Rising electricity demand from artificial intelligence, electrification, and cooling is placing increasing pressure on power grids, while hot weather tightens the thermal limits of overhead power lines. In a recent study, a high-thermal-conductivity radiative-cooling coating is introduced as a passive strategy for relieving this thermal constraint. By combining efficient heat conduction with solar reflection and mid-infrared emission in a thermally conductive particle–polymer composite, the coating extends radiative cooling to internally heated power-line infrastructure. Outdoor tests and heat-balance modeling show lower cable temperatures, reduced sag, and projected ampacity gains, suggesting a practical passive-cooling strategy for increasing the capacity of existing power lines.
Photonics has the potential to transform high-speed and energy-efficient artificial intelligence. However, most photonic computing platforms still rely on costly electro-optic conversions for data input, weight loading and storage, resulting in substantial overhead during large-scale computation. Here, we present a large-scale photonic in-memory computing platform based on liquid crystal (LC)-enabled Poincaré-sphere-connected neural networks. Megabyte-level nonvolatile photonic memory is introduced through light-driven reorientation of sulfonic azo-dye and LC molecules, which also functions as the reconfigurable optical neurons. This architecture uniquely integrates optical sensing, memory, and computing in situ, allowing minimal data-movement overheads in various switchable machine vision tasks. Ultrahigh scalability is demonstrated by a 9.44-million-neuron photonic processor array that supports massively parallel optical computing, recording and retrieval, providing a memory capacity 100,000 times larger than state-of-the-art photonic in-memory computing. This work offers a novel pathway toward scalable submicrometer-precision photonic memory, thus establishing a new paradigm for physical neural networks and brain-like artificial intelligence.
Abstract A research team demonstrated a 2067-km field-deployed optical frequency dissemination link over standard telecommunications fiber. The system achieved a record fractional frequency instability of 2.9 × 10⁻ 21 at one day averaging time, and maintained continuous phase lock for more than four days, marking a major step toward large-scale optical clock networks and future precision timing infrastructure.
Abstract Real-time intelligent systems increasingly require hardware that can adapt continuously to evolving inputs, yet most existing processors rely on static-weight inference, making them vulnerable to distribution shifts and error accumulation in dynamic environments. Although adaptive weight updates can, in principle, address this limitation, their implementation on electronic hardware is hindered by the stability–plasticity trade-off, as well as by the memory wall and clocking bottlenecks that become particularly severe in sequential processing. Here, we present a Temporally Plastic Photonic Processor (TPPP) that enables ultra-fast in situ adaptation by combining multi-timescale photonic kernels with a recursive optical delay memory. The architecture integrates a slow, reconfigurable kernel for stable long-term processing and a fast, dynamic kernel for transient adaptation, enabling time-varying weights to be embedded directly in the photonic domain without repeated electronic memory access. We experimentally validate the TPPP on linear and nonlinear sequential tasks. In both regimes, data-driven temporal plasticity enables the TPPP to outperform conventional static photonic baselines in robustness and accuracy. Under an operation-matched INT8 comparison, scaling analysis projects up to 16 × higher per-operation energy efficiency and up to 100 × lower intrinsic single-pass compute delay than advanced electronic processors, establishing the TPPP as a promising hardware framework for real-time adaptive photonic computing.
Abstract Hybrid metasurface–refractive systems have often raised concerns about their overall volume and the complexity of mechanical alignment. This reported study presents a fully integrated meta-aspheric optical system that realizes a wide field of view within an ultracompact volume of 0.02 cm3.
