Rare earth elements (REEs) are vital to the development of low-carbon technologies. There are rising concerns in the United States and elsewhere about REE supply chain stability and risks given the unvalidated perception in the heavy reliance of China, by far the largest REE supplier. However, the relationship between key countries at different stages of global REE supply chains remains unclear. Here, we use a dynamic flow analysis to explore supply dependence between the United States and China by tracing REE flows from mineral mining to market between 2000 and 2022. Our results indicate complementary and cooperative US–China interactions, especially after 2018 when the United States became a net exporter of REE and China's largest supplier, and China became the largest importer of the US REEs and manufacturer of REE-enabled low-carbon technologies. This intensifying interdependence stabilizes REE supply chains and highlights the importance of cooperative REE trade networks.

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Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this article, a real-time early fault diagnosis scheme for lithium-ion batteries is proposed. By applying both the discrete Fréchet distance and local outlier factor to the voltage and temperature data of the battery cell/module that measured in real time, the battery cell that will have thermal runaway is detected before thermal runaway happens. Compared with the widely used single parameter based diagnosis approach, the proposed one considerably improve the reliability of the fault diagnosis and reduce the false diagnosis rate. The effectiveness of the proposed method is validated with the operational data from electric vehicles with/without thermal runaway in daily use.

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Safety analysis according to the spatial distribution characteristics of maritime traffic accidents is critical to maritime traffic safety management. An accident analysis framework based on the geographic information system (GIS) is proposed to characterize the spatial distribution of maritime traffic accidents occurring in the Fujian sea area in 2007–2020 by employing kernel density estimation and spatial autocorrelation techniques. The sea area is divided into various grids, and in each grid, the mapping relationships between the number and severity of the traffic accidents and the traffic characteristics are established. Machine learning (ML) technology is used to assess whether a grid area is an accident-prone area and to predict accident severity in each grid. The accident prediction of different ML models, including random forest (RF) model, Adaboost model, gradient boosting decision tree (GBDT) model, and Stacking combined model, were compared. The optimality of the Stacking combined model was verified by comparing the experimental results of this model with those of classical prediction models, convolutional neural network (CNN), long short term memory (LSTM), and support vector machine (SVM). According to the results, the maritime accident data set of the entire Fujian sea area shows typical clustering characteristics and positive spatial correlation. That is, the kernel density estimation indicates that subareas, including the Ningde sea area, Fuzhou sea area, and Xiamen sea area, generally have high densities of maritime accidents and the highest risk value within the whole Fujian sea area. High-high accident clustering, that is high cluster areas neighbored by other areas of high cluster, is mainly seen in the Ningde and Fuzhou sea areas, while the Xiamen, Putian, and Zhangzhou subareas show low-low clustering, which are low clusters neighbored by low clusters. Among the ML models, the Stacking combined model shows high accuracy, precision, recall, and F1-score values of 0.912, 0.910, 0.912, and 0.904 in predicting whether a grid area is an accident-prone area and 0.750, 0.745, 0.750, and 0.746 in predicting the accident severity in the grid, indicating its superior maritime traffic accident prediction performance. Based on our analysis of the distribution characteristics and geospatial data, our proposed method demonstrates effective and reliable risk prediction.@en

The kinetics of the transformation of metallic Fe to the active Fe carbide phase at the start of the Fischer-Tropsch (FT) reaction were studied. The diffusion rates of C atoms going in or out of the lattice were determined using 13C-labeled synthesis gas in combination with measurements of the transient 12C and 13C contents in the carbide by temperature-programmed hydrogenation. In the initial 20 min, C diffuses rapidly into the lattice occupying thermodynamically very stable interstitial sites. The FT reaction starts already during these early stages of carburization. When reaching steady state, the diffusion rates of C in and out of the lattice converge and the FT reaction continues via two parallel reaction mechanisms. It appears that the two outer layers of the Fe carbide are involved in hydrocarbon formation via a slow Mars-Van Krevelen-like reaction contributing to ∼10% of the total activity, while the remainder of the activity stems from a fast Langmuir-Hinshelwood reaction occurring over a minor part of the catalyst surface.

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The formation of Fe-carbide phases is relevant to the synthesis of Fischer-Tropsch synthesis catalysts. We investigated the carburization of Raney Fe as a model catalyst using spectroscopic and temperature-programmed techniques. IR spectroscopy shows that CO dissociation already occurs at −150 °C, while C diffusion into metallic Fe requires much higher temperature (~180 °C). The carburization rate increases with increasing H₂/CO ratio, which can be attributed to the lower overall barrier for O removal as H₂O as compared to CO₂. O removal frees vacancies that are needed for CO dissociation. The resulting higher C coverage increases the driving force for Fe-carbide formation. A higher driving force leads to predominant formation of the more carbon-rich ε(́)-carbide, while χ-Fe₅C₂ is formed at lower H₂/CO ratio. The removal of surface O appears to be the rate-limiting step under all conditions. Initially, most of deposited C is used for Fe-carbide formation with a small contribution to hydrocarbons formation.

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The Fe-catalyzed Fischer-Tropsch (FT) synthesis reaction is the core of the coal-to-liquids (CTL) process, which is an efficient route to convert coal into liquid fuels via synthesis gas (a mixture of CO and H₂). Conventional Fe-based FT catalysts convert typically 30% of the feed CO to undesirable CO₂during the FT step. A decrease of CO₂production in FT units can reduce the profitability of the overall CTL process. In this context, we synthesized phase-pure ϵ-Fe-carbide FT catalysts by careful control of the pretreatment and carburization conditions starting from Raney Fe. The resulting phase-pure Fe-carbide exhibits a low CO₂selectivity during the Fischer-Tropsch reaction. The developed preparation method can be applied to supported Fe catalysts and does not require expensive starting chemicals. The purity and durability of as-synthesized phase-pure ϵ-Fe-carbide catalyst under FT conditions were characterized in detail by in situ characterization techniques. A bulk phase-pure ϵ-Fe-carbide catalyst operated at 23 bar, 235 °C and a H₂/CO ratio of 1.5 shows stable performance with no deactivation during a 150 h pilot test. The catalyst is free of primary CO₂formation and presents reduced secondary CO₂formation compared to conventional Fe-based FT catalysts. These findings contribute to the development of new Fe-based FT catalysts for CTL processes.

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The large scale of construction in mega projects leads to significant environmental and socioeconomic impacts; thus, the projects should also exhibit greater social responsibility. Adopting green supply chain management in the construction process is an important way to realize the goal of sustainable development of mega projects. Because the green supply chain behavior during construction is mainly demonstrated by contractors, it is especially important to study the evolutionary trend of their behavior. Thus, to explore the cooperative relationship among contractors, this paper considers a lengthy construction period, multi-agent participation dynamics, and opportunistic behavior-all are key features of mega projects-and establishes an evolutionary game model. Specifically, a replicator dynamic equation is used to describe the long-term effects of the contractor's decisions. Equilibrium determinants are then analyzed and simulated. The results show that the initial probabilities of the two types of contractors (main contractor and subcontractor) demonstrating opportunistic behaviors have a significant effect on the direction of evolution of the mega project. The main contractor, who dominates the subsidy allocation by the government and project owner, should pay attention to maintaining a balance in the income distribution between him or her and the subcontractor. Additionally, there is an optimal distribution coefficient for the subsidy that minimizes the overall probability of opportunistic behavior. This paper provides a point of reference for the decisions of the main contractor and the subcontractor in the green supply chain of mega projects.

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