New low-temp method advances nanoparticle synthesis

In today's rapidly advancing technological landscape, manipulating materials at a microscopic level is proving more critical than ever. Inorganic nanoparticles, especially those made from metals, play a pivotal role in a myriad of applications, from powering our smartphones and computers to enabling breakthroughs in medical treatments and sustainable energy solutions. Crafting mixtures of multiple metals at the nanoparticle scale is not just a scientific endeavor; it's a necessity that underpins many of the technologies we now rely on daily.

Yet, achieving precise control over these mixtures has been complicated. Standard techniques often require high temperatures and swift cooling, which makes controlling the size and shape of the resulting nanoparticles a challenge. “Further, there is no analytical technique to guarantee that the intended mixing was successful,” says Cedric David Koolen, a researcher with EPFL’s School of Basic Sciences. “Conventional approaches offer either the average composition or, at best, the composition of only a handful of particles – which makes it impossible to draw any hard conclusions.”

In a new study led by Koolen, a team of scientists have developed a new method for synthesizing such multimetallic nanoparticles. These minuscule particles, many times finer than a human hair, are important in numerous applications, from electronics to environmental solutions. The new approach, published in Nature Synthesis, could reshape how these nanoparticles are crafted, providing enhanced control and adaptability.

The team were also able to guarantee, for the first time ever, a homogenous mixing of all particles using technology developed by SCIDENTIFY, an EPFL start-up incubated in the laboratory of Professor Andreas Züttel.

The method can be used to synthesize nanosurface alloys (NSAs) at temperatures no higher than 80°C, a significantly lower temperature than those needed in conventional methods, thereby creating a more sophisticated tool for designing NSAs. The team used machine learning to predict and guide the synthesis of specific NSA compositions and fine-tune the structure and performance of the NSAs, speeding up the cycle of testing and improvement.

The main advantage of the new method is its adaptability, as the researchers used it to generate NSAs from both mixable (“miscible”) and non-mixable (“immiscible”) components. This broadens the range of metals that can be combined, ushering in new possibilities in the design and application of these nanoparticles.

The work has already been heralded as “a better match to theory-guided catalyst optimization” by researchers at Lawrence Berkeley National Laboratory and the Korean Institute of Science and Technology in a News&Views article in Nature Synthesis.

The method can have significant application in chemical catalysis. The researchers were able to use their newly designed NSAs as catalysts in the electroreduction of carbon dioxide, a process crucial for sustainable energy technologies. By experimenting with different shapes and compositions of NSAs, the researchers were able to gather insights into how these factors influence their efficiency as catalysts, which can prove invaluable for designing more effective catalysts for a range of chemical reactions.

Other contributors

• Empa Materials Science & Technology
• SCIDENTIFY
• EPFL Interdisciplinary Centre for Electron Microscopy (CIME)
• Paul Scherrer Institute
• University of Copenhagen Center for High Entropy Alloy Catalysis (CHEAC)
• Shanghai University School of Environmental and Chemical Engineering