We use cookies to enhance your experience. By continuing to browse this site you agree to our use of cookies. More info.

The oxidation kinetics and corrosion of Zr alloys are highly dependent on the porosity exhibited by the zirconia (ZrO2) layers.

​​​​​​​​​​​​​​​​​​​ ​​​​​​​Study: Nano-Porosity effects on Corrosion Rate of Zr Alloys Using Nanoscale Microscopy Coupled to Machine Learning. Image Credit: Korawat photo shoot/Shutterstock.com

In a study published in Corrosion Science, the team exposed Zircaloy-4 to oxidation for about three months. Transmission electron microscopy (TEM) was used to evaluate porosity on a nanoscale, which was then accurately quantified using manual counting and a novel machine learning algorithm based on grayscale value differences.

Zr alloys are the materials of choice for use inside the cores of water-cooled reactors thanks to their small thermal neutron absorption cross-sectional area and good corrosion resistance in high-temperature water.

Zircaloy-4 offers the highest resistance against corrosion and minimum hydrogen uptake among the Zr alloys.

The hostile conditions inside reactors result in non-negligible oxide formation and substantial uptake of associated hydrogen.

Understanding and improving the resistance against corrosion in Zr alloys, and in particular, Zircaloy-4, has important economic and safety benefits.

The kinetics of Zircaloy-4 corrosion is typically sub-parabolic, wherein the rate of passivation through oxide buildup continues to diminish until a threshold thickness level is reached.

A shift in the corrosion kinetics, referred to as the transition, occurs at the threshold oxide thickness level.

The oxide protection quickly dissipates when the transition is reached, as revealed by weight gain observations. A new passivating oxide layer forms within the metallic substrate, leading to distinctive cyclic kinetics of the oxidation process.

When the Zr alloy undergoes oxidation, various phases, microstructures, stresses, and textures are produced in the oxide scale.

The rate of oxide formation is primarily regulated by the influx of oxygen and out flux of electrons via diffusion. Hydrogen and oxygen can both diffuse through the oxide layer.

Diffusion may take place at the grain boundaries and the accompanying pores on the oxide layer; therefore, the texture of the oxide layer is a key element that influences the diffusion mechanism. The oxide texture is linked with the texture of the Zr alloy.

The oxide layer protects the Zr alloy from subsequent oxidation and hydrogen influx. Knowledge of texture development in the oxide layer is crucial for understanding the mechanism and extent of this protection.

The accurate quantification and qualitative development of the pores in zirconia are critical because they play key roles in comprehending the oxidizing and hydriding processes of Zr alloys.

So far, the porosity of zirconia has been quantified to a small extent via the manual counting of pores found in TEM imaging. Unfortunately, this method is time intensive and vulnerable to human error.

Machine learning (ML) approaches, as substitutes to human observation descriptors, have been widely employed to determine nanoscale characteristics via automatic extraction of these characteristics from multidimensional data sources.

Machine learning approaches are ideal for quantifying pores in zirconia, characterizing pore density more effectively, and even predicting the formation of pores in zirconia under various corrosion settings.

The team used a set of manually counted TEM images of zirconia to train the machine learning model. Different properties, including oxide porosity, oxide fractures, and grains in the metallic substrate, served as the training data for the machine learning model.

The nanoscale distribution of porosity was then correlated with localized oxide texture, establishing a strong association between nanoscale porosity and oxide grain boundary misalignments.

The nanoscale porosity and oxide texture were finally examined with respect to the corrosion rate, corrosion temperature, and texture of the substrate. The goal was to comprehend their correlations and determine the best strategy to enhance the resistance against corrosion in Zr alloys.

Using machine learning-based approaches, the researchers evaluated the impacts of oxide texture, temperature, and exposure duration, on nanoscale porosity and suboxide formation in corroded beta-quench Zircaloy-4 alloy.

They discovered that the orientation of the metallic grains had a significant impact on oxide porosity, oxide texture, and corrosion rate.

The stress-driven formation was prevalent between 25° and 75° from the direction of oxide growth, while lattice-match formation was prominent in the remaining orientations.

Reduced porosity and reduced corrosion rates were reported in the stress-driven formation mode, whereas larger porosity and increased corrosion rates were seen in lattice-match formation mode.

The texture of the metallic substrate and the oxide formation mode could significantly impact pore concentration and grain boundary misalignment in the resulting oxide, strongly influencing corrosion behavior.

This strategy may be used to generate superior corrosion-resistant materials by tailoring suitable textures using certain material processing approaches.

Zhang, H., Kim, T. et al. (2022). Nano-Porosity effects on Corrosion Rate of Zr Alloys Using Nanoscale Microscopy Coupled to Machine Learning. Corrosion Science. Available at: https://linkinghub.elsevier.com/retrieve/pii/S0010938X22005789

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Shaheer is a graduate of Aerospace Engineering from the Institute of Space Technology, Islamabad. He has carried out research on a wide range of subjects including Aerospace Instruments and Sensors, Computational Dynamics, Aerospace Structures and Materials, Optimization Techniques, Robotics, and Clean Energy. He has been working as a freelance consultant in Aerospace Engineering for the past year. Technical Writing has always been a strong suit of Shaheer's. He has excelled at whatever he has attempted, from winning accolades on the international stage in match competitions to winning local writing competitions. Shaheer loves cars. From following Formula 1 and reading up on automotive journalism to racing in go-karts himself, his life revolves around cars. He is passionate about his sports and makes sure to always spare time for them. Squash, football, cricket, tennis, and racing are the hobbies he loves to spend his time in.

Please use one of the following formats to cite this article in your essay, paper or report:

Rehan, Shaheer. (2022, September 14). Machine Learning Predicts Porosity Effects on Zr Alloys Corrosion. AZoNano. Retrieved on September 23, 2022 from https://www.azonano.com/news.aspx?newsID=39679.

Rehan, Shaheer. "Machine Learning Predicts Porosity Effects on Zr Alloys Corrosion". AZoNano. 23 September 2022. <https://www.azonano.com/news.aspx?newsID=39679>.

Rehan, Shaheer. "Machine Learning Predicts Porosity Effects on Zr Alloys Corrosion". AZoNano. https://www.azonano.com/news.aspx?newsID=39679. (accessed September 23, 2022).

Rehan, Shaheer. 2022. Machine Learning Predicts Porosity Effects on Zr Alloys Corrosion. AZoNano, viewed 23 September 2022, https://www.azonano.com/news.aspx?newsID=39679.

Do you have a review, update or anything you would like to add to this news story?

In this interview, AZoNano discusses the development of a novel solid-state optical nanomotor, which is driven by light.

In this interview, we discuss a nanoparticle ink used to produce low-cost printable perovskite solar cells, helping to catalyze the technology transition toward commercial viable perovskite-based devices.

We speak with researchers behind the latest advancement in graphene hBN research that could boost the development of next-generation electronic and quantum devices.

Inoveno’s PE-550 is a best-selling electrospinning/spraying machine that can be used for the continuous production of nanofibers.

The Filmetrics R54 advanced sheet resistance mapping tool for semiconductor and compound semiconductor wafers.

The Filmetrics F40 turns your benchtop microscope into an instrument for measuring thickness and refractive index.

AZoNano.com - An AZoNetwork Site

Owned and operated by AZoNetwork, © 2000-2022