通过传输电子显微镜在催化材料中进行单原子检测的自动图像分析
Sharon Mitchell1, Ferran Parés2, Dario Faust Akl1
1Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland.
Journal of the American Chemical Society
|March 25, 2022
概括
一种新的深度学习方法自动检测传输电子显微镜 (TEM) 图像中的原子,使单原子催化剂 (SAC) 的高吞吐量分析成为可能. 这种人工智能方法提高了先进的催化材料的统计准确性和可扩展性.
科学领域:
- 材料科学
- 催化科学
- 显微镜中的人工智能
背景情况:
- 单原子催化剂 (SAC) 对于先进的催化剂至关重要,但它们使用传输电子显微镜 (TEM) 的表征存在局限性.
- 挑战包括从TEM图像中手动识别原子的不良统计意义,可重复性和互操作性.
研究的目的:
- 在TEM图像中开发深度学习的自动原子检测方法.
- 为了使单原子异质催化剂 (SAC) 的高通量表征.
- 在催化中提高结构性能关系研究的统计意义和可扩展性.
主要方法:
- 在TEM图像分析中开发了一个定制的深度学习模型.
- 该模型在复杂形态的功能化碳材料上支持的单个原子上进行了测试.
- 该方法还在碳化物支持的铁SAC上得到了验证.
主要成果:
- 这种深度学习模型成功地检测了20,000多个原子位置, 实现了催化剂特性的统计分析.
- 分析的关键特性包括表面密度,近距离,聚合程度和金属物种的分散均性.
- 该模型证明了对各种碳相关材料的单原子检测的通用性.
结论:
- 开发的人工智能方法显著加速了SAC的TEM图像处理,减少了人类偏见并改善了标准化.
- 这种方法将人工智能整合到常规的TEM工作流程中,为更强大的前沿催化材料表征铺平了道路.
- 自动化分析有助于建立纳米结构催化剂的可靠结构性能关系.
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