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.

中文翻译：

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用于聚合物电解质燃料电池催化剂层中颗粒分析自动化的深度学习

能源材料领域成像基础设施的使用迅速增长，推动了大量数据和复杂性的大量积累。图像处理常规技术在材料研究中的应用通常是临时性的，不加区分的和经验性的，这使得获得可靠的量化指标的关键任务变得模糊不清。此外，这些技术昂贵、缓慢，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，用于对聚合物电解质燃料电池碳负载催化剂的透射电子显微镜 (TEM) 图像的粒度分布进行高通量分析。手动注释了 40 张不同放大倍数的高分辨率 TEM 图像数据集，范围从 10 到 100 nm。该数据集用于训练 U-Net 模型，损失函数采用 StarDist 公式，用于纳米粒子分割任务。StarDist 通过对小至 30 张图像的数据集进行训练，达到了 86% 的准确率、85% 的召回率和 85% 的 F1 分数。