According to scientists from MIT and Duke University, a new strategy strengthening polymer materials can lead to permanent plastics and reduce plastic waste.
Using machine learning, scientists identified network molecules that can be added to polymer materials, enabling them to withstand greater strength before tearing. These chain stores belong to the class of molecules called mechanophores, which change their shape or other properties in response to mechanical strength.
“These molecules can be useful for creating polymers that would be stronger in response to strength. You use stress to them, and instead of cracking or breaking, instead you see something that has higher immunity,” says Heather Kulik, Professor Lammot durt chemical engineering in myth, who is also a professor of chemistry and an older author of studies.
Vaccups that scientists identified in this study are compounds containing iron known as Ferrocenes, which until now have not been widely studied in terms of their potential as mechanophores. The experimental assessment of a single mechanoform may take weeks, but scientists have shown that they can use a machine learning model to dramatically accelerate this process.
The myth of Postdoc Ilia Kevlylismvili is the main author of open access paperwhich appeared on Friday ACS Central Science. Other authors are Jafer Vakil, a graduate of Duke; David Kastner and Xiao Huang, both students MIT; and Stephen Craig, professor of chemistry in Duke.
The weakest link
Mechanoforms are molecules that react by force in a unique way, usually by changing their color, structure or other properties. In a new study, the MIT and Duke syndrome wanted to examine whether they could be used to make polymers more resistant to damage.
The new work is based on the study 2023 from Craig and Jeremiah Johnson, professor of chemistry A. Thomas Guertin in myth and their colleagues. In this work, scientists have discovered that, surprisingly, including weak chains to the polymer network can increase the general material. When the materials with these weak chainists are stretched to the fracture point, all cracks propagated by the material try to avoid stronger bonds and instead undergo weaker bindings. This means that the crack must break more bonds than if all bonds were the same force.
To find new ways to use this phenomenon, Craig and Kulik have joined forces to try to identify mechanophores that could be used as weak network.
“We had this new mechanistic insight and the possibility, but it came with a big challenge: of all possible compositions of matter, how do we zoom on those with the greatest potential?” Craig says. “Full appreciation for Heather and Ilia for identifying this challenge and developing an approach to his fulfillment.”
Discovering and characterizing mechanophores is a difficult task that requires time -consuming experiments or intensive compulsory simulation of molecular interactions. Most of the well -known maychanophores are organic compounds, such as the cyclose, which was used as network in the 2023 study.
In a new study, scientists wanted to focus on molecules known as Ferrocenes, which, as it is believed, have potential as mechanora. Ferrocenes are organometal relationships that have an iron atom located between two rings containing carbon. These rings can have different chemical groups to them that change their chemical and mechanical properties.
Many Ferrocenes are used as pharmaceuticals or catalysts, and a handful is known as good mechanophores, but most were not assessed in terms of this use. Experimental tests on a single potential mechanofor may take several weeks, and computing simulations, although faster, still last a few days. The assessment of thousands of candidates using these strategies is a discouraging task.
Realizing that the approach to machine learning can significantly accelerate the characteristics of these molecules, the MIT and Duke team decided to use the neural network to identify Ferrocenes, which can be a promising mechanory.
They started with information from the database known as a Cambridge structural database, which contains the structures of 5000 different ferrocenes, which have already been synthesized.
“We knew that we didn't have to worry about the question of synthesis, at least from the perspective of the mechanophofophor.
First of all, scientists conducted computing simulations for about 400 of these compounds, enabling them to calculate how much strength is necessary to separate atoms in each molecule. In the case of this application, they searched for particles that would fall apart quickly, because these weak cells could make polymer materials more resistant to tearing.
Then they used this data, along with information about the structure of each compound, to train the machine learning model. This model was able to predict the strength needed to activate the mechanor, which in turn affects the resistance to tearing, for the remaining 4,500 compounds in the database, as well as an additional 7,000 compounds that are similar to those in the database, but have rearranging some atoms.
Scientists have discovered two main features that seemed to increase tear resistance. One of them was interactions between chemical groups attached to the Ferrocene rings. In addition, the presence of large, bulky molecules connected to both Ferrocene rings meant that the molecule fell more often in response to forces applied.
Although the first of these features was not surprising, the second feature was not something that the chemist would predict before and could not be detected without AI, the scientists say. “It was something really surprising,” says Kulik.
More difficult plastics
When scientists identified about 100 promising candidates, the Craig laboratory in Duke synthesized the polymer material containing one of them, known as M-TMS-FC. In the M-TMS-FC material, it acts as a connector screams, combining the polymer band that form polyacrylate, a kind of plastic.
By applying strength to each polymer, until he ripped off, scientists discovered that the weak M-TMS-FC connector created a strong, tear-resistant polymer. This polymer turned out to be about four times more difficult than polymers made with a standard ferrocene as a chainer.
“It really has great consequences, because if we think about all plastics from which we use and all accumulation of plastic waste, if you make materials, it means that their lives will be longer. They will be useful for a long time, which can reduce the production of plastics in a long -term perspective,” says Kevlishvili.
Scientists now hope to apply the approach to machine learning to identify mechanora with other desired properties, such as the ability to change color or become catalytically active in response to strength. Such materials can be used as stress sensors or switching catalysts, and can also be useful for biomedical applications such as providing drugs.
In these studies, researchers plan to focus on ferrocenes and other mechanophores containing metals, which have already been synthesized, but whose properties are not fully understood.
“Transitional metal mechanophores are relatively underexposed and are probably a bit more difficult to do,” says Kulik. “This calculating flow of work can be widely used to increase the space of the mechanics that people studied.”
The research was financed by the National Science Foundation Center for the Chemistry of Moleculary optimized networks (coins).
