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.

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.

City of Hope develops adaptable CAR T technology that can be updated after treatment to keep pace with evolving tumors. “Much like a software update adds new features to a smartphone after you buy it, meditope allows us to send new instructions to CAR T cells already in the body,” said Dr. John Williams, a City of Hope professor.

Oregon State University scientists have patented a better and more eco-friendly process for separating two metals that are critical in their pure forms for the semiconductor and atomic energy industries.