Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells
Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells
复制标题
用于聚合物电解质燃料电池催化剂层颗粒分析自动化的深度学习
DOI：
10.1039/d1nr06435e
复制
发表时间：
2021
期刊：
影响因子：
6.7
通讯作者：
Eslamibidgoli, Mohammad J.
中科院分区：
文献类型：
--
作者：
Colliard-Granero, André;Batool, Mariah;Jankovic, Jasna;Jitsev, Jenia;Eikerling, Michael H.;Malek, Kourosh;Eslamibidgoli, Mohammad J.
能源材料领域成像基础设施的快速增长推动了大量数据的积累和复杂性。常规的图像处理技术在材料研究中的应用往往是特设的，不加选择的，和经验的，这使得获得可靠的量化指标模糊的关键任务。此外，这些技术是昂贵的，缓慢的，并且通常涉及几个预处理步骤。本文提出了一种新的基于深度学习的方法，用于高通量分析聚合物电解质燃料电池碳载催化剂透射电子显微镜（TEM）图像的粒度分布。一个数据集的40个高分辨率TEM图像在不同的放大倍率水平，从10到100 nm的规模，手动注释。该数据集用于训练U-Net模型，其中StarDist公式用于损失函数，用于纳米颗粒分割任务。StarDist通过在小到30张图像的数据集上训练，达到了86%的准确率，85%的召回率和85%的F1分数。分割图优于文献中报道的类似问题的模型，粒度分析的结果与手动粒度测量结果一致，尽管成本显着降低。
The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often ad hoc, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. A dataset of 40 high-resolution TEM images at different magnification levels, from 10 to 100 nm scales, was annotated manually. This dataset was used to train the U-Net model, with the StarDist formulation for the loss function, for the nanoparticle segmentation task. StarDist reached a precision of 86%, recall of 85%, and an F1-score of 85% by training on datasets as small as thirty images. The segmentation maps outperform models reported in the literature for a similar problem, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost.