New 3D printing method accelerates materials discovery

Yanliang Zhang, an associate professor of aerospace and mechanical engineering at the University of Notre Dame, has revolutionized the traditional trial-and-error process of material discovery with a groundbreaking 3D printing method. Zhang recognized the need for faster development of new materials for clean energy, environmental sustainability, electronics, and biomedical devices, and set out to create a game-changing solution.

His innovative process, known as high-throughput combinatorial printing (HTCP), combines multiple aerosolized nanomaterial inks in a single printing nozzle. During the printing process, the ink mixing ratio can be adjusted on the fly, allowing for precise control over the 3D architecture and local compositions of the printed materials. This results in materials with gradient compositions and properties at a microscale resolution, surpassing the capabilities of conventional manufacturing methods.

Zhang’s research, recently published in the journal Nature, demonstrates the incredible versatility of the aerosol-based HTCP. It can be applied to a wide range of materials, including metals, semiconductors, dielectrics, polymers, and biomaterials. By generating combinational materials, each containing thousands of unique compositions, HTCP functions as a “library” of materials, significantly accelerating the process of material discovery.

The integration of combinatorial materials printing with high-throughput characterization enables even greater acceleration in materials discovery. Zhang and his team have already utilized this approach to identify a semiconductor material with outstanding thermoelectric properties, holding great promise for energy harvesting and cooling applications.

Beyond its speed and efficiency, HTCP also produces functionally graded materials, which gradually transition from stiff to soft. This characteristic makes them particularly valuable in biomedical applications that require materials capable of bridging the gap between soft body tissues and rigid wearable or implantable devices.

Moving forward, Zhang and his students in the Advanced Manufacturing and Energy Lab plan to incorporate machine learning and artificial intelligence techniques to leverage the data-rich nature of HTCP. By doing so, they aim to further accelerate the discovery and development of a wide array of materials.

Zhang envisions a future where an autonomous and self-driving process for materials discovery and device manufacturing is developed, allowing lab students to focus on high-level thinking and innovation. This vision holds tremendous potential for transforming the field and opening up new possibilities in material science and engineering.

Source: University of Notre Dame

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