New review in iOptics demonstrates how AI is overcoming key challenges in metasurface design, enabling advanced applications in compact optics and computational imaging.

A research team from Fudan University has developed a hydrogel technology based on microenvironment-responsive mechanisms. The material can sense pH changes in the wound environment and dynamically release functional agents, enabling a switch from antibacterial action to tissue repair. Constructed from an interpenetrating network of sodium alginate and carboxymethyl chitosan, and loaded with tannic acid and zinc-doped bioactive glass, the hydrogel rapidly releases antibacterial molecules during infection and gradually delivers regenerative ions during healing—achieving, for the first time, precise, stage-specific control of infected wound treatment.

Reactive planning and control capacity for collaborative robots is essential when the tasks change online in an unstructured environment. This is more difficult for collaborative mobile manipulators (CMM) due to high redundancies. To this end, this paper proposed a reactive whole-body locomotion-integrated manipulation approach based on combined learning and optimization. First, human demonstrations are collected, where the wrist and pelvis movements are treated as whole-body trajectories, mapping to the end-effector (EE) and the mobile base (MB) of CMM, respectively. A time-input kernelized movement primitive (T-KMP) learns the whole-body trajectory, and a multi-dimensional kernelized movement primitive (M-KMP) learns the spatial relationship between the MB and EE pose. According to task changes, the T-KMP adapts the learned trajectories online by inserting the new desired point predicted by M-KMP. Then, the updated reference trajectories are sent to a hierarchical quadratic programming (HQP) controller, where the EE and the MB trajectories tracking are set as the first and second priority tasks, generating the feasible and optimal joint level commands. An ablation simulation experiment with CMM of the HQP is conducted to show the necessity of MB trajectory tracking in mimicking human whole-body motion behavior. Finally, the tasks of the reactive pick-and-place and reactive reaching were undertaken, where the target object was randomly moved, even out of the region of demonstrations. The results showed that the proposed approach can successfully transfer and adapt the human whole-body loco-manipulation skills to CMM online with task changes.

Researchers led by Prof. ZHAO Yan from the Institute of Biophysics of the Chinese Academy of Sciences reported high-resolution cryo-EM structures of GlyT2 in three major conformational states. These structures illuminate the transporter's molecular mechanisms and provide critical insights into how analgesic compounds are recognized.

Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI. It is crucial for advancing next-generation intelligent robots and has garnered significant interest recently. Unlike data-driven machine learning methods, embodied learning focuses on robot learning through physical interaction with the environment and perceptual feedback, making it especially suitable for robotic manipulation. In this paper, researchers provide a comprehensive survey of the latest advancements in this field and categorize the existing work into three main branches: 1) Embodied perceptual learning, which aims to predict object pose and affordance through various data representations; 2) Embodied policy learning, which focuses on generating optimal robotic decisions using methods such as reinforcement learning and imitation learning; 3) Embodied task-oriented learning, designed to optimize the robot′s performance based on the characteristics of different tasks in object grasping and manipulation. In addition, researchers offer an overview and discussion of public datasets, evaluation metrics, representative applications, current challenges, and potential future research directions. A project associated with this survey has been established at https://github.com/RayYoh/OCRM_survey.

A collaborated research team from the Institute of Solid State Physics, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, has discovered a high-energy-density barocaloric effect in the plastic superionic conductor Ag₂Te₁₋ₓSₓ. 

This study examines the impact of corporate digital mergers and acquisitions (M&As) on the development of New Quality Productive Forces (NQPF). Using a multi-period difference-in-differences (DID) methodology with data from Chinese listed firms (2011-2021), we demonstrate that digital M&As significantly enhance NQPF. We identify two key mechanisms driving this effect: enhanced firm innovation capability and accelerated data asset accumulation. Furthermore, our findings reveal that external factors including advanced industrial structure, higher urban human capital, and lower economic policy uncertainty positively moderate this relationship. This research introduces a novel NQPF measurement index and provides actionable insights for firms and policymakers seeking to leverage digital transformation for high-quality economic development.

Researchers from Zhejiang Lab have proposed a novel spatiotemporal mode multiplexing technology, coupling pulsed orbital angular momentum (OAM) beams with diffractive deep neural networks (D²NN) and optical fiber delay-line arrays to realize a “space-time” projection mechanism. This enables high-speed, all-optical multidimensional multiplexing with potential scalability to the GHz level, offering a breakthrough solution for large-capacity optical communications in challenging environments such as free space and underwater.