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Tracking the hydrogen spillover of heterogeneous catalysts in hydrogenation: from formation, migration, and regulation to fate
Hydrogen spillover is a critical factor in enhancing the hydrogenation of heterogeneous catalysts. However, understanding and orderly controlling spillover are still challenging. The structural design of heterogeneous catalysts significantly promotes hydrogen spillover, which affects the activity, selectivity, and stability of hydrogenation. However, to date, very few systematic reviews have tracked the period of hydrogen spillover in heterogeneous catalysis. Herein, we systematically reviewed the recent research progress on hydrogen spillover from formation and fate to regulation. A strengthening mechanism of hydrogen spillover in hydrogenation for the catalytic reaction process is proposed. In addition, targeted regulatory strategies for promoting hydrogen spillover are summarized via constitutive relationships to guide the development of highly efficient hydrogenation catalysts. Finally, the opportunities and challenges of hydrogen spillover are prospectively discussed, providing theoretical guidance for the catalytic hydrogenation methodology.
Two Decades of Optogenetic Tools: A Retrospective and a Look Ahead
Illuminating Life with Optogenetics Beams of light orchestrate cellular control: light-gated ion channels shape ion flux; photoswitchable enzymes and receptors modulate signaling pathways; light-controlled protein interactions tune function; and light-regulated gene expression. In article 202500021, Xiao Duan, Mo Zhu, and Shiqiang Gao review two decades of optogenetics, from fundamental biology to early clinical translation. The cover image is based on the article Two Decades of Optogenetic Tools: A Retrospective and a Look Ahead by Xiao Duan et al., https://doi.org/10.1002/ggn2.202500021.
Accelerating battery innovation: AI-powered molecular discovery
The global energy transition urgently demands advanced battery technologies to address current climate challenges, where molecular engineering plays a pivotal role in optimizing performance metrics such as energy density, cycling lifespan, and safety. This review systematically examines the integration of artificial intelligence (AI) into molecular discovery for next-generation battery systems, addressing both transformative potential and sustainability challenges. Firstly, multidimensional strategies for molecular representation are delineated to establish machine-readable inputs, serving as a prerequisite for AI-driven molecular discovery (Section 2). Subsequently, AI algorithms are systematically summarized, encompassing classical machine learning, deep learning, and the emerging class of large language models (Section 3). Next, the substantial potential of AI-powered predictions for key electrochemical properties is illustrated, including redox potential, viscosity, and dielectric constant (Section 4). Through paradigmatic case studies, significant applications of AI in molecular design are elucidated, spanning chemical knowledge discovery, high-throughput virtual screening, oriented molecular generation, and high-throughput experimentation (Section 5). Finally, a general conclusion and a critical perspective on current challenges and future directions are presented, emphasizing the integration of molecular databases, algorithms, computational power, and autonomous experimental platforms. AI is expected to accelerate molecular design, thereby facilitating the development of next-generation battery systems and enabling sustainable energy innovations.
期刊作品分类
NSC系列
Nature, Science, Cell 顶级期刊系列
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ACS系列
JACS, ACS Nano, ACS Catalysis 等
10 个作品案例
Wiley系列
Advanced Materials, Angewandte 等期刊
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RSC系列
Chemical Science, Materials Chemistry 等
3 个作品案例
期刊创刊号
各大期刊创刊号特辑作品
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其他期刊
其他重要科研期刊作品集
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科学插图
分子结构、细胞图像、实验装置
科研动画
分子动力学、过程演示、机制解释
教育培训课程
专业的科研图像设计培训体系
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科研图像规范与排版
100
封面图像设计精讲
300
3DSMAX科研应用
300
PS+AIGC科研应用
300
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开课时间:2026/4/24
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开课时间:2026/9/14
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