Fluorine-Free Future for MXenes Unlocked Through Computational Materials Science

Despite rapid growth in research and development since their discovery at Drexel University in 2011, the promising two-dimensional materials MXenes face barriers to large-scale manufacturing and practical application due to reliance on hazardous hydrofluoric acid etching methods. MXenes show potential for uses including energy storage, catalysis, water purification, optoelectronics, communication and healthcare, but scaling production while controlling surface chemistry has challenged wide commercialization.

New research from scientists in the College of Engineering demonstrates a pathway to sustainable, precise MXene synthesis using a computational framework to guide fluorine-free production. The novel method, published in Advanced Materials, highlights the power of computational materials design to advance MXene manufacturing for real-world technologies.

Schematic representation of this new synthesis pathway of MXene
Schematic representation of this new synthesis pathway of MXene based on dry selective extraction (DSE).

Our motivation for using a computational approach is because it allows us to design a new, more sustainable synthesis method efficiently and rationally from a large space of materials chemistry and reaction parameters,” explained Yong-Jie Hu, PhD , assistant professor of materials science and engineering at Drexel, who led the computational study.

The study establishes a computational approach for dry selective extraction of MXenes using iodine vapor to replace the hydrofluoric acid solutions. The team used density functional theory (DFT) calculations to identify thermodynamically stable candidates for the process, including the most popular and widely used titanium carbide MXene.

“Identifying promising candidates of precursors through DFT-based computational screening was challenging yet rewarding,” said Hu. “Our calculations successfully revealed several promising candidates from numerous possibilities.”

The CALPHAD (CALculation of PHAse Diagrams) approach was then used to model optimal temperature and pressure conditions for iodine vapor etching synthesis. Initial experiments produced the desired MXene composition, validating the computational predictions.

“It is not enough to ‘synthesize’ a material in a computer. The process must be transferred to a laboratory and, eventually, industrial-scale production,” said co-author Yury Gogotsi, PhD , Distinguished University Professor and Charles T. and Ruth M. Bach Professor at Drexel, who led the efforts of experimental validation.

This methodology opens the door to dry synthesis of MXenes with compositions unattainable through conventional aqueous acid etching. It also provides a framework to optimize synthesis conditions based on thermodynamic characteristics. Moreover, the process is more sustainable, as it does not require large amounts of water for synthesis and washing of the product. The reaction byproducts can be collected and the iodine can be reused in the synthesis process.

“We envision this methodology as a catalyst for advancing MXene synthesis and applications,” said Gogotsi. “In the future, we plan to apply this approach to other precursors and etching agents, expanding the range of accessible MXene compositions and surface chemistries.”

The researchers see promise for improved performance and sustainability of MXenes in various technological fields, driving innovation through the computational design enabled by this study. “The ability to computationally predict and control surface termination unlocks new possibilities for MXene materials design,” concluded Gogotsi.

The multidisciplinary study was performed by a diverse research team, including Eiara Fajardo, a fith year BS materials science and engineering student; Ervin Rems, a master’s student from the European Erasmus Mundus MESC+ program ; Mark Anaye, a PhD candidate studying under Gogotsi; Robert Lord, PhD, a post-doctoral researcher in Gogotsi’s group; and David Bugallo, PhD, a Marie Curie post-doctoral research in Hu’s group.

Read the full paper here: https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202305200

computationally predicted reaction thermodynamic map
A computationally predicted reaction thermodynamic map to guide the discovery of optimal reaction temperature and pressure along with experimental validations.