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Category: transportation – Page 3

Curved nanosheets in anode help prevent battery capacity loss during fast charging

As electric vehicles (EVs) and smartphones increasingly demand rapid charging, concerns over shortened battery lifespan have grown. Addressing this challenge, a team of Korean researchers has developed a novel anode material that maintains high performance even with frequent fast charging.

A collaborative effort by Professor Seok Ju Kang in the School of Energy and Chemical Engineering at UNIST, Professor Sang Kyu Kwak of Korea University, and Dr. Seokhoon Ahn of the Korea Institute of Science and Technology (KIST) has resulted in a hybrid anode composed of graphite and organic nanomaterials. This innovative material effectively prevents capacity loss during repeated fast-charging cycles, promising longer-lasting batteries for various applications. The findings are published in Advanced Functional Materials.

During battery charging, lithium ions (Li-ions) move into the , storing energy as Li atoms. Under rapid charging conditions, excess Li can form so-called “dead lithium” deposits on the surface, which cannot be reused. This buildup reduces capacity and accelerates battery degradation.

3D-printed metamaterials harness complex geometry to dampen mechanical vibrations

In science and engineering, it’s unusual for innovation to come in one fell swoop. It’s more often a painstaking plod through which the extraordinary gradually becomes ordinary.

But we may be at an inflection point along that path when it comes to engineered structures whose are unlike anything seen before in nature, also known as mechanical metamaterials. A team led by researchers at the University of Michigan and the Air Force Research Laboratory (AFRL) has shown how to 3D print intricate tubes that can use their to stymie vibrations.

Such structures could be useful in a variety of applications where people want to dampen vibrations, including transportation, civil engineering and more. The team’s new study, published in the journal Physical Review Applied, builds on decades of theoretical and computational research to create structures that passively impede vibrations trying to move from one end to the other.

Self-healing layer improves the safety and lifespan of all-solid-state lithium batteries

Scientists have come up with a new way to improve the safety and performance of all-solid-state lithium metal batteries (ASSLMBs), the next-generation energy source technology that is set to power everything from electric vehicles to renewable energy grids.

Most batteries that are in common use today contain flammable liquid electrolytes. The next evolution in batteries is the ASSLMB, which replaces the flammable liquid with a non-flammable solid material to move between electrodes. While they are significantly safer, there is a critical flaw that prevents them from being reliable and long-lasting. That is, repeated charging and discharging cause gaps to form between the solid lithium metal anode and the solid electrolyte, which means the quickly breaks down and stops working.

To solve this problem, researchers from the Chinese Academy of Sciences developed a self-healing layer they call DAI (Dynamically Adaptive Interphase) that keeps the battery connected.

Global lead exposure still costs trillions and endangers children, study finds

Lead poisoning was once thought to largely be a problem of the past, as the globe gradually weaned itself off leaded gasoline in road vehicles in 2021. But has global lead pollution truly been resolved?

A new study led by Dr. Chen Mengli, a Research Fellow from the Tropical Marine Science Institute at the National University of Singapore (NUS), in collaboration with researchers from Imperial College London, University of Warwick, University of Oxford, Jadavpur University, University of Michigan, Ann Arbor, Hebrew University of Jerusalem, Massachusetts Institute of Technology, and University of Bristol, showed the answer is not yet: Lead exposure remains a pressing public health and economic challenge in the 21st century.

The researchers estimated that ongoing childhood lead exposure costs the world more than US$3.4 trillion in lost economic potential each year, with disproportionate impacts on low-and middle-income countries.

Novel alloy withstands extreme conditions, could replace metals used in aircraft engines and gas turbines

A new material might contribute to a reduction of the fossil fuels consumed by aircraft engines and gas turbines in the future. A research team from Karlsruhe Institute of Technology (KIT) has developed a refractory metal-based alloy with properties unparalleled to date.

The novel combination of chromium, molybdenum, and silicon is ductile at . With its of about 2,000°C, it remains stable even at high temperatures and is at the same time oxidation resistant. These results are published in Nature.

High-temperature-resistant metallic materials are required for , , X-ray units, and many other technical applications. Refractory metals such as tungsten, molybdenum, and chromium, whose melting points are around or higher than 2,000°C, can be most resistant to high temperatures.

AI-based model can help traffic engineers predict future sites of possible crashes

In a significant step toward improving road safety, Johns Hopkins University researchers have developed an AI-based tool that can identify the risk factors contributing to car crashes across the United States and to accurately predict future incidents.

The tool, called SafeTraffic Copilot, aims to provide experts with both crash analyses and crash predictions to reduce the rising number of fatalities and injuries that happen on U.S. roads each year.

The work, led by Johns Hopkins University researchers, is published in Nature Communications.

New Technique Auto-Selects Training Examples to Speed Up Fine-Tuning

Fine-tuning large language models via reinforcement learning is computationally expensive, but researchers found a way to streamline the process.

What’s new: Qinsi Wang and colleagues at UC Berkeley and Duke University developed GAIN-RL, a method that accelerates reinforcement learning fine-tuning by selecting training examples automatically based on the model’s own internal signals, specifically the angles between vector representations of tokens. The code is available on GitHub.

Key insight: The cosine similarity between a model’s vector representations of input tokens governs the magnitude of gradient updates during training. Specifically, the sum of those similarities that enter a model’s classification layer, called the angle concentration, governs the magnitude of gradient updates. Examples with higher angle concentration produce larger gradient updates. The magnitude of a gradient update in turn determines the effectiveness of a given training example: The larger the update, the more the model learns. Prioritizing the most-effective examples before transitioning to less-effective ones enhances training efficiency while adding little preprocessing overhead.

Novel method for controlling Faraday rotation in conductive polymers

Researchers at the University of Tsukuba have developed a novel method for controlling the optical rotation of conductive polymer polythiophene in a magnetic field at low voltage. This method combines the “Faraday rotation” phenomenon, in which a polarizing plane rotates in response to a magnetic field, with the electrochemical oxidation and reduction of conductive polymers.

The study is published in the journal Molecular Crystals and Liquid Crystals.

Conductive polymers possess various properties in addition to conductivity, with applications in light-emitting devices, electromagnetic wave shielding, and anticorrosion materials.

View a PDF of the paper titled Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play, by Qinsi Wang and 8 other authors

Although reinforcement learning (RL) can effectively enhance the reasoning capabilities of vision-language models (VLMs), current methods remain heavily dependent on labor-intensive datasets that require extensive manual construction and verification, leading to extremely high training costs and consequently constraining the practical deployment of VLMs. To address this challenge, we propose Vision-Zero, a domain-agnostic framework enabling VLM self-improvement through competitive visual games generated from arbitrary image pairs. Specifically, Vision-Zero encompasses three main attributes: Strategic Self-Play Framework: Vision-Zero trains VLMs in “Who Is the Spy”-style games, where the models engage in strategic reasoning and actions across multiple roles. Through interactive gameplay, models autonomously generate their training data without human annotation. Gameplay from Arbitrary Images: Unlike existing gamified frameworks, Vision-Zero can generate games from arbitrary images, thereby enhancing the model’s reasoning ability across diverse domains and showing strong generalization to different tasks. We demonstrate this versatility using three distinct types of image datasets: CLEVR-based synthetic scenes, charts, and real-world images. Sustainable Performance Gain: We introduce Iterative Self-Play Policy Optimization (Iterative-SPO), a novel training algorithm that alternates between Self-Play and reinforcement learning with verifiable rewards (RLVR), mitigating the performance plateau often seen in self-play-only training and achieving sustained long-term improvements. Despite using label-free data, Vision-Zero achieves state-of-the-art performance on reasoning, chart question answering, and vision-centric understanding tasks, surpassing other annotation-based methods. Models and code has been released at https://github.com/wangqinsi1/Vision-Zero.

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