A scratch that disappears of its own accord. A plastic component that can be repaired after damage. Or a high-quality material that, at the end of its life, does not end up in landfill but can be reused. It sounds futuristic, but according to VUB researcher Lise Vermeersch, the key may lie in a chemical reaction that has been known for almost a century.
In her PhD research at the VUB, Vermeersch investigated how new so-called self-healing and recyclable plastics can be designed more intelligently. These materials belong to a new generation of polymers, the building blocks of, amongst other things, coatings, adhesives, electronics and composite materials. They are strong and lightweight, but have one major drawback: once damaged or discarded, they are often difficult to repair or recycle.
“Many of the materials we use today are designed to last a long time, but not to be given a second life,” says Vermeersch. “We have investigated how we can develop materials that remain strong, yet are capable of repairing themselves, reshaping themselves or being recycled.”
Central to her research is the so-called Diels–Alder reaction, a chemical reaction that enables scientists to link two relatively simple molecules together as building blocks to form a larger, ring-shaped molecule. These molecular bonds can not only be formed but, under the right conditions, can also be broken again. The reaction produces materials that can adapt to their environment and partially repair damage.
Because the reaction is rapid, efficient and produces few unwanted by-products, it has become one of the most important tools in organic chemistry. The reaction was described in 1928 – almost 100 years ago – by the German chemists Otto Diels and Kurt Alder. They were awarded the Nobel Prize in Chemistry for it in 1950.
To better understand which chemical compounds are best suited as building blocks, Vermeersch carried out thousands of computer simulations. She identified patterns that determine how quickly reactions proceed and how stable the resulting compounds are. These results were compiled into a comprehensive database.
She also investigated whether artificial intelligence could help to identify new, promising combinations more quickly. “Instead of carrying out complex calculations for every possible reaction, we can use machine learning to quickly predict which candidates are the most interesting,” says Vermeersch. “This could significantly speed up the development of sustainable materials.”
A key challenge with self-healing materials is striking a balance between strength and reparability. Materials must be sufficiently robust during use, whilst retaining the ability to repair damage or be recycled. That is why Vermeersch also investigated how catalysts – substances that accelerate chemical reactions – can support the process without disrupting this delicate balance.
Furthermore, because large molecules are constantly changing shape, she developed a new computer model, BoltCAR, which takes into account the many possible conformations of a molecule. This enables researchers to predict more accurately how materials will behave in practice. The insights from these molecular simulations were then linked to larger computer models that describe the formation and behaviour of entire polymers. Laboratory experiments confirmed the models’ predictions.
It is noteworthy that the PhD research focuses not only on the scientific feasibility of new materials, but also on their societal impact. Vermeersch developed a framework enabling researchers to assess, at an early stage, whether new material concepts are not only technically viable but also environmentally and socially responsible. “The question is no longer simply whether we can create a new material,” she says. “We must also consider its consequences for people, the environment and society. Sustainability must be part of the design process from the very beginning.”
Further information:
Lise Vermeersch +32 472 31 15 24
Video: https://youtu.be/ip_vmeqHoME?feature=shared
https://doi.org/10.1021/acs.macromol.4c01748
AI modelling:
Conceptual DFT Meets Machine Learning: A New Route to Enhanced Diels–Alder Reactivity
https://doi.org/10.1002/jcc.70277
Catalysis:
Unravelling the Mechanism and Governing Factors in Lewis Acid and Non-Covalent Diels–Alder Catalysis: Different Perspectives
https://doi.org/10.3390/ijms24054938
Hydrogen-Bond-Assisted Diels–Alder Kinetics or Self-Healing in Reversible Polymer Networks? A Combined Experimental and Theoretical Study
https://doi.org/10.3390/molecules27061961
Experimental validation:
Experimental Development of Self-Healing Polymer Networks Supported by DFT Calculations
https://doi.org/10.1021/acs.macromol.5c01859
Modelling of polymers:
Computational Insights into Tunable Reversible Network Materials: Accelerated ReaxFF Kinetics of Furan-Maleimide Diels–Alder Reactions for Self-Healing and Recyclability