Results 111 to 120 of about 170,883 (277)

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms

CoRR, 2018
Embedding graph nodes into a vector space can allow the use of machine learning to e.g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs.
Kento Nozawa, Masanari Kimura, Atsunori Kanemura   +2 more
openaire   +2 more sources

Photoswitching Conduction in Framework Materials

Advanced Functional Materials, EarlyView.
This mini‐review summarizes recent advances in state‐of‐the‐art proton and electron conduction in framework materials that can be remotely and reversibly switched on and off by light. It discusses the various photoswitching conduction mechanisms and the strategies employed to enhance photoswitched conductivity.
Helmy Pacheco Hernandez, Stefan Hecht, Wolfgang Wenzel, Lars Heinke, Mariana Kozlowska   +4 more
wiley   +1 more source

Raman Microscopy at the Organic–Inorganic Interfaces in Human Calcified Aortic Valves Shows the Co‐Existence of Whitlockite Crystals and Carbonated Hydroxyapatite‐Mineralized Collagen Fibrils

Advanced Functional Materials, EarlyView.
This study advances our understanding of aortic valve stenosis by capturing spatially resolved chemical and structural changes at the nanoscale. The findings highlight the potential of combined Raman and electron microscopy for understanding calcification mechanisms across diverse tissue types.
Robin H. M. Van der Meijden, Carolien T. A Kuster, Amber van Broekhoven, Raphaela Allgayer, Luco Rutten, Rona Roverts, Marta Cerruti, Elena Macías‐Sánchez, Jan Hein Cornel, Salloua el Messaoudi, Anat Akiva, Nico Sommerdijk   +11 more
wiley   +1 more source

Fiber-Wireless Network Virtual Resource Embedding Method Based on Load Balancing and Priority

IEEE Access, 2018
Network virtualization is becoming one of the most promising ways to solve resource solidification problem of fiber-wireless access networks. To fully utilize substrate resources, a virtual resource embedding method including three sub-mechanisms is ...
Siya Xu, Peng Li, Shao-Yong Guo, Xuesong Qiu   +3 more
doaj   +1 more source

Search Efficient Binary Network Embedding

, 2019
Traditional network embedding primarily focuses on learning a dense vector representation for each node, which encodes network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the ...
Yin, Jie, Zhang, Chengqi, Zhang, Daokun, Zhu, Xingquan   +3 more
core  

Covalent Organic Frameworks for Photocatalytic CO2 Reduction: Metal Integration Principles, Strategies and Functions

Advanced Functional Materials, EarlyView.
Covalent organic frameworks (COFs) with metals have been recognized as versatile platforms for photocatalytic CO2 reduction (CO2PRR). Herein, an overview of metal integration strategies for COFs is systematically summarized. Regulatory mechanisms and structure–activity relationships between metal integration and COF‐based CO2PRR are emphasized.
Jie He, Minxian Zhang, Jinqiang Zhang, Hongqi Sun, Shaobin Wang, Xiaoguang Duan   +5 more
wiley   +1 more source

DINE: Dimensional Interpretability of Node Embeddings

IEEE Transactions on Knowledge and Data Engineering
Graphs are ubiquitous due to their flexibility in representing social and technological systems as networks of interacting elements. Graph representation learning methods, such as node embeddings, are powerful approaches to map nodes into a latent vector space, allowing their use for various graph tasks.
Piaggesi, Simone, Khosla, Megha, Panisson, André, Anand, Avishek, PIAGGESI, SIMONE   +4 more
openaire   +2 more sources

Node Embedding via Word Embedding for Network Community Discovery

IEEE Transactions on Signal and Information Processing over Networks, 2017
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate that the proposed ...
Weicong Ding, Christy Lin, Prakash Ishwar   +2 more
openaire   +2 more sources

2D Magnetic and Topological Quantum Materials and Devices for Ultralow Power Spintronics

Advanced Functional Materials, EarlyView.
2D magnets and topological quantum materials enable ultralow‐power spintronics by combining robust magnetic order with symmetry‐protected, Berry‐curvature‐driven transport. Fundamentals of 2D anisotropy and spin‐orbit‐coupling induced band inversion are linked to scalable growth and vdW stacking.
Brahmdutta Dixit, Yifei Yang, Shawn Li, Yu‐Chia Chen, Qi Jia, Jian‐Ping Wang   +5 more
wiley   +1 more source