Savannah River National Laboratory and 3D Systems have entered into a cooperative research and development agreement, or CRADA, to accelerate innovation in additive manufacturing for advanced energy generation and national security applications. The partnership centers on SRNL’s Advanced Manufacturing Collaborative, leveraging the facility’s unique position as South Carolina’s only national laboratory-operated site located on a public university campus.

Through this CRADA, SRNL and 3D Systems will jointly pursue advancements in materials development, equipment enhancements, artificial intelligence/machine learning – enabled process optimization, manufacturing systems, cybersecurity and workforce development training.

Polycrystalline ferroelectric ceramics can provide strong piezoelectric and electro-optic responses, but pores, grain boundaries and ferroelectric domains scatter light, making transparency difficult to combine with functionality. A new review systematically connects these optical-loss mechanisms with densification, grain and domain engineering, phase tuning and defect control in lead-based PMN–PT and lead-free KNN ceramics. It evaluates five coupled functions—piezoelectricity, electro-optic modulation, energy storage, photoluminescence and photochromism—and distinguishes ceramic devices from single-crystal, thin-film and glass benchmarks. The authors outline a roadmap based on machine learning, texture engineering, high-Curie-temperature design, additive manufacturing and system-level integration.

Robots are often promoted for their increasing intelligence and autonomy. Yet a growing body of research at EPFL suggests that a robot's contribution to sustainability goals may be an equally important measure of success.

Enhancing low-light images with machine learning-based techniques becomes increasingly difficult as resolution rises. To address this challenge, researchers from China have developed LL-Refiner, a novel framework that first performs coarse enhancement at low resolution before progressively refining details at multiple scales. Tests on real-world datasets show that LL-Refiner achieves state-of-the-art image quality while also improving performance in computer-vision tasks such as depth estimation.