Chinese firms are building battery plants from Europe to North America, promising jobs while prompting local concerns about the environment, politics, and who really benefits. When the rest of WIRED subscribers get their hands on our next print magazine, you, dear readers of Made in China, can proudly say you heard about it here first. The issue is all about China and includes stories about robots, AI boyfriends, a Chinese town that became the crystal capital of the world, and a Chinese DNA database built for family reunions. Like this newsletter, the issue is our attempt to document how deeply Chinese technology now shapes everyday life--no matter where you live in the world. As part of the issue, I reported a story on how Chinese lithium battery companies like CATL, BYD, and Gotion are now building factories on nearly every continent.

You'd never guess this lock is smart by looking at it, and that's my favorite part. No bulk or screen; looks like a regular lock from both sides of the door. Impressive design with the battery hidden inside the lock bolt. App is beautiful and easy to use. Best with accessories that need to be purchased separately.

China's lithium batteries aren't always "made in China." Companies like BYD and CATL are building factories on nearly every continent. THE symbolism was clear last June when Emmanuel Macron, surrounded by factory workers, held up a sleek lithium battery in his right hand and a mining lamp in his left. He was in Douai, a northern French city with a coal mining history dating back to the 1700s. The city is now also the site of a battery factory, which would allow France to produce all parts of electric vehicles domestically. This factory, Macron declared, represented an "economic and ecological revolution."

Plus: This company is developing gene therapies for muscle growth, erectile dysfunction, and "radical longevity" For decades, lithium-ion batteries have powered our phones, laptops, and electric vehicles. But lithium's limited supply and volatile price have led the industry to seek more resilient alternatives. They work much like lithium-ion ones: they store and release energy by shuttling ions between two electrodes. But unlike lithium, a somewhat rare element that is currently mined in only a handful of countries, sodium is cheap and found everywhere. Read why it's poised to become more important to our energy future. Sodium-ion batteries are one of 10 Breakthrough Technologies this year.

These rare sea creatures live where the sun don't shine. Breakthroughs, discoveries, and DIY tips sent every weekday. Like a scene out of a Jules Verne novel, scientists from Schmidt Ocean Institute recently encountered a giant phantom jelly (). The enormous deep-sea jellyfish was spotted about 830 feet below the surface of the Pacific Ocean by a Remotely Operated Vehicle (ROV) exploring the Colorado-Rawson submarine canyon wall off the coast of Argentina. ROV pilots filmed this giant phantom jelly, or Stygiomedusa gigantea, at 253 meters during an ROV descent to explore the Colorado-Rawson submarine canyon wall.

Things to Do in L.A. Tap to enable a layout that focuses on the article. An attendee plays the Monster Hunter Wilds video game on the Sony PlayStation 5 Pro console during the Tokyo Game Show 2024 at Makuhari Messe in 2024 in Chiba, Japan. This is read by an automated voice. Please report any issues or inconsistencies here . With the start of the new year, Californians will pay a new fee every time they buy a product with a nonremovable battery -- whether it's a power tool, a PlayStation or even a singing greeting card.

Breakthroughs, discoveries, and DIY tips sent every weekday. The United States Consumer Product Safety Commission (CPSC) is well known for getting their point across on social media. A seven-minute montage of mannequins succumbing to 4th of July firework injuries may be an unconventional way to warn about the dangers of recreational explosives--but try forgetting those images when lighting your next bottle rocket. In similar pyrotechnic fashion, the CPSC is warning everyone to take extra care during the holidays when it comes to all kinds of combustible, seasonally appropriate objects. On December 22, the commission illustrated how some gifts are far more flammable than others with its 30-minute Escooter Lithium-Ion Battery Yule Log video.

Battery safety is paramount for electric vehicles. Early fault diagnosis remains a challenge due to the subtle nature of anomalies and the interference of dynamic operating noise. Existing data-driven methods often suffer from "physical blindness" leading to missed detections or false alarms. To address this, we propose a Physics-Aware Attention LSTM Autoencoder (PA-ALSTM-AE). This novel framework explicitly integrates battery aging laws (mileage) into the deep learning pipeline through a multi-stage fusion mechanism. Specifically, an adaptive physical feature construction module selects mileage-sensitive features, and a physics-guided latent fusion module dynamically calibrates the memory cells of the LSTM based on the aging state. Extensive experiments on the large-scale Vloong real-world dataset demonstrate that the proposed method significantly outperforms state-of-the-art baselines. Notably, it improves the recall rate of early faults by over 3 times while maintaining high precision, offering a robust solution for industrial battery management systems.

Abstract-- Battery recycling is becoming increasingly critical due to the rapid growth in battery usage and the limited availability of natural resources. Moreover, as battery energy densities continue to rise, improper handling during recycling poses significant safety hazards, including potential fires at recycling facilities. Numerous systems have been proposed for battery detection and removal from WEEE recycling lines, including X-ray and RGB-based visual inspection methods, typically driven by AI-powered object detection models (e.g., Mask R-CNN, YOLO, ResNets). Despite advances in optimizing detection techniques and model modifications, a fully autonomous solution capable of accurately identifying and sorting batteries across diverse WEEEs types has yet to be realized. In response to these challenges, we present our novel approach which integrates a specialized X-ray transmission dual energy imaging subsystem with advanced pre-processing algorithms, enabling high-contrast image reconstruction for effective differentiation of dense and thin materials in WEEE. Devices move along a conveyor belt through a high-resolution X-ray imaging system, where YOLO and U-Net models precisely detect and segment battery-containing items. An intelligent tracking and position estimation algorithm then guides a Delta robot equipped with a suction gripper to selectively extract and properly discard the targeted devices. The approach is validated in a photorealistic simulation environment developed in NVIDIA Isaac Sim and on the real setup.

Large Multimodal Models (LMMs) are inherently modular, consisting of vision and audio encoders, projectors, and large language models. Yet, they are almost always executed monolithically, which underutilizes the heterogeneous accelerators (NPUs, GPUs, DSPs) in modern SoCs and leads to high end-to-end latency. In this paper, we present NANOMIND, a hardware--software co-design inference framework for Large Multimodal Models (LMMs) that breaks large models into modular ``bricks'' (vision, language, audio, etc.) and maps each to its ideal accelerator. The key insight is that large models can be broken into modular components and scheduled to run on the most appropriate compute units. It performs module-level dynamic offloading across accelerators on unified-memory SoCs. By combining customized hardware design, system-level scheduling, and optimized low-bit computation kernels, we demonstrate our framework with a compact, battery-powered device capable of running LMMs entirely on device. This prototype functions as a self-contained intelligent assistant that requires no network connectivity, while achieving higher throughput and superior power efficiency under strict resource constraints. The design further bypasses CPU bottlenecks and reduces redundant memory usage through token-aware buffer management and module-level coordination. Our system outperforms existing implementations in resource efficiency, cutting energy consumption by 42.3\% and GPU memory usage by 11.2\%. This enables a battery-powered device to run LLaVA-OneVision with a camera for nearly 20.8 hours.