硅酸盐学报

基于背散射电子-X射线能谱图像智能分析的胶凝材料微观多模态多视图表征

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李粒珲 ¹ , 王相权 ¹ , 姚羿舟 ² , 毛丽璇 ³ , 杨健 ⁴

硅酸盐学报 | 研究论文 2025,53(8): 2362-2373

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硅酸盐学报 |研究论文 2025 , 53 (8) : 2362 -2373

基于背散射电子-X射线能谱图像智能分析的胶凝材料微观多模态多视图表征

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李粒珲¹, 王相权¹, 姚羿舟², 毛丽璇³, 杨健⁴

作者信息

^1．昆明理工大学建筑工程学院土木工程系，昆明 650500

^2．西安建筑科技大学土木工程学院，西安 710055

^3．厦门理工学院土木工程与建筑学院，福建厦门 361024

^4．上海交通大学船舶海洋与建筑工程学院，上海 200240

作者简介:

李粒珲（1996—），女，博士，讲师。

Multimodal and Multiview Characterization of Cementitious Blended Binders Using Intelligent BSE-EDS Image Analysis

Lihui LI¹, Xiangquan WANG¹, Yizhou YAO², Lixuan MAO³, Jian YANG⁴

Affiliations

^1.Department of Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China

^2.School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China

^3.School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, Fujian, China

^4.School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

LI Lihui (1996-), female, Ph.D., Lecturer. E-mail: lilihui@kust.edu.cn

出版时间: 2025-08-20 doi: 10.14062/j.issn.0454-5648.20250074

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摘要

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背散射电子（BSE）与能量色散X射线谱（EDS）图像分析技术凭借其出色的二维空间分辨能力，已成为表征土木工程材料微观结构的重要技术手段。本工作提出一种标准化的图像预处理策略，通过整合BSE图像和同视场的定性与定量EDS元素面谱，建立以超像素为基本分析单元的多模态数据集，有效克服孔隙存在及特征X射线与样品相互作用体积过大对物相识别的影响。结合降维和聚类算法，开发可解释的BSE-EDS图像分析模型，实现水泥矿渣复合胶凝体系物相组成的自动化分类。基于Glue平台构建多视图交互分析技术，实现化学元素特征、物相含量分布和离子迁移路径的可视化、同步、联动表征。基于PHATE（Potential of heat-diffusion for affinity-based trajectory embedding）图像构建物相网络拓扑图，通过节点-连接拓扑关系量化物相演化动态特征。最终形成以EDS图像为核心、BSE图像为辅助的智能分析范式，实现化学模态（元素组成、物相类别）与空间模态（孔隙分布、物相分布）的协同定量表征，达成“单次独立微观表征-多模态特征融合分析”的技术愿景，为胶凝体系反应机理研究及微观结构动态演化分析提供较为综合全面的技术支撑。

关键词

胶凝材料 / 背散射电子图像 / 能谱图像 / 微观表征 / 图像处理

Abstract

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Introduction

The precise characterization of microstructure in cement-slag blended pastes is pivotal for understanding reaction mechanisms and optimizing material performance. Traditional methods relying on single-model analysis face limitations in distinguishing phases with similar grey value ranges and integrating multi element mappings. This study proposes an intelligent Backscattered electron and Energy-Dispersive X-ray Spectroscopy (BSE-EDS) image analysis methodology, integrating standardized pre-processing procedures, Potential of Heat-diffusion for Affinity-based Trajectory Embedding and Gaussian Mixture Model (PHATE-GMM) based phase identification model, and Glue-based Multiview visualization, to achieve multimodal characterization of cement-slag pastes. The framework bridges the gap between the BSE image and EDS element mappings with multimodal features in cementitious systems, enabling comprehensive phase identification, element migration analysis, chemical composition analysis, phase evolution tracking, and microstructure quantitative characterization.

Methods

Cement-slag paste (30% slag replacement ratio, water-to-binder ratio 0.4) were prepared, cured (0, 7 d and 14 d), and examined by Scanning electron microscope (SEM) after epoxy impregnating and polishing. The BSE image, qualitative element mappings, and quantified element mappings were acquired with accelerating voltage of 15 kV, working distance of 11 mm. A BSE-EDS data preprocessing pipeline was developed: (1) Guided filtering enhanced EDS element mapping resolution using BSE image as guided image; (2) Generating phase masks excluding pore and epoxy interference; (3) Super-pixel segmentation on phase region (10 000 units) to integrates grey value information from BSE images and element information from quantified element mappings. The PHATE-GMM model combined PHATE dimensionality reduction algorithm for 2D visualization with GMM for clustering PHATE-derived attributes, achieving automated and interpretable phase identification. Glue platform enabled synchronized multi-view interactive analysis (element scatterplots, phase maps, PHATE plots, element histograms, density plots) to validate phase identification results and explore phase relationships.

Results and discussion

PHATE-GMM model achieved great consistency with visual inspection-labeled phases. In raw materials, distinct clusters corresponded to cement clinker, slag and gypsum. The C₃A/C₄AF phases are frequently embedded within the C₂S/C₃S phases, which results in partial super-pixel regions containing both phases during super-pixel segmentation. Consequently, interconnected clusters can be observed in PHATE plot. For hydrated pastes, rim phases (e.g., C₂S/C₃S-rim, slag-rim, CH-rim) emerged as transitional zones between critical phases and C-S-H. The spatial distribution and element composition analyses of both slag-rim and slag demonstrate that the slag-rim exhibits a lightly higher Mg/Ca ratio compared to slag, indicating a distinct enrichment of Mg ions in the rim region. Local analysis mode via Glue facilitates element exploration in un-clustered cementitious systems, particularly for complex or novel binders. Global analysis mode via Glue, suitable for well-characterized cementitious systems. PHATE-derived element density maps further enable visualization of ion migration pathways. The phase network topology diagram achieves tri-dimensional synergistic representation through spatial positioning, radial scale, and connection line thickness parameters, while preserving the original relational information between phases shown in PHATE plots and simultaneously incorporating phase content features. The BSE-EDS image intelligent analysis methodology established in this study enables efficient and accurate determination of pore and phase in cement-slag binders, thereby enabling multiple downstream applications including elemental composition analysis of phases, investigation of evolutionary patterns, and quantitative characterization of microstructure.

Conclusions

1) The proposed pipeline for generating BSE-EDS datasets demonstrates strong generalizability and practicality, effectively mitigating the impact of pore interference and excessive interaction volume on phase identification, providing a viable data management solution for multimodal feature extraction and downstream analysis in cementitious systems.

2) The developed PHATE-GMM phase identification model exhibits high interpretability and intelligence, enabling comprehensible automated phase identification. The visualization structure of PHATE plot offers novel analytical insights for scientifically describing reaction mechanisms and microstructure evolution in cementitious systems.

3) The Glue-based multimodal feature analysis technique facilitates synchronous and interactive exploration of chemical and spatial modalities, allowing for expert-guided optimization of automated phase clustering results.

4) The intelligent BSE-EDS image analysis methodology enables comprehensive downstream applications, which fully exploits the analytical potential of BSE-EDS for microstructural characterization, realizing the technical vision of "single independent micro-characterization - multimodal feature fusion analysis".

Key words

cementitious materials / backscattered electron image / energy-dispersive X-ray spectroscopy image / microstructural characterization / image processing

引用本文

李粒珲, 王相权, 姚羿舟, 毛丽璇, 杨健. 基于背散射电子-X射线能谱图像智能分析的胶凝材料微观多模态多视图表征. 硅酸盐学报, 2025 , 53 (8) : 2362 -2373 . DOI: 10.14062/j.issn.0454-5648.20250074

Lihui LI, Xiangquan WANG, Yizhou YAO, Lixuan MAO, Jian YANG. Multimodal and Multiview Characterization of Cementitious Blended Binders Using Intelligent BSE-EDS Image Analysis[J]. Journal of the Chinese Ceramic Society, 2025 , 53 (8) : 2362 -2373 . DOI: 10.14062/j.issn.0454-5648.20250074

基金

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国家自然科学基金(52408294)

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2025年第53卷第8期

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文章信息

doi: 10.14062/j.issn.0454-5648.20250074

接收时间：2025-02-04
首发时间：2026-10-21
出版时间：2025-08-20

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收稿日期：2025-02-04
修回日期：2025-02-26

基金

国家自然科学基金(52408294)

作者信息

^1．昆明理工大学建筑工程学院土木工程系，昆明 650500

^2．西安建筑科技大学土木工程学院，西安 710055

^3．厦门理工学院土木工程与建筑学院，福建厦门 361024

^4．上海交通大学船舶海洋与建筑工程学院，上海 200240

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250074

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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