Researchers from the Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT), Chinese Academy of Sciences (CAS), described a new way of processing formamidinium-based perovskite film that relieves some of the limitations by using ammonia treatment to remove pore structures formed during the processing.

The hybrid magnet at the Steady High Magnetic Field Facility (SHMFF) in Hefei, China set a world record for the highest steady magnetic field by a working magnet when it produced a steady field of 45.22 tesla (T) on Aug. 12. It broke the previous world record of 45 tesla set in 1999 by a hybrid magnet at the National High Magnetic Field Laboratory of the United States.

A recent study published in Nature Biotechnology revealed the development of high-fidelity Cas13 variants with markedly reduced collateral effects by mutagenesis and demonstrated the feasibility of hfCas13 for efficient on-target RNA degradation with almost no collateral damage in mammalian cells and animals.

Researchers at UCLA, USA, report an AI-designed camera that performs class-specific imaging and encryption of target objects with instantaneous erasure of other undesired object classes. The success of this new camera was experimentally demonstrated using terahertz waves and 3D-printed diffractive layers that selectively imaged only one class of handwritten digits, all-optically erasing all the other handwritten digits. This AI-designed camera is fast, data- and energy-efficient, making it ideal for task-specific, privacy-aware, and power-limited imaging applications.

The formation of a super strong Mongolian cyclone and its contributing factors

Computer-generated holography (CGH) is acknowledged as the next-generation display technology for its ability to visualize photorealistic 3D imagery to naked eyes. However, efficient rendering of high-quality 3D CGH has been a grand challenge due to high computational cost and complicacy of correctly handling effects such as occlusion and depth-of-field. To tackle this problem, scientists from MIT introduced a machine learning system that understands the underlying physics and accelerates this process by orders of magnitude.