ADVANCED MATERIALS & PROCESSES | SEPTEMBER 2025 5 MACHINE LEARNING | AI Arizona State University, Tempe, received a grant from the National Science Foundation for a project that will use AI to make metal 3D printing faster and more reliable by predicting how the material will form during manufacturing. The team will first 3D print a five-axis, metal naval propeller using 316L stainless steel. asu.edu. BRIEF AI HELPS REMOVE RADIOACTIVE IODINE Researchers at the Korea Advanced Institute of Science and Technology (KAIST) used artificial intelligence to discover a new material that can remove radioactive iodine for nuclear environmental remediation. This form of iodine primarily exists in aqueous environments in the form of iodate, yet existing silver-based adsorbents have weak chemical adsorption strength for removing it. To find a better alternative, the scientists used a machine learning- based experimental strategy to identify optimal iodate adsorbents among layered double hydroxides (LDHs), which contain various metal elements. The multi-metal LDH developed in this study—Cu₃(CrFeAl)— showed exceptional adsorption perfor- mance, eliminating over 90% of the iodate. This outcome was achieved by exploring a vast compositional space using AI-driven active learning to help narrow down the most promising options. The team focused on the fact that LDHs can incorporate a wide range of metal compositions and possess structures favorable for anion adsorption. However, due to the overwhelming number of possible metal combinations in multi-metal LDHs, identifying the optimal composition through traditional experimental methods has been nearly impossible. Starting with data from 24 binary and 96 ternary LDH compositions, the researchers expanded their search to include quaternary and quinary candidates. As a result, they were able to discover the best material for iodate removal by testing only 16% of the total candidate materials. The team will now pursue commercialization through industry-academia collaborations for applications such as iodine-adsorbing powders and contaminated water treatment filters. www.kaist.ac.kr. AI-BUILT MATERIALS BEAT THE HEAT An international team of scientists from The University of Texas at Austin, Shanghai Jiao Tong University, National University of Middle building is wrapped with meta-emitter materials, achieving lower temperatures than those using conventional paint. Courtesy of UT Austin. Singapore, and Umea University in Sweden developed a machine learning- based approach to create complex 3D thermal meta-emitters. Using this system, researchers came up with more than 1500 different materials that can selectively emit heat at various levels and in different manners, making them well suited for achieving precise cooling and heating. To test their platform, the team fabricated four materials to verify the designs. Next, they applied one of the materials to a model house and compared it to commercial paints regarding the cooling effect. After a four-hour midday exposure to direct sunlight, the meta-emitter- coated building roof came in between 5° and 20°C cooler on average than the ones with white and gray paints, respectively. The researchers estimated that this level of cooling could save the equivalent of 15,800 kW per year in an apartment building in a hot climate. Beyond energy efficiency in homes and offices, thermal meta-emitters could be used to manage a spacecraft’s temperature by reflecting solar radiation and emitting heat efficiently. Other potential cooling applications include textiles, fabrics, and car wraps. utexas.edu. AI helps explore materials for radioactive iodine removal. Courtesy of J. Hazard. Mater, 2025, doi.org/10.1016/j.jhazmat.2025.138735.
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