重新构想碳纳米材料分析:在扫描电子显微镜中授权转移学习和机器视觉,用于高保真识别
Siddharth Gupta1,2, Sunayana Gupta1, Arushi Gupta3
1Ira A Fulton School of Engineering, Computer Science and Engineering, Arizona State University, Tempe, AZ 85281, USA.
Materials (Basel, Switzerland)
|August 12, 2023
概括
我们使用人工智能和电子显微镜开发了一种新方法,以识别纳米级碳材料,如Q碳和纳米钻石. 这种技术能够有效地发现和分类各种各样的碳异构体,即使它们分布稀疏.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术纳米技术
- 人工智能的人工智能
背景情况:
- 鉴定各种纳米级碳全方位是具有挑战性的,因为稀疏分布.
- 标准的激光照射方法无法预测地产生各种碳多态.
研究的目的:
- 提出一种用于识别和分析多种纳米级碳异构体的新技术.
- 为了能够有效地发现和分类稀疏的碳多态体.
主要方法:
- 通过激光照射精确控制低温融碳的火速度.
- 应用转移学习与卷积神经网络和计算机视觉到扫描电子显微镜.
- 即使在空间存在稀疏的情况下,也可以实现全方位的发现.
主要成果:
- 通过可控激光照射成功形成微钻,纳米钻和Q碳膜.
- 实现了92%的Q-碳识别准确度.
- 在区分Q-碳和纳米钻石方面达到94%的准确性.
结论:
- 开发的技术可以有效地识别和描述纳米碳结构.
- 提供用于Q材料和碳多态体识别的自动化工具.
- 开辟了探索这些材料在各种应用中的新途径.
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