Machine learning applications in SEM-based pore analysis: a review

IF 4.8 3区材料科学 Q1 CHEMISTRY, APPLIED

Microporous and Mesoporous Materials Pub Date : 2025-05-05 DOI:10.1016/j.micromeso.2025.113675

Efi-Maria Papia , Alex Kondi , Vassilios Constantoudis

{"title":"Machine learning applications in SEM-based pore analysis: a review","authors":"Efi-Maria Papia , Alex Kondi , Vassilios Constantoudis","doi":"10.1016/j.micromeso.2025.113675","DOIUrl":null,"url":null,"abstract":"<div><div>Scanning Electron Microscopy (SEM) is a cornerstone technique for analyzing porous materials, providing high-resolution images essential for understanding material properties and performance. However, traditional SEM image analysis methods often involve manual interpretation and are limited by challenges such as noise, segmentation difficulties, and resolution constraints. Recent advancements in machine learning have revolutionized SEM image analysis, offering automated, accurate, and scalable solutions. These technologies enable precise pore size distribution measurement, pore shape classification, and network connectivity analysis while enhancing image quality through advanced denoising techniques. This paper reviews the integration of Artificial Intelligence (AI) in SEM-based porous material analysis, discussing its applications, challenges, and future directions. Through highlighting key contributions in the field, we aim to provide a comprehensive overview of how AI is reshaping SEM image analysis and unlocking new possibilities for porous material characterization, also emphasizing challenges and limitations that arise.</div></div>","PeriodicalId":392,"journal":{"name":"Microporous and Mesoporous Materials","volume":"394 ","pages":"Article 113675"},"PeriodicalIF":4.8000,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microporous and Mesoporous Materials","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1387181125001891","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}

引用次数: 0

Abstract

Scanning Electron Microscopy (SEM) is a cornerstone technique for analyzing porous materials, providing high-resolution images essential for understanding material properties and performance. However, traditional SEM image analysis methods often involve manual interpretation and are limited by challenges such as noise, segmentation difficulties, and resolution constraints. Recent advancements in machine learning have revolutionized SEM image analysis, offering automated, accurate, and scalable solutions. These technologies enable precise pore size distribution measurement, pore shape classification, and network connectivity analysis while enhancing image quality through advanced denoising techniques. This paper reviews the integration of Artificial Intelligence (AI) in SEM-based porous material analysis, discussing its applications, challenges, and future directions. Through highlighting key contributions in the field, we aim to provide a comprehensive overview of how AI is reshaping SEM image analysis and unlocking new possibilities for porous material characterization, also emphasizing challenges and limitations that arise.

Abstract Image

查看原文本刊更多论文

机器学习在基于扫描电镜的孔隙分析中的应用综述

扫描电子显微镜（SEM）是分析多孔材料的基础技术，为了解材料特性和性能提供高分辨率图像。然而，传统的扫描电镜图像分析方法往往涉及人工判读，并且受到噪声、分割困难和分辨率限制等挑战的限制。机器学习的最新进展彻底改变了扫描电镜图像分析，提供了自动化，准确和可扩展的解决方案。这些技术可以实现精确的孔径分布测量、孔隙形状分类和网络连通性分析，同时通过先进的去噪技术提高图像质量。本文综述了人工智能（AI）在基于sem的多孔材料分析中的应用，讨论了其应用、挑战和未来发展方向。通过强调该领域的关键贡献，我们的目标是全面概述人工智能如何重塑SEM图像分析，并为多孔材料表征解锁新的可能性，同时强调出现的挑战和限制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Microporous and Mesoporous Materials 化学-材料科学：综合

CiteScore

10.70

自引率

5.80%

发文量

649

审稿时长

26 days

期刊介绍： Microporous and Mesoporous Materials covers novel and significant aspects of porous solids classified as either microporous (pore size up to 2 nm) or mesoporous (pore size 2 to 50 nm). The porosity should have a specific impact on the material properties or application. Typical examples are zeolites and zeolite-like materials, pillared materials, clathrasils and clathrates, carbon molecular sieves, ordered mesoporous materials, organic/inorganic porous hybrid materials, or porous metal oxides. Both natural and synthetic porous materials are within the scope of the journal. Topics which are particularly of interest include: All aspects of natural microporous and mesoporous solids The synthesis of crystalline or amorphous porous materials The physico-chemical characterization of microporous and mesoporous solids, especially spectroscopic and microscopic The modification of microporous and mesoporous solids, for example by ion exchange or solid-state reactions All topics related to diffusion of mobile species in the pores of microporous and mesoporous materials Adsorption (and other separation techniques) using microporous or mesoporous adsorbents Catalysis by microporous and mesoporous materials Host/guest interactions Theoretical chemistry and modelling of host/guest interactions All topics related to the application of microporous and mesoporous materials in industrial catalysis, separation technology, environmental protection, electrochemistry, membranes, sensors, optical devices, etc.