利用转移深度学习框架对过渡金属矿氧化物进行研究,采用先进的p-n转换传感性能

Shaofeng Shao1, Liangwei Yan1, Jiale Li1

  • 1Institute of Advanced Materials and Flexible Electronics (IAMFE), School of Chemistry and Materials Science, Nanjing University of Information Science & Technology, Nanjing 210044, China.

ACS sensors
|March 3, 2025
PubMed
概括

本研究引入了一个深度学习框架,用于预测和设计过渡金属矿氧化物 (TMPO) 气体传感器. 优化的-氧化物传感器显示出对挥发性有机化合物 (VOC) 的高灵敏度和选择性.

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