Manufacturing Trade and Connectivity (MTC)

A new cost-effective electrochemical method to create single-atom catalysts could provide a boost for future battery technologies.

A new iron-based electrode material keeps lithium ions flowing freely in lithium-ion battery systems, boosting their capacity for energy storage.

A*STAR researchers develop new tools to speed up the development of potent enzymes for oxidising complex alcohol molecules, providing more options for a challenging synthesis step in sustainable chemical manufacturing.

A new thin-film technology solves the challenge of creating rich, durable colours, offering a more efficient and adaptable solution for next-generation displays.

New acoustic filters could support faster future mobile communications by tapping into underused higher frequency bands, reducing congestion in existing channels.

A new optical scanner reduces costs and expands medical imaging applications, enabling doctors to better view and diagnose internal body structures.

Machine-learning-based predictions intelligently analyse complex sensor data to enhance equipment maintenance and reliability.

A*STAR researchers develop machine learning models that quickly and accurately simulate chemical reactions involving complex liquid interactions.

Advanced photonic processors boost machine learning efficiency by handling vast data streams in parallel, with potential benefits for enhanced patient monitoring and cloud computing.

The innovative integration of robotics and machine learning accelerates the development of efficient, sustainable hydrogen catalysts.

A breakthrough approach integrates light-responsive molecules into polymers to advance biomedical applications.

Aided by machine learning, researchers develop a novel high-performance steel optimised for more eco-friendly 3D printing.