Monteiro Gabriel A A, Monteiro Bruno A A, Dos Santos Jefersson A, Wittemann Alexander
Colloid Chemistry, Department of Chemistry, University of Konstanz, Universitaetsstrasse 10, 78464, Konstanz, Germany.
Pattern Recognition and Earth Observation Laboratory, Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil.
Sci Rep. 2025 Jan 17;15(1):2341. doi: 10.1038/s41598-025-86327-x.
Complex structures can be understood as compositions of smaller, more basic elements. The characterization of these structures requires an analysis of their constituents and their spatial configuration. Examples can be found in systems as diverse as galaxies, alloys, living tissues, cells, and even nanoparticles. In the latter field, the most challenging examples are those of subdivided particles and particle-based materials, due to the close proximity of their constituents. The characterization of such nanostructured materials is typically conducted through the utilization of micrographs. Despite the importance of micrograph analysis, the extraction of quantitative data is often constrained. The presented effort demonstrates the morphological characterization of subdivided particles utilizing a pre-trained artificial intelligence model. The results are validated using three types of nanoparticles: nanospheres, dumbbells, and trimers. The automated segmentation of whole particles, as well as their individual subdivisions, is investigated using the Segment Anything Model, which is based on a pre-trained neural network. The subdivisions of the particles are organized into sets, which presents a novel approach in this field. These sets collate data derived from a large ensemble of specific particle domains indicating to which particle each subdomain belongs. The arrangement of subdivisions into sets to characterize complex nanoparticles expands the information gathered from microscopy analysis. The presented method, which employs a pre-trained deep learning model, outperforms traditional techniques by circumventing systemic errors and human bias. It can effectively automate the analysis of particles, thereby providing more accurate and efficient results.
复杂结构可被理解为由更小、更基本的元素组成。对这些结构的表征需要分析其组成部分及其空间构型。在星系、合金、生物组织、细胞乃至纳米颗粒等各种系统中都能找到相关例子。在纳米颗粒领域，由于其组成部分靠得很近，细分颗粒和基于颗粒的材料是最具挑战性的例子。此类纳米结构材料的表征通常通过利用显微照片来进行。尽管显微照片分析很重要，但定量数据的提取往往受到限制。本文展示了利用预训练人工智能模型对细分颗粒进行形态学表征。使用三种类型的纳米颗粒（纳米球、哑铃形纳米颗粒和三聚体纳米颗粒）对结果进行了验证。使用基于预训练神经网络的“分割一切模型”（Segment Anything Model）研究了整个颗粒及其各个细分部分的自动分割。颗粒的细分部分被组织成集合，这在该领域提出了一种新颖的方法。这些集合整理了来自大量特定颗粒域的数据集，表明每个子域属于哪个颗粒。将细分部分排列成集合以表征复杂纳米颗粒，扩展了从显微镜分析中收集到的信息。本文所采用的利用预训练深度学习模型的方法，通过规避系统误差和人为偏差，优于传统技术。它可以有效地自动分析颗粒，从而提供更准确、更高效的结果。
