MSGID: 1:317/3 6466fb67   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Novel 3D printing method a 'game changer' for discovery, manufacturing   
   of new materials    
      
     Date:   
         May 18, 2023   
     Source:   
         University of Notre Dame   
     Summary:   
         Researchers have created a novel 3D printing method that produces   
         materials in ways that conventional manufacturing can't match.   
      
      
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   FULL STORY   
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   The time-honored Edisonian trial-and-error process of discovery is slow   
   and labor-intensive. This hampers the development of urgently needed   
   new technologies for clean energy and environmental sustainability,   
   as well as for electronics and biomedical devices.   
      
   "It usually takes 10 to 20 years to discover a new material," said   
   Yanliang Zhang, associate professor of aerospace and mechanical   
   engineering at the University of Notre Dame.   
      
   "I thought if we could shorten that time to less than a year -- or   
   even a few months -- it would be a game changer for the discovery   
   and manufacturing of new materials."  Now Zhang has done just that,   
   creating a novel 3D printing method that produces materials in ways that   
   conventional manufacturing can't match. The new process mixes multiple   
   aerosolized nanomaterial inks in a single printing nozzle, varying the   
   ink mixing ratio on the fly during the printing process. This method   
   -- called high-throughput combinatorial printing (HTCP) -- controls   
   both the printed materials' 3D architectures and local compositions   
   and produces materials with gradient compositions and properties at   
   microscale spatial resolution.   
      
   His research was just published in Nature.   
      
   The aerosol-based HTCP is extremely versatile and applicable to a broad   
   range of metals, semiconductors and dielectrics, as well as polymers   
   and biomaterials. It generates combinational materials that function as   
   "libraries," each containing thousands of unique compositions.   
      
   Combining combinational materials printing and high-throughput   
   characterization can significantly accelerate materials discovery, Zhang   
   said. His team has already used this approach to identify a semiconductor   
   material with superior thermoelectric properties, a promising discovery   
   for energy harvesting and cooling applications.   
      
   In addition to speeding up discovery, HTCP produces functionally graded   
   materials that gradually transition from stiff to soft. This makes   
   them particularly useful in biomedical applications that need to bridge   
   between soft body tissues and stiff wearable and implantable devices.   
      
   In the next phase of research, Zhang and the students in his Advanced   
   Manufacturing and Energy Lab plan to apply machine learning and artificial   
   intelligence-guided strategies to the data-rich nature of HTCP in order   
   to accelerate the discovery and development of a broad range of materials.   
      
   "In the future, I hope to develop an autonomous and self-driving process   
   for materials discovery and device manufacturing, so students in the   
   lab can be free to focus on high-level thinking," Zhang said.   
      
       * RELATED_TOPICS   
             o Matter_&_Energy   
                   # Materials_Science # Civil_Engineering #   
                   Engineering_and_Construction # 3-D_Printing # Electronics   
                   # Weapons_Technology # Nanotechnology # Physics   
       * RELATED_TERMS   
             o Knot_theory o Materials_science o Pyroelectricity o   
             Radiocarbon_dating o Carbon-14 o Resonance_(chemistry) o   
             Metallurgy o Tissue_engineering   
      
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   Story Source: Materials provided by University_of_Notre_Dame. Original   
   written by Karla Cruise. Note: Content may be edited for style and length.   
      
      
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   Journal Reference:   
      1. Minxiang Zeng, Yipu Du, Qiang Jiang, Nicholas Kempf, Chen Wei,   
      Miles V.   
      
         Bimrose, A. N. M. Tanvir, Hengrui Xu, Jiahao Chen, Dylan   
         J. Kirsch, Joshua Martin, Brian C. Wyatt, Tatsunori Hayashi,   
         Mortaza Saeidi-Javash, Hirotaka Sakaue, Babak Anasori, Lihua Jin,   
         Michael D. McMurtrey, Yanliang Zhang. High-throughput printing of   
         combinatorial materials from aerosols.   
      
         Nature, 2023; 617 (7960): 292 DOI: 10.1038/s41586-023-05898-9   
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   Link to news story:   
   https://www.sciencedaily.com/releases/2023/05/230518120903.htm   
      
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