Chemical Reviews最新文献

As various applications increasingly demand Li-ion batteries (LIBs) with higher energy densities, cathode materials with extensively high Ni contents have been developed for LIBs. However, commercially available polycrystalline (PC) cathodes struggle to maintain structural stabilities due to severe cracking. In this regard, single-crystal (SC) cathode materials have gained significant attention owing to their inherent structural integrities and resistances to intergranular cracking. This review comprehensively examines nanoscale-to-microscale degradation mechanisms, challenges in the synthesis, and characteristic electrochemical behaviors of SC cathodes, in comparison with PC cathodes. By elucidating the distinct structural and kinetic characteristics of SC and PC cathodes, this review offers strategic insights into the rational design of durable, high-energy LIB cathode materials.

Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive and expressive framework for learning the complex topological and geometric features of drug-like molecules, cementing their role in modern molecular modeling. This review provides a comprehensive overview of the methodological foundations and representative applications of GNNs in drug discovery, spanning tasks such as molecular property prediction, virtual screening, molecular generation, biomedical knowledge graph construction, and synthesis planning. Particular attention is given to recent methodological advances, including geometric GNNs, interpretable models, uncertainty quantification, scalable graph architectures, and graph generative frameworks. We also discuss how these models integrate with modern deep learning approaches, such as self-supervised learning, multitask learning, meta-learning and pretraining. Throughout this review, we highlight the practical challenges and methodological bottlenecks encountered when applying GNNs to real-world drug discovery pipelines, and conclude with a discussion on future directions.

This Review surveys the properties and applications of the pentafluoroorthotellurate (“teflate”, OTeF₅) ligand and highlights the syntheses of the known teflate-based compounds across the periodic table. Due to the accessibility to several useful teflate transfer reagents and its unique properties, including strong electron-withdrawing character, considerable steric bulk, and stability against oxidation, a variety of intriguing p-block and d-block species have been reported. These encompass highly reactive Lewis acids, versatile weakly coordinating anions, neutral and cationic noble gas compounds, and a wide number of transition metal complexes. The lower analogues of the pentafluoroorthochalcogenate group, OSeF₅ and OSF₅, are described as well, although fewer examples are known. Recent progress in the derivatization of the OTeF₅ group to cis- and trans-PhTeF₄O or trans-(C₆F₅)₂TeF₃O moieties is also discussed, opening pathways to exciting new research directions.

Fluorinated groups are widely applied substituents in medicinal and agricultural chemistry as well as materials sciences because the introduction of per- and polyfluorinated substituents allow the targeted tuning of molecules and materials properties, in general. In addition to per- and polyfluoroalkyl substituents, especially trifluoromethylheteroatom substituents have attracted increasing interest in recent years. The bis(trifluoromethyl)amino group (CF₃)₂N is an example for a trifluoromethylheteroatom substituent. It has been known since the middle of the last century and it has been used and tested in different fields of applications. This review summarizes the chemistry of the bis(trifluoromethyl)amino group since its beginning up to the end of 2024. It focuses on the synthesis of (CF₃)₂N-containing compounds, precursors for the introduction of the (CF₃)₂N group, and follow-up reactions of (CF₃)₂N-containing molecules. The physicochemical properties of the (CF₃)₂N group and of bis(trifluoromethyl)amines are collected and potential applications that have been described are summarized, as well.

Computational methods have revolutionized NMR spectroscopy, driving significant advancements in structural biology and related fields. This review focuses on recent developments in quantum chemical and machine learning approaches for computational NMR, emphasizing their role in enhancing accuracy, efficiency, and scalability. QM methods provide precise predictions of NMR parameters, enabling detailed structural characterization of diverse systems. ML techniques, leveraging extensive data sets and advanced algorithms, complement QM by efficiently automating spectral assignments, predicting chemical shifts, and analyzing complex data. Together, these approaches have transformed NMR workflows, addressing challenges in metabolomics, protein structure determination, and drug discovery. This review highlights recent progress, emerging tools, and future directions in computational NMR, underscoring its critical role in modern structural science.

Diffusion is a fundamental process in the transfer of mass and energy. Diffusion metamaterials, a class of engineered materials with distinctive properties, enable precise control and manipulation of diffusion processes. Meanwhile, topology, a branch of mathematics, has attracted growing interest within the condensed matter physics community. Recently, the integration of diffusion metamaterials and topology has established a groundbreaking framework for understanding and controlling mass and energy transport processes. This review examines the rapidly emerging field of topological diffusion metamaterials, emphasizing how topological principles enhance robustness and precision in diffusion-driven systems, including thermal, particle, and plasma transport. The foundational theories of this field integrate basic topological theories from topological physics with the core theories of diffusion metamaterials, encompassing transformation theory and its various extensions. Additional related topics, beyond metamaterials, are also discussed. These advancements may have significant applications in various disciplines, including chemistry, enabling unprecedented levels of control in areas such as microfluidic heat management, targeted drug delivery, plasma etching, and beyond.

Achieving precise control of materials synthesis is a cornerstone of modern manufacturing, driving efficiency, functionality, and device innovation. This review examines the roles of in situ transmission electron microscopy (TEM) and neutron scattering (NS) in advancing our understanding of these processes. In situ TEM offers atomic-scale insights into nucleation, growth, and phase transitions, while NS provides an analysis of reaction pathways, phase evolution, and structural transformations over broader length scales. Recent advancements in hardware have greatly improved spatial, temporal, and environmental control in in situ experiments. TEM enables breakthroughs in thermally controlled synthesis, gas-phase deposition, and beam-induced fabrication, including single-atom device creation. NS, particularly in situ neutron diffraction and imaging, are essential for studying bulk-level synthesis pathways. Together, these techniques offer a multiscale view of synthesis and processing. Integrating artificial intelligence (AI), automated workflows, and multimodal characterization is highlighted as a path toward high-throughput, predictive synthesis. By discussing challenges and opportunities in instrumentation and analysis, this review proposes a multiscale approach to accelerate innovation in materials synthesis, with applications across energy storage, quantum materials, and next-generation manufacturing.

Self-regulating hydrogels represent the next generation in the development of soft materials with active, adaptive, autonomous, and intelligent behavior inspired by sophisticated biological systems. Nature provides exemplary demonstrations of such self-regulating behaviors, including muscle tissue’s precise biochemical and mechanical feedback mechanisms, and coordinated cellular chemotaxis driven by dynamic biochemical signaling. Building upon these natural examples, self-regulating hydrogels are capable of spontaneously modulating their structural and functional states through integrated negative feedback loops. In this review, the key design principles and implementation strategies for self-regulating hydrogel actuators are comprehensively summarized. We first systematically classify self-regulating hydrogels into sustained regulation, involving continuous modulation cycles under constant stimuli and one-cycle regulation, characterized by transient transitions driven by specific chemical fuels. Thereafter, the underlying mechanisms, types of hydrogels used, fuels, oscillation periods, amplitudes, and potential applications are highlighted. Finally, current scientific challenges and future opportunities for enhancing the robustness, modularity, and practical applicability of self-regulating hydrogel actuators are discussed. This review aims to provide structured guidelines and inspire interdisciplinary research to further develop advanced hydrogel-based regulatory systems for applications such as soft robotics, autonomous sensors, responsive biomedical devices, and adaptive functional materials.