作者：Xu, Ziang; Lin, Dongcheng; Yin, Haoyu; Feng, Qingqing; Foglia, Fabrizia; Zhen, Yihan; Morris, Adam; Berrod, Quentin; Pang, Maobin; Huang, Lida; Liu, Jing; Tian, Jiekang; Wang, Xiaonan; Yang, Chunming; Tang, Xingchen; Zhang, Xi; Wang, Baoguo; Wang, Haotian; Liu, Kai
作者单位：Tsinghua University; Tsinghua University; Tsinghua University; University of London; University College London; Communaute Universite Grenoble Alpes; Centre National de la Recherche Scientifique (CNRS); CEA; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Tsinghua University; Chinese Academy of Sciences; Shanghai Advanced Research Institute, CAS; Tsinghua University; Rice University
摘要：Nanoporous anion-conducting membranes have gained considerable interest for their potential to reduce resistance in electrochemical devices1, 2, 3-4. Current pore-forming methods, such as backbone engineering through polymers of intrinsic microporosity5,6 or covalent organic and metal-organic frameworks7,8, however, suffer from limited structural control, mechanical fragility or demanding synthesis. Here we establish a supramolecular strategy that overcomes these limitations by constructing un...
作者：Peng, Jiayong; Luo, Mingcheng; Han, Yuxi; Wu, Siying; Li, Hongsheng; Shastri, Bhavin J.; Shu, Chester; Dou, Qi; Chai, Yang; Huang, Chaoran
作者单位：Chinese University of Hong Kong; Queens University - Canada; Chinese University of Hong Kong; Hong Kong Polytechnic University
摘要：Large-scale artificial intelligence (AI) models achieve notable performance in computer vision but require substantial computational resources, limiting their deployment on edge devices1,2. Optical neural networks (ONNs) promise reduced latency and energy consumption by making use of the inherent parallelism of light3. However, present ONNs struggle to scale and are confined to simple tasks, owing to the challenges of replicating exact algebraic operations of digital models using physical (ana...
