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Author: Gauss Supercomputing Center

Graphene may be one of the most exciting scientific discoveries of the last century. Although we are very familiar—graphene is considered an allotrope of carbon, which means that it is essentially the same substance as graphite, but has a different atomic structure—graphene also opens up new technologies for designing and constructing A new world of possibilities.

This material is two-dimensional, which means that each "sheet" of graphene is only 1 atom thick, but its bonds make it as strong as some of the hardest metal alloys in the world, while remaining light and flexible. This valuable and unique combination of properties has aroused the interest of scientists in a wide range of fields, leading to the use of graphene in new coatings for next-generation electronic products, industrial instruments and tools, and research on new biomedical technologies.

Perhaps it is the huge potential of graphene that has led to one of its biggest challenges-graphene is difficult to produce in large quantities, and the demand for this material is increasing. Recent studies have shown that the use of liquid copper catalysts may be a fast and effective way to produce graphene, but researchers have limited understanding of the molecular interactions that occur at these short and chaotic moments that lead to the formation of graphene, which means they It has not been possible to reliably produce flawless graphene sheets using this method.

To meet these challenges and help develop methods to produce graphene faster, a team of researchers at the Technical University of Munich (TUM) has been using JUWELS and SuperMUC-NG high-performance computing (HPC) systems (JSC) and Lai The Bunitz Supercomputing Center (LRZ) runs high-resolution simulations of graphene formation on liquid copper.

Enter the experiment window

The attractiveness of graphene mainly stems from the material's perfect and uniform crystal structure, which means that it is a waste of effort to produce graphene with impurities. For laboratory environments or situations where only a small amount of graphene is needed, researchers can put a piece of scotch tape on the graphite crystal and use a technique similar to using tape or other adhesives to "peel off" the atomic layer of graphite to help remove clothes. Pet hair. Although this reliably produces a flawless graphene layer, the process is slow and impractical to create graphene for large-scale applications.

The industry needs methods that can reliably produce high-quality graphene cheaper and faster. One of the more promising methods being studied involves the use of liquid metal catalysts to promote the self-assembly of carbon atoms from molecular precursors into individual graphene sheets grown on top of liquid metal. Although this liquid can effectively expand the scale of graphene production, it also brings many complications, such as the high temperatures required to melt the typical metals used (such as copper).

When designing new materials, researchers use experiments to observe how atoms interact under various conditions. Although technological progress has opened up new ways to understand atomic-scale behavior, even under extreme conditions such as extremely high temperatures, experimental techniques do not always allow researchers to observe ultra-fast reactions (or reactive reactions) that promote correct changes in the atomic structure of materials. Which aspects may introduce impurities). This is where computer simulation can help, but simulating the behavior of dynamic systems (such as liquids) is not without its own complexity.

"The problem with describing things like this is that you need to apply molecular dynamics (MD) simulations to get the correct samples," Anderson said. "Of course, there is also the size of the system-you need to have a system large enough to accurately simulate the behavior of liquids." Unlike experiments, molecular dynamics simulation allows researchers to observe events that occur on the atomic scale from a variety of different perspectives. , Or pause the simulation to focus on different aspects.

Although MD simulations provide researchers with insights into the motion and chemical reactions of individual atoms that cannot be observed in experiments, they also face their own challenges. The most important one is the trade-off between accuracy and cost-when relying on accurate ab initio methods to drive MD simulations, it is the computational cost of obtaining a large enough and long enough simulation to accurately simulate these reactions in a meaningful way very high.

Andersen and her colleagues used approximately 2,500 cores on JUWELS for the most recent simulation in the past month or so. Despite a lot of computational work, the team can only simulate about 1,500 atoms in picoseconds. Although these numbers may not sound big, these simulations are the largest of an ab initio MD simulation of graphene on liquid copper. The team uses these highly accurate simulations to help develop cheaper methods to drive MD simulations so that it is possible to simulate larger systems and longer time scales without compromising accuracy.

Strengthen the links in the chain

The team published its record-breaking simulation work in the Journal of Chemical Physics, and then used these simulations to compare with experimental data obtained in their paper recently published on ACS Nano.

Andersen said that the current generation of supercomputers, such as JUWELS and SuperMUC-NG, enable the team to run its simulations. However, the next generation of machines will open up more possibilities because researchers can simulate more numbers or systems faster in a longer period of time.

Andersen received her doctorate. In 2014, it said that graphene research has exploded during the same period. "It is fascinating that this material is the focus of recent research-it is almost included in my own scientific career, and people have studied it carefully," she said. Although more research is needed on the use of liquid catalysts to produce graphene, Andersen said that a two-pronged approach using HPC and experiments at the same time is essential for the further development of graphene and its use in commercial and industrial applications. "In this research, there is a good interaction between theory and experiment, and I have always stood on both sides of this research," she said. Further explore the use of 3-D curved graphene to stay ahead. More information: Maciej Jankowski et al., Real-time multi-scale monitoring and adjustment of graphene growth on liquid copper, ACS Nano (2021). DOI: 10.1021/acsnano.0c10377 Journal information: ACS Nano, Journal of Chemical Physics

Provided by Gauss Center for Supercomputing Citation: Researchers continue to use HPC (2021, June 4) retrieved from https://phys.org/news/2021-06-refine-graphene-production-hpc.html (2021, June 4) Improving graphene production This document is protected by copyright. Except for any fair transaction for private learning or research purposes, no part may be copied without written permission. The content is for reference only.

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