Images and videos provide a wealth of information for people in production and life. Although most digital information is transmitted via optical fiber, the image acquisition and transmission processes still rely heavily on electronic circuits. The development of all-optical transmission networks and optical computing frameworks has pointed to the direction for the next generation of data transmission and information processing. Here, we propose a high-speed, low-cost, multiplexed parallel and one-piece all-fiber architecture for image acquisition, encoding, and transmission, called the Multicore Fiber Acquisition and Transmission Image System (MFAT). Based on different spatial and modal channels of the multicore fiber, fiber-coupled self-encoding, and digital aperture decoding technology, scenes can be observed directly from up to 1 km away. The expansion of capacity provides the possibility of parallel coded transmission of multimodal high-quality data. MFAT requires no additional signal transmitting and receiving equipment. The all-fiber processing saves the time traditionally spent on signal conversion and image pre-processing (compression, encoding, and modulation). Additionally, it provides an effective solution for 2D information acquisition and transmission tasks in extreme environments such as high temperatures and electromagnetic interference.

Stimulated emission depletion microscopy (STED) holds great potential in biological science applications, especially in studying nanoscale subcellular structures. However, multi-color STED imaging in live-cell remains challenging due to the limited excitation wavelengths and large amount of laser radiation. Here, we develop a multiplexed live-cell STED method to observe more structures simultaneously with limited photo-bleaching and photo-cytotoxicity. By separating live-cell fluorescent probes with similar spectral properties using phasor analysis, our method enables five-color live-cell STED imaging and reveals long-term interactions between different subcellular structures. The results here provide an avenue for understanding the complex and delicate interactome of subcellular structures in live-cell.

Thermal management of nanoscale quantum dots (QDs) in light-emitting devices is a long-lasting challenge. The existing heat transfer reinforcement solutions for QDs-polymer composite mainly rely on thermal-conductive fillers. However, this strategy failed to deliver the QDs’ heat generation across a long distance, and the accumulated heat still causes considerable temperature rise of QDs-polymer composite, which eventually menaces the performance and reliability of light-emitting devices. Inspired by the radially aligned fruit fibers in oranges, we proposed to eliminate this heat dissipation challenge by establishing long-range ordered heat transfer pathways within the QDs-polymer composite. Ultrahigh molecular weight polyethylene fibers (UPEF) were radially aligned throughout the polymer matrix, thus facilitating massive efficient heat dissipation of the QDs. Under a UPEF filling fraction of 24.46 vol%, the in-plane thermal conductivity of QDs-radially aligned UPEF composite (QDs-RAPE) could reach 10.45 W m?1 K?1, which is the highest value of QDs-polymer composite reported so far. As a proof of concept, the QDs’ working temperature can be reduced by 342.5 °C when illuminated by a highly concentrated laser diode (LD) under driving current of 1000 mA, thus improving their optical performance. This work may pave a new way for next generation high-power QDs lighting applications.

In a very recent study, Prof. Lingling Huang and co-workers proposed and demonstrated reconfigurable optical neural networks based on cascaded metasurfaces. By fixing one metasurface and switching the other pluggable metasurfaces, the neural networks, which operate at near-infrared wavelengths, can perform distinct recognition tasks for handwritten digits and fashion products. This innovative device opens up an avenue for all-optical, high-speed, low-power, and multi-functional artificial intelligence systems.

Optical multilayer thin film structures have been widely used in numerous photonic applications. However, existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets, or are difficult to suit for different types of structures, e.g., designing for different materials at each layer. These methods also cannot accommodate versatile design situations under different angles and polarizations. In addition, how to benefit practical fabrications and manufacturing has not been extensively considered yet. In this work, we introduce OptoGPT (Opto Generative Pretrained Transformer), a decoder-only transformer, to solve all these drawbacks and issues simultaneously.