Researchers use AI to discover new catalyst beyond material boundaries
Peer-Reviewed Publication
Updates every hour. Last Updated: 28-May-2026 13:15 ET (28-May-2026 17:15 GMT/UTC)
Discovering new catalysts is one of the central challenges in developing clean-energy technologies such as green hydrogen production. Yet catalyst discovery has traditionally remained confined within individual material families, limiting researchers’ ability to transfer knowledge across chemically distinct systems.
A research team led by Director HYEON Taeghwan of the Center for Nanoparticle Research within the Institute for Basic Science (IBS) has developed an artificial intelligence (AI) framework that discovers catalysts in a fundamentally new way — by combining knowledge across different catalyst families.Researchers from The University of Osaka created stable cobalt-based honeycomb structures inside a layered material and observed ferromagnetic-like ordering at low temperatures. By introducing a small amount of cobalt into NaSbO3, the team demonstrated a new platform to study Kitaev materials using abundant 3d transition metals, potentially supporting future cost-effective quantum technologies.
Waste plastic and carbon dioxide are two major global waste carbon sources. A new study in Engineering introduces a simple, atmospheric-pressure catalytic method that turns polyethylene and CO₂ into high-value separable aromatics. Using a specially designed oxide–zeolite catalyst, the process delivers high selectivity and stable performance, turning two pollutants into useful petrochemical materials efficiently.
Discarded polyolefin plastics pose severe environmental risks, yet efficient recycling remains challenging. A new study in Engineering presents a simple entropy‑engineering method using silane agents to adjust catalyst surface polarity. This approach stabilizes polymer adsorption, boosts hydrogenolysis activity, and turns waste plastics into high‑value liquid fuels. It works for various catalysts and real‑world plastic wastes, showing strong potential for scalable, sustainable plastic upcycling.
Discarded PET plastic brings serious environmental pressure. A latest study in Engineering offers a green and controllable recycling solution. Without extra catalysts, it uses 1,4-cyclohexanedimethanol (CHDM) to gently break down PET into adjustable oligomers guided by kinetic models. These intermediates can be directly reused to make high-performance elastomers and glycol-modified PET, showing good industrial scalability and promising a more sustainable way for plastic circular economy.
Discarded PET plastic bottles and waste can now be turned into two high-value chemicals without extra hydrogen. A study in Engineering introduces a mild, two-step method using a common Ru/C catalyst to convert PET and methanol into lactic acid and 1,4-cyclohexanedicarboxylic acid. This green approach makes full use of plastic waste’s carbon and hydrogen, offering a sustainable way for plastic upcycling.
Polyurethane (PU) is one of the most widely used plastics, yet recycling it has long been difficult. A new study in Engineering shares recent progress in chemical recycling methods, including hydrogenation, acidolysis, and chem-solvolysis, that can break down PU into reusable raw materials. It explains how these approaches support a circular economy, while pointing out key challenges for moving lab research into real industrial applications.
Plastic pollution poses a growing global threat, while traditional recycling methods suffer from high costs and low efficiency. A new article in Engineering explores promising biocatalytic solutions for plastic depolymerization, including AI-designed enzymes and multi-enzyme systems. These mild, eco-friendly approaches show potential to break down plastics like PET and PUR more effectively, supporting sustainable recycling and the shift toward a circular plastic economy.
Researchers from The University of Osaka and collaborators proposed the Insect Synergy Circuit (ISC), a new concept for bio-hybrid control that uses AI to interpret internal biological signals from insects. By integrating heartbeat, neural signal features, and body movement data, the team developed a cyborg cockroach system that can estimate environment-associated internal states and guide movement while reducing unnecessary stimulation.