Fine Structure and Facet Analyses of Tunnel-Structured FeOOH Nanocrystals
Progress in Natural Science Materials International(2024)
Abstract
The development of energy storage systems in the future relies on identifying suitable target materials and synthesizing target materials with controllable physicochemical properties to unlock their electrochemical contributions. Based on the structure-property-performance relationship established in materials science, it is crucial to determine the fine structure of key functional materials. Among these materials, tunnel-structured beta-FeOOH is widely studied due to its low-cost, decent performance and environmental friendliness; yet, its fine structure in terms of the atomic framework and surface configuration has not been well understood. In this work, we successfully synthesized beta-FeOOH nanorods and analyzed their crystallographic features via transmission electron microscopy (TEM). By combining ultramicrotome and aberration-corrected scanning TEM with atomic resolution, we first experimentally revealed the structural details of beta-FeOOH featuring 1 x 1 tunnels and 2 x 2 tunnels along the c-axis ([001]), the same as the nanorod growth direction. The dominant exposed facets of beta-FeOOH nanorods were further revealed to be {010}. Such fine structural elucidation unveils the long-existing mystery in this material system, which is expected to contribute to structural/compositional engineering toward functionality optimization in the near future.
MoreTranslated text
Key words
β-FeOOH tunnel,Anisotropic growth,Atomic resolution,Nanorod,Energy storage
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2013
被引用258 | 浏览
2014
被引用203 | 浏览
2015
被引用50 | 浏览
2017
被引用38 | 浏览
2016
被引用208 | 浏览
2017
被引用64 | 浏览
2016
被引用11 | 浏览
2017
被引用43 | 浏览
2020
被引用8 | 浏览
2020
被引用41 | 浏览
2022
被引用4 | 浏览
2022
被引用11 | 浏览
2023
被引用15 | 浏览
2023
被引用41 | 浏览
2023
被引用28 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper