It is laborious and slow to discover new things using the traditional Edisonian method of trial-and error. The slow and labor-intensive process of discovery is a hindrance to the development and adoption of new technologies that are urgently required for clean energy, environmental sustainability and electronic and biomedical device devices.
Yanliang Zhang said that it usually takes between 10 and 20 years to develop a new material.
I thought that if the time could be reduced to less than one year, or even a couple of months, it would change everything for the discovery and manufacture of new materials.
Zhang has now done that by creating a 3D printing technique that can produce materials in a way that is unmatched by conventional manufacturing. The new method mixes aerosolized nanomaterials inks with a single print nozzle and changes the mixing ratio of the inks on the fly. This method, called high-throughput combinational printing (HTCP), controls the 3D architectures of printed materials as well as their local compositions. It also produces materials that have gradient compositions with microscale spatial resolution.
The research has just been published. Nature.
The HTCP aerosol is extremely versatile, and can be used with a wide range of materials, including metals, dielectrics and semiconductors, polymers, and biomaterials. It creates combinations of materials that act as “libraries” containing thousands unique compositions.
Zhang explained that combining combinational materials print with high-throughput characterisation can accelerate the discovery of new materials. Zhang’s team has used this method to identify a material with superior thermoelectric characteristics, which is a promising discovery that could be used for energy harvesting or cooling applications.
HTCP also produces materials with a functionally graded transition, from stiff to softer. They are therefore particularly useful for biomedical applications where they need to bridge the gap between soft tissues and rigid wearable or implantable devices.
The next phase of Zhang’s research will involve applying machine learning and artificial-intelligence-guided strategies on the HTCP data to speed up the discovery and development a wide range of materials.
Zhang said, “I hope to develop an automated and self-driving system for materials discovery and manufacturing devices in the future so that students can focus on high level thinking.”