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Optimizing Graphene-Metal Composite Production With Laser Modeling

Author(s): Diego Espinoza

Mentor(s): Pilgyu Kang, Department of Mechanical Engineering

Abstract
Composite materials are engineered materials made from two or more different composite structures which change physical or chemical properties. Covetics are a type of composite material where graphene is integrated into a metal; these materials show improvement in mechanical strength, electrical and thermal conductivity and increase in material durability. Currently, the formation of covetics has been made using an electric arc method, which gives a non-homogenous graphene distribution, making it more difficult to optimize. Laser-Induced Graphene (LIG) presents a promising alternative. This process employs a laser and a polyimide (PI) film to create porous graphene, infusing it into a metal substrate, resulting in covetic material. Unlike traditional methods, LIG allows for a more controlled and homogenous structure. However, the optimal laser parameters, such as power, wavelength, speed, frequency, and fluence, remain unknown, impacting the quality and efficiency of porous graphene formation in covetics. This research addresses the gaps in understanding the laser parameters’ influence on LIG by developing a photothermal processing modeling of the process. The simulation focuses on correlating laser parameters to the heat distribution during the LIG process. By simulating the thermal changes of the material, the project aims to streamline the determination of optimal laser parameters, bypassing the need for repetitive physical experiments. This computational approach offers a more efficient and cost-effective means of exploring the intricate relationship between parameters and covetic material quality.
Audio Transcript
Hello, my name is Diego Espinoza and I am physics student at George Mason university working with Dr. Pilgyu Kang. Today I will talk about the work I have been doing under him. Optimizing Graphene Metal Composite Production with Laser Modelling.

We are going to start with defining a composite material, which are engineered materials made from two or more different materials. This new material will have new physical or chemical properties.

A good example of one is steel, which is the combination of Iron and Carbon. One process of making steel is by the use of a furnace which reaches extremely high temperatures and the carbon and iron are mixed.

This composite material obtains improvements in properties like strenght and thoughness.

Now that we know what composite materials are, we are going to see a type of composite material which is obtained by integrating graphene, a 2d layer structure of carbon atoms, into a metal. This new materials can show improvements durability, mechanical strenght, thermal and electrical conductivity.

A way of forming this composite material is by the used of a laser, which can be compare to a furnace for the case of steel. This method allows for a more controlled and homogenous structure. However, the optimal laser parameters like its power, the speed, the lasing time or the frequency are not known. Everytime a study is done, the people working with the materials have to start from 0, constantly creating different samples with different parameters and studying them to see which ones are better.

The project we have been working on intends to adress these challenges. By relating the temperature distribution to the formation of the induced graphene in the metal, we are working on computer simulation that shows us the temperature distribution across the material during the lasing process. During the process we study and used the material properties and how some of them can be changed during the temperature changes. Currently we are still working on the modelling and we are improving it week by week. The overall objective is to have a computational model that, by just changing the materials or the laser parameters, a good approximation of the temperature distribution, and therefore the graphene formation can be obtained.

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