以电子显微学为主，从晶格和电荷自由度揭示功能氧化物材料、储能材料和催化材料原子尺度结构和电子结构的关联；在此基础上，实现轨道自由度电子占据态的直接观测。 获得国际电子显微学联合会青年科学家奖 (2006)...

2024年11月19日 · The perfect Lingangu Lingan Linganguli Animated GIF for your conversation. Discover and Share the best GIFs on Tenor.

Presently, I am a project manager（PM=課題推進者） within the Moonshot Program (ムーンショット型研究開発制度). Moonshot is a national program overseen by the Cabinet of Japan (内閣府) that seeks to stimulate high-risk, high-impact research and development, ultimately achieving innovative value creation tailored to Japan's needs in the modern era.The program is designed ...

Listen & share Lingan guli don pollo. Lingan guli guli guli guacha lingan gu lingan gu Lingan guli guli guli guacha lingan gu lingan guuuuuuuuuu. #donpolo . 21,455 views. Uploaded by evelina3. Find more instant sound buttons on Myinstants!

Lin Gu. Chinese Academy of Sciences. No verified email. Articles Cited by Public access. Title. Sort. Sort by citations Sort by year Sort by title. Cited by. Cited by. Year; Photochemical route for synthesizing atomically dispersed palladium catalysts. P Liu, Y Zhao, R Qin, S Mo, G Chen, L Gu, DM Chevrier, P Zhang, Q Guo, ...

Lin Gu, Professor at the Institute of Physics, Chinese Academy of Sciences, has been engaged in the field of electron microscopy for nearly 20 years. In 2002, he graduated from Tsinghua University and obtained bachelor's degree mentored by Academician Prof. Jing Zhu.

Promoting openness in scientific communication and the peer-review process

Lin GU | Cited by 86,763 | of Chinese Academy of Sciences, Beijing (CAS) | Read 1021 publications | Contact Lin GU

Lin Gu Beijing National Center for Electron Microscopy and Laboratory of Advanced Materials, School of Materials Science and Engineering, Tsinghua University, Beijing, China. HomePage

Kohtaro Tanaka | Kohei Uehara | Lin Gu | Yusuke Mukuta | Tatsuya Harada Findings of the Association for Computational Linguistics: NAACL 2024 Although automated image captioning methods have benefited considerably from the development of large language models (LLMs), generating humorous captions is still a challenging task.