Nanotechnology

New insights into bubble interference could enhance electrode design

(A) Diagram of the high-level ML architecture used for bubble identification. Note that the Mask R-CNN architecture has an additional convolutional layer to generate the mask, whereas Faster R-CNN does not. (B) Image of a b200 grid nucleation surface with oxygen bubble nucleation annotated with predictions and union (IoU) scores. Green indicates the highest IoU (≥0.9) and red indicates the lowest (<0.7). Bubble predictions were generated using the Faster R-CNN V2 architecture. See Table S1† for overall mAP and IoU results by model architecture. (C) Image of a b50 grid nucleation surface with oxygen bubble nucleation. Predictions and IoU scores are also annotated. (D) Subset of inset (B) highlighting bubble predictions with different IoU scores. Note that bubble 105 with IoU < 0.7 is a false positive bubble after detachment from the surface. Only attached bubbles are considered valid predictions. Predictions with low scores are often false positives or small bubbles. Credit: Nanoscale (2024). DOI:10.1039/D4NR02628D

Industrial electrochemical processes that use electrodes to produce fuels and chemicals are hampered by the formation of air bubbles that block part of the electrode surface, reducing the area available for active reactions. Such clogging reduces electrode performance by 10-25%.

But new research reveals decades of misconceptions about the extent of that interference. This discovery shows exactly how blocking effects work and could lead to new ways to design electrode surfaces to minimize inefficiencies in these widely used electrochemical processes. There is a gender.

It has long been thought that the entire area of ​​the electrode shadowed by each bubble is effectively inerted. However, it turns out that a much smaller area, corresponding to the area where the bubble actually contacts the surface, is blocked from electrochemical activity. New insights can lead directly to new ways to pattern surfaces that minimize contact area and improve overall efficiency.

The discovery was reported today in the journal Nanoscale in a paper by Dr. Jack Lake, a recent MIT graduate. ’23, University of Chicago and Argonne National Laboratory graduate student Simon Rufer, mechanical engineering professor Kripa Varanasi, research scientist Ben Blazik, and six others. The team has made available an open source AI-based software tool. Engineers and scientists can now use this tool to automatically recognize and quantify air bubbles formed on specific surfaces as a first step in controlling the properties of electrode materials. .

Credit: Massachusetts Institute of Technology

Gas-generating electrodes often feature catalytic surfaces that promote chemical reactions and are used in a wide range of processes, including the production of “green” hydrogen without fossil fuels, carbon capture processes that can reduce greenhouse gas emissions, and aluminium. used in Chlor-alkali process used in manufacturing and manufacturing of widely used chemical products.

These are very extensive processes. The chlor-alkali process alone accounts for 2% of all electricity usage in the United States. Aluminum production accounts for 3% of the world’s electricity. And as the world strives to meet greenhouse gas reduction goals, both carbon capture and hydrogen production are likely to grow rapidly in the coming years. Therefore, Varanasi says, new discoveries could lead to big changes.

“Our research shows that engineering the contact and growth of bubbles on electrodes can have dramatic effects on how bubbles form and how they leave the surface. “It shows that,” he says. “The knowledge that the region beneath the bubble can be highly active leads to a new set of design rules for high-performance electrodes to avoid the negative effects of bubbles.”

“The extensive literature that has built up over the past few decades suggests that the entire area beneath the bubble becomes passivated, not just that small contact area,” Rufer says. The new study found that “there are significant differences between the two models due to changes in the way the electrodes are developed and designed to minimize these losses.”

To test and demonstrate the impact of this effect, the research team created different versions of the electrode surface with patterns of dots that nucleate and trap air bubbles at different sizes and spacings. They were able to show that surfaces with widely spaced dots promote large bubble sizes, but the area of ​​surface contact is negligible, and this is in contrast to the expected and actual effects of bubble coverage. It helped clarify the difference.

The team’s analysis required developing software to detect and quantify bubble formation, Rufer explains. “We wanted to collect a lot of data and look at different electrodes, different reactions, different bubbles, and they all look slightly different,” he says. Creating a program that can reliably identify and track bubbles across different materials and different lighting was a difficult process, he says, and machine learning was key to making it work. .

Using the tool, he said, they were able to collect “an extremely large amount of data about the bubbles on the surface, the location of the bubbles, the size of the bubbles, the rate of growth, and many other things.” The tool is currently available for free to everyone via a GitHub repository.

By using their tools to correlate visual measurements of bubble formation and evolution with electrical measurements of electrode performance, the researchers disproved the established theory and showed that only the area of ​​direct contact is affected. I was able to show it. The video further proved the point, revealing new bubbles actively evolving directly beneath some of the larger bubbles.

Researchers have developed a very general methodology that can be applied to characterize and understand the effects of gas bubbles on the surfaces of electrodes and catalysts. They were able to quantify the bubble passivation effect with a new performance metric called BECSA (Bubble-induced Electrochemically Active Surface), as opposed to ECSA (Electrochemically Active Surface Area), which is used in the field. It’s done. “The BECSA metric was a concept defined in previous research, but until this study there was no effective estimation method,” says Varanasi.

The knowledge that the region beneath the bubble can be highly active leads to a new set of design rules for high-performance electrodes. This means that electrode designers should strive to minimize the bubble contact area, rather than simply the bubble coverage. This can be achieved by controlling the morphology and chemistry of the electrodes.

Surfaces designed to control air bubbles can not only improve the overall efficiency of the process and reduce energy usage, but also save on initial material costs. Many of these gas-generating electrodes are coated with catalysts made from expensive metals such as platinum or iridium, and the findings from this study could lead to electrode designs that reduce material wasted due to air bubbles that interfere with the reaction. Can be used for

Varanasi said: “The insights gained from this research could inspire new electrode structures that not only reduce the use of valuable materials but also improve the overall performance of electrolyzers.” Both would have large-scale environmental benefits.

More information: Jack R. Lake et al, Discovery of gas-evolving electrode bubble passivation through machine learning, Nanoscale (2024). DOI: 10.1039/D4NR02628D, pubs.rsc.org/en/content/articl … g/2024/nr/d4nr02628d

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Citation: New insights into bubble interference could enhance electrode design (October 8, 2024) from https://phys.org/news/2024-10-insights-electrode.html 10/2024 Retrieved on March 8th

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