How can effective shielding, rapid heat dissipation, and complex structural shaping be achieved simultaneously of the ceramic? Researchers have resolved this electromagnetic trap from both materials and topological perspectives, developing a heat-venting ceramic metastructure capable of highly efficient terahertz shielding.

A study in Geodesy and Geodynamics used satellite radar to examine the November 2025 Hayli Gubbi eruption in Ethiopia’s Afar Depression. Researchers combined NISAR’s left-looking radar with Sentinel-1’s right-looking observations to reconstruct three-dimensional ground movement. Although the eruption occurred at Hayli Gubbi, the strongest deformation was detected at Erta Ale, where the summit crater partially collapsed and the ground sank inward. Modeling linked this to two shallow dikes beneath Erta Ale, both about 1 km deep. The event also reactivated nine nearby faults. Compared with a larger eruption earlier in 2025, the November event was more localized and relied mainly on shallow magma storage.

A new study published in Science China Earth Sciences presents a comprehensive plan for scientific ocean drilling in the Huatung Basin, a key region in the western Pacific. Researchers propose a four-site drilling strategy for China’s new drilling vessel, Mengxiang, to resolve long-standing debates about the basin's age and the origin of the adjacent Gagua Ridge. The plan aims to determine if the Huatung Basin is a remnant of Mesozoic oceanic crust and to clarify the region's tectonic evolution, marking a potential shift for China’s role in leading international ocean drilling.

As metabolic dysfunction-associated steatotic liver disease (MASLD) reaches epidemic levels in young people, identifying an easy-to-use, globally applicable screening tool has become a pressing public health priority. 

Quantum photonic chips could transform future quantum networks and quantum computers, but the best quantum emitters and photonic materials are difficult to combine without losing light. A self-aligned heterogeneous photonic integration method overcomes this barrier by joining diamond quantum emitters and spins with integrated photonics at low insertion loss, advancing scalable quantum photonic technologies.

Given the escalating challenge of global plastic pollution, pyrolysis has emerged as an important technology for converting waste plastics into high-value chemicals and liquid fuels. However, due to complex reaction networks, variable feedstock compositions, and multiphase flow characteristics, traditional methods face significant bottlenecks in process optimization and mechanistic exploration. Artificial intelligence (AI) offers a novel perspective for overcoming these complexities with its inherent advantages in processing high-dimensional data involving nonlinear interactions among variables. Against this backdrop, this review comprehensively surveys and analyzes the latest progress and limitations of applying AI to plastic pyrolysis, focusing on the identification of pyrolysis kinetic parameters using intelligent optimization algorithms and AI methods, the prediction of plastic pyrolysis reaction outcomes using AI, the exploration of intrinsic relationships between key process features and product distribution, and pyrolysis mechanisms using interpretability tools, and reactor optimization combining physical models such as computational fluid dynamics (CFD) with AI-based surrogate models. Furthermore, emerging applications of natural language processing (NLP) and large language models (LLMs) in automating the construction of standardized pyrolysis databases and knowledge graphs are explored. While summarizing technical advantages, current weaknesses such as data scarcity, insufficient model generalization, and the lack of physical information coupling are also analyzed and discussed. Finally, a conceptual architecture for an LLM-driven pyrolysis agent is proposed to integrate mechanism retrieval, process simulation, and experimental support. This review provides theoretical insights and future perspectives for intelligent process optimization and potential engineering applications of plastic pyrolysis for the conversion of waste plastics into fuels and chemicals.

Quantitative structure–property relationship (QSPR) modeling is a fundamental approach for property-oriented fuel design because it establishes quantitative relationships between molecular structures and physicochemical properties. Conventional QSPR models generally rely on manually engineered molecular descriptors, are usually developed to predict a single property, and often cannot readily incorporate newly available data, limiting their scalability and long-term applicability. Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in text understanding and parsing knowledge in specialized fields such as chemistry. This study proposes FuelProp-LM, a QSPR framework based on instruction tuning and in-context learning that predicts multiple fuel properties with only the SMILES notation of a fuel molecule as input. Four open-source language models were fine-tuned using 100000 data entries obtained from the PubChem database. To enhance contextual reasoning, high-information prompts were constructed through MACCS molecular fingerprint similarity retrieval. The proposed framework was evaluated on 10 fuel physicochemical property datasets and compared with the widely-adopted LLM DeepSeek-V3.2 and existing QSPR approaches. FuelProp-LM consistently achieved competitive or superior predictive performance across downstream tasks. Quantitative analysis further revealed how instruction tuning and in-context learning improve molecular property prediction by enhancing chemically relevant attention patterns. The accuracy, generalizability, data scalability, and ease of use of this work demonstrate that FuelProp-LM can serve as a practical and flexible tool for fuel property prediction and play a significant role in future AI-assisted fuel design.

Due to the excessive consumption of fossil fuels and the increasingly severe global environment, the world urgently needs to develop new clean and renewable energy sources. However, these energy sources have intermittency and instability, so it is necessary to vigorously develop efficient and large-scale energy conversion and storage technologies such as fuel cells, water electrolyzers, and metal–air batteries based on the core reactions of oxygen evolution reaction and oxygen reduction reaction. Because of their excellent molecular designability and structural tunability, covalent organic frameworks (COFs) show great potential for applications in this field. In this review, we first classify COF-based electrocatalysts based on the nature of active sites. Subsequently, strategies including structural design and functional synthesis for improving performance of COF-based electrocatalysts in the field of oxygen catalysis are systematically reviewed. Importantly, mechanism discussion of the performance improvement is highlighted. Finally, we put forward prospects for the future research directions, challenges, and development opportunities in this field. This review aims to provide guidance for the development of high-performance COF-based electrocatalysts through critical analysis of existing research, revealing the correlation between material design and mechanism for performance improvement.