All in all, this project aims to reduce the effort of the characterization of one material regarding its suitability as a catalyst for alkaline electrolysis.

Activities

Activities involved a custom, 3D printed electrochemical cell and the development of a script for performing Bayesian optimization to determine optimal parameters.

Lab automation – custom electrochemical cell

A custom, 3D printable electrochemical cell, in which material characterization in three electrode setup can be performed, was designed and printed. The cell is optimized for being low cost, allowing quick and automated setup of experiments and having a high degree of reproducibility. To develop this design, a team of three persons worked together: Sterling Baird, myself, and Bojan Micevski (a technical designer). In a public github issue, all relevant aspects of the first design were discussed and test results were shared. During the time of this project, 3 cell designs and 2 designs for the sample holder were printed. This way, numerous improvements could be implemented within the prototyping stage at high development speed. The prints were carried out with two Stereolithography printers owned by the Acceleration Consortium and two printers owned by the National Research Council (NRC). The material compatibility of the resin from which this cell and sample holder were printed, was tested within this project for the “clear v4” and “rigid 10k” resin provided by Formlabs. Cubes with a volume of 1cm³ each were printed of both materials and were exposed to concentrated KOH solution (30w%). These tests were carried out both, at room temperature and at 80°C. The mass of the test cubes was measured before, during and after the exposure to KOH solution to evaluate any mass loss due to degradation within KOH solution.

You can see the cell in operation at the back of this picture:

Machine Learning for electrolysis life cycle testing

A script was developed for creating an accelerated stress test protocol. By running multiple experiments with the same material with stress parameters selected by the Bayesian Optimization algorithm, the “worst” parameters for a given material are intended to be found. This way a protocol can be established, which is known to stress the electrode to the maximum so that within a short experiment the performance of a given material under maximal stress can be evaluated. To create this script, the search space was defined by specifying the parameters and their constraints. Once this was completed, the Honegumi tool along with the Bayesian Optimization library “Ax Platform” were used to create the actual optimization script. The Bayesian Optimization script for developing this test protocol was completely written during this project. The actual experiments remain to be run in the labs of IFAM Dresden. If this approach turns out to be successful, it could greatly reduce the time for long term stability tests.

Scientific results

A 3D printed electrochemical cell was successfully designed, and a functioning machine learning script was written. Experiments using the printed cell and as suggested by the script are planned.

Lab automation – custom electrochemical cell

Tests in 3 electrode setup for the characterization of a material as a catalyst for AEL need to be carried out in a certain electrolyte solution. This electrolyte needs to be exchanged between all experiments to provide the same conditions at the beginning of each experiment. The cell design developed allows to automate this step completely. The electrolyte can be removed with a pump via an inlet into the cell and a subsequent washing procedure can be applied via an integrated washing nozzle in the inside of the cell which is connected to external water supply. The water of this cleaning step can once again be removed with the previously described pump. Next, fresh electrolyte can be filled into the cell through another inlet. Temperature control is implemented through double wall design, which allows an externally heated thermal fluid to be flowing between the inner and outer wall, thereby heating the electrolyte. Supply of inert gas into the electrolyte to purge solved oxygen is realised through a gas inlet tube which is connected to the cells tap. A Harber-Luggin-Capillary is also integrated into the tap, so that a HydroFlex Reversibel Hydrogen Electrode (Gaskatel) can be easily inserted into the setup. The sample holder, into which the tested material is mounted was designed from scratch to optimize it for the given requirements. These are: Electrical contact between the sample and the external Potentiostat, no contact between any cable/wire and the electrolyte and mechanically reliable mounting of the sample. The design created in this project fulfils all these requirements by combining 3D printed parts with several seals. The sample holder is the only part, which needs to be removed between experiments. Thanks to it being printable with an SLA printer, multiple sample holders can be easily printed, so that it’s easy to prepare several samples at once to reduce downtime between experiments.

Machine learning for electrolysis life cycle testing

The general idea of this approach is to stress a sample during a fixed period of time under discontinuous conditions as it is known that rapid changes in load can contribute to the altering of an electrode material. The time over which a specific current density is applied effects the material as well as the changes in current density. E.g. forcing the Hydrogen Evolution Reaction to occur at the electrodes surface with a current density j=0.5A/-cm² for 2 hours will stress the electrode material differently than running the HER with j=0.4A/cm² for 1h and subsequently with j=0.6A/cm² for 1h, even though the net amount of produced hydrogen is the same. It is of interest to have a test protocol which is known to stress the electrode material significantly in a short period of time, which involves numerous load changes. However, developing such a protocol would be a highly time consuming task as it would involve running a great number of possible test protocols if the number of cycles and their corresponding current density would be tested. That’s why Bayesian Optimization for the development of such a protocol was chosen.

The developed optimization protocol follows the following general structure:

1. Preconditioning (1h) to get the sample into a constant electrochemical state

2. Stress phase (12h)

3. Characterization phase (ca. 2h)

The stress phase will consist of a maximum of n cycles with varying time and current densities each. Current densities are allowed between -2 A and 0A, where 0A are effectively applied as OCP. The time of each cycle can be between 1s and the maximum stress phase time of 12h. The exact number of cycles, corresponding time intervals and current densities are chosen by the Bayesian Optimization Algorithm.

The characterization phase will consist of three steps, which analyse the electrochemical behaviour of the electrode material after the stress phase. These steps contain a galvanostatic step with -0.5A/cm² for 30 min, a Tafel-Analysis with measurements between 0 and -1A/cm² and a cyclovoltammetric step for the measurement of the double layer capacity. The results of this analysis phase are hence the overpotential, Tafel-slope, exchange current density and double layer capacity. These results will then be reported back to the optimization algorithm, which in turn will calculate the stress parameters of the next test protocol.

### Main achievements

• Prototype of a fully equipped and automatable custom electrochemical cell

• Many lessons learned from the design process of the custom cell. This experience is highly valuable for the next iterations of the design and is already speeding up the development significantly

• Script for the development of an accelerated stress testing protocol for electrochemical analysis of electrode materials in a 3-electrode setup

• Successful completion of a course from the Acceleration Consortiums educational program

• Many discussions with scientists form University of Toronto, the Acceleration Consortium and the National Research Council. Several ideas for collaborations have already grown from these encounters.

Data management

All data created within this project is available on GitHub within the AccelerationConsortium/ac-training-lab repository. Data about the electrochemical tests in the custom cell will be published in the corresponding issues, too. Custom electrochemical cell (v1): Custom electrochemical corrosion cell for electrocatalysis experiments (v1) · Issue #62 · AccelerationConsortium/ac-training-lab · GitHub Custom electrochemical cell (v2): Custom electrochemical cell (v2) - enhancements and automation · Issue #104 · AccelerationConsortium/ac-training-lab · GitHub Machine learning for life cycle testing: ML for electrolysis life cycle testing · Issue #98 · AccelerationConsortium/ac-training-lab · GitHub

We plan to move the relevant issues from the training lab repository to GitHub - AccelerationConsortium/echem-cell: A custom, 3D printed electrochemical cell for autonomous electrochemistry, targeted at fuel cell electrolysis studies. and flesh out documentation and other details. This repo is currently a placeholder (as of 2025-04-04).

[sgbaird]

Thanks, @mgramlich! So good to work with you on this. Recently, I came across another e-chem cell setup (one for CO2 electrolysis)

Best practices for automated electrochemistry (tool and workflow design)

Neat electrochemical cell design by @dlin and folks as part of a workflow involving “seven robots with automated modules for gas diffusion electrode fabrication and characterization and an automated test cell (ATC) that performs zero-gap CO2 electrolysis”: [image] Comparison of the models of the automated test cell (ATC) described herein and a manually assembled electrolyzer. The ATC is structured similarly to a manually assembled electrolyzer using the same stack of flow plates, cathode, …

@nipun.0092 has also been working on some custom e-chem cell setups with 3D printed parts and is interested in aligning our separate efforts (at the very least we can share challenges and thoughts).

[mgramlich]

oh wow, that´s quite impressive.
Sure thing, we can align our efforts. Let me know, how we can to that best.

[nipun.0092]

Thanks Sterling! I’d be happy to combine efforts here.

Our team has been working on creating a budget-friendly HT echem platform designed for screening (cyclic voltammetry (CV), pulsed voltammetry (PV)) and electrocatalysis (chronopotentiometry or chronoamperometry). While a reference electrode and a glassy carbon electrode are needed for CV and PV modes, graphite rods can often suffice for the electrocatalysis workflow. We integrate commercially available 20 mL glass beakers, graphite rods, and 3D-printed cell caps to keep the total cost per cell under $25, as shown in Fig. 1A. We also designed the cell caps to sit on top of the glass beakers near the pipettes on an Opentrons liquid dispenser, as shown in Fig. 1B. If an inert atmosphere is desired, the cell cap has a few holes to run an N2 (or Ar) line under positive pressure. Our setup can run up to six electrocatalysis reactions in parallel.

Figure 1: Panel (A) shows the echem cell, with a close-up of the cap shown in (B). After the electrodes are inserted, the setup looks similar to panel (C), showing the graphite rod electrodes with nails drilled into them to make the electrical connection.
Making the connection between the electrode and the potentiostat is often challenging, and we’re seeking ideas to enhance our ability to establish these connections (see the electrodes in Fig. 1C). For now, I’ve drilled nails into the electrodes, which seems to work well (I checked the resistance between the end of the graphite rod and the nail with a potentiostat, and it’s consistently <10Ω).

We have designed two solutions to improve the stirring of your electrocatalysis reaction. Our team has redesigned the stir plate module (which was developed with assistance from SDL5 at the AC) to hold six vials simultaneously (see Fig. 2A). The other solution to assist in stirring your reaction vessel is a 3D-printed vial holder that fits on an IKA stir plate (see Fig. 2B), and the design can be modified for any stir plate that you might have access to.

Figure 2: Panel (A) shows the six-vial stir plate that has been adapted from a design by SDL5, and (B) shows a six-vial holder that sits atop a stir plate

At times, it is desirable in electrocatalysis to reduce uncontrolled mass transport between the anode and the cathode, or vice versa. We designed a membrane-separated compartment that fits inside the beaker to achieve this, as shown in Fig. 3. The compartment can house the anode (or cathode) compartment, and the presence of the membrane hinders the transport of errant species, thereby preventing side reactions and electrode fouling. The compartment is also designed to be moved by the pipettes on an Opentrons liquid dispenser and is 3D-printed. The choice of the 3D-printed compartment is informed by the solvent used in the electrocatalysis workflow.

Figure 3: (A) An Autocad visualisation of the compartment to separate the anode from the cathode electrodes in an electrocatalysis cell, and (B) how the compartment looks after it has been inserted into the cell

[mgramlich]

hey @nipun.0092, thanks a lot for sharing details about your project. It looks very interesting, what you have been working on so far. If I may, I would like to ask a couple of things about your setup:

• Do you always intend to work with a static electrolyte or will there be an option for a flowing electrolyte?
• Which potentiostat do you use and did you manage to control it via a (python) API?
• What kind of reference electrode do you use?

About the topic with connecting the potentiostat to the electrodes, we already have spent quite some time in looking for solutions as this is a surprisingly challenging task. So far we have been thinking about either connecting the potentiostat permanently to the cell and then establish electircal contact between the sample holder and the cell or designing some kind of mechanism which allows the connection to be established with a robotic arm. However, as we had more pressing issues to solve so far, we have not decided for a final design yet. Once we tackle this task again, I’ll happily share our ideas here.