As a distributed ledger technology, blockchain demonstrates broad prospects in battery recycling due to its decentralized, transparent, and secure characteristics. However, practical implementation faces challenges including technical barriers, cost investments, regulatory adjustments, and data privacy protection. This paper comprehensively introduces blockchain applications in battery recycling, explaining its principles, advantages, and real-world deployment. It analyzes existing problems in current recycling systems, such as data fragmentation, inefficient reverse logistics, and regulatory failures. Furthermore, blockchain-IoT integration applications, including full lifecycle data management, intelligent monitoring, and logistics optimization, are discussed. Innovative business models are proposed, such as decentralized recycling platforms, data-driven frameworks, and sharing economy models. The importance of establishing unified industry standards is emphasized, along with an outlook on future development directions. Through systematic analysis, this study offers insights for researchers and practitioners and serves as a reference for promoting sustainable development in the battery recycling industry.

Wide bandgap semiconductors have become the core material system for high-power electronic devices, deep-ultraviolet optoelectronic devices, and quantum information technology, featuring high-temperature stability and optoelectronic properties. First-principles calculations play an irreplaceable role in revealing the intrinsic physical properties of materials, guiding the optimization of energy-band engineering, and predicting the performance of devices through accurate modeling at the quantum mechanical level. This paper systematically reviews the key advances of first-principles calculations in wide bandgap semiconductor research in recent years: from the theoretical breakthroughs in electronic structure calculation and bandgap correction to the multiscale modeling of photoexcitation dynamics and thermal transport behavior; from the innovative design of defect formation mechanisms and doping strategies to the prediction of the performance of low-dimensional materials and heterojunction devices. The review shows that by integrating multi-body perturbation theory, machine learning algorithms, and dynamic simulation technology, first-principles calculations promote the paradigm shift from empirical exploration to theory-driven research on wide bandgap semiconductors, and the material-performance-device-function correlation model established by the study will lay the theoretical foundation for the breakthrough development of new-generation semiconductor technology.

Planar laser-induced fluorescence (PLIF) is a widely applied non-invasive diagnostic technique in the combustion of power units. PLIF often detects CH and OH radicals to be the key markers for chemical process. Therefore, the clarity of OH-PLIF and CH-PLIF imaging is crucial for studying combustion phenomena. Given the short lifetime of CH radicals in flames, the signal-to-noise ratio (SNR) of CH-PLIF images is much lower than that of OH-PLIF images. Faced with the challenge that traditional denoising methods are ineffective for CH-PLIF images, this study proposes an improved Res-U-Net model based on the U-Net network and incorporating residual connections. This model builds on the traditional U-Net architecture, incorporating previous usage experience and introducing a shortcut connection structure to enhance performance. To improve the model's denoising performance under extreme SNR conditions, this study employs a CH-OH collaborative denoising strategy, which uses OH-PLIF images with a higher SNR to assist in the denoising of CH-PLIF images. Comparisons with several other models on low-SNR datasets demonstrate that the proposed denoising model achieves the best denoising performance.

Computer vision-based traffic object detection plays a critical role in road traffic safety. Under hazy weather conditions, images captured by road monitoring systems exhibit three main challenges: significant scale variations, abundant background noise, and diverse perspectives. These factors lead to insufficient detection accuracy and limited real-time performance in object detection algorithms. We propose AMC-YOLO an improved YOLOv11-based traffic detection algorithm to address these challenges. In this work, we replace the C3k block's bottleneck module with our novel attention-gate convolution (AGConv), which improves contextual information capture, enhances feature extraction, and reduces computational redundancy. Additionally, we introduce the multi-dilation sharing convolution (MDSC) module to prevent feature information loss during pooling operations, enhancing the model's sensitivity to multi-scale features. We design a lightweight and efficient cross-channel feature fusion module (CCFM) for the path aggregation neck to adaptively adjust feature weights and optimize the model's overall performance. Experimental results demonstrate that AMC-YOLO achieves a 1.1% improvement in mAP@0.5 and a 2.7% increase in mAP@0.5:0.95 compared to YOLOv11n. On graphics processing unit (GPU) hardware, it achieves real-time performance at 376 (FPS) with only 2.6 million parameters, ensuring high-precision traffic detection while meeting deployment requirements on resource-constrained devices.

For intelligent transportation systems (ITS), understanding pedestrian motion intention is crucial for enhancing traffic safety, enabling human-centered mobility services, and facilitating adaptive vehicle-pedestrian interactions. This paper proposes a pedestrian gait recognition method based on a modified particle swarm optimization-support vector machine (MPSO-SVM), utilizing fused surface electromyography (sEMG) signals and ankle joint angles. Seven lower-limb gait features were extracted from these signals to characterize walking patterns. The MPSO algorithm optimizes the support vector machine (SVM) parameters to improve classification performance. Experimental results based on data collected from healthy subjects demonstrate a recognition accuracy exceeding 92.5% across four gait phases. The proposed method offers significantly enhanced accuracy and robustness compared to traditional classifiers. These results suggest that the method is suitable for deployment in intelligent traffic control systems, autonomous vehicle navigation, and urban pedestrian behavior prediction.

This paper reviews the current research status, challenges, and prospects of applying large language models (LLMs) in carbon capture technologies. The review emphasizes the importance of interdisciplinary research, integrating AI into chemistry, engineering, and environmental science to address complex challenges in carbon capture. It provides a detailed analysis of how LLMs can be utilized across various stages of carbon capture, from experimental design to industry implementation, showcasing their potential to accelerate innovation. It also reveals the use of LLMs to support gathering and analyzing sustainable information, such as carbon tax, carbon footprint, and social analysis. LLMs not only show great potential in designing and discovering materials for carbon capture technologies but also are promising to accelerate the whole industry's development through their powerful data processing and pattern recognition capabilities. In addition, the review paper also discusses challenges in the application of LLMs for carbon capture technologies and future directions and prospects.

Lithium plating has been identified as the main form of failure in fast charging, so it is imperative to develop effective methods for detecting lithium plating. Experimental results validated the effectiveness of lithium plating detection via expansion force measurement. While multi-stage constant current (MCC) charging is a common form of fast charging, experimental investigations of lithium plating under MCC charging remain scarce, particularly lacking models to describe the battery expansion force changes during the lithium plating process under MCC conditions. The expansion of batteries during charging is caused by factors such as intercalation reactions, thermal expansion, and lithium plating. Through the establishment of the coupling model, the expansion displacement of the battery is calculated. The thermal expansion coefficient and equivalent stiffness of the battery are obtained through experiments, which are used to calculate the battery expansion force. Additionally, the battery overpotential is calculated through the model to determine the impact of lithium plating on the expansion force. The model investigates the effects of lithium plating on battery expansion force under different C-rates during constant current and MCC charging processes, achieving semi-quantitative prediction of battery expansion force under MCC charging and providing model-based optimization directions for MCC charging strategies. This work provides valuable insights for battery modeling and charging strategy optimization in terms of expansion force modeling under multi-stage constant current charging.