[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118507-en":3,"doc-seo-118507-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118507,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Techniques in Microscopic Characterisation of Nanomaterials","Nanomaterial characterisation is difficult because properties and behaviours vary strongly with size and scale. Machine learning (ML) advances enable new ways to analyse microscopic data and extract dependable information at the nanoscale, such as chemical composition and structure, plus other material properties. The study surveys ML-based approaches for chemical quantification, structure interpretation from imaging, and nano-electrical characterisation. Results show that ML can improve reliability, reduce analysis bias and uncertainty, and support integrated interpretation from multiple techniques, strengthened by microscopic imaging–ML coupling.","IOP Conf. Series: Materials Science and Engineering 1324 (2025) 012007 doi:10.1088/1757-899X/1324/1/012007  \nMachine Learning techniques in microscopic characterisation of nanomaterials  \nBenedykt R. Jany  \nJagiellonian University, Marian Smoluchowski Institute of Physics, Faculty of Physics, Astronomy and Applied Computer Science, Lojasiewicza 11, 30348 Krakow, Poland  \ne-mail: [benedykt.jany@uj.edu.pl](benedykt.jany@uj.edu.pl)  \nAbstract. The characterisation of nanomaterials presents significant challenges due to their unique properties and size-dependent behaviours. Recent breakthroughs in Machine Learning (ML) techniques have enabled the development of innovative methods for analysing microscopic data, thereby facilitating the extraction of reliable information on nanomaterials' chemical composition, structure, and other properties at the nanoscale level. This study provides an overview of selected ML-based approaches applied to microscopic characterisation of nanomaterials, including chemical composition quantification, structure analysis via imaging, and nano-electrical characterisation. The results demonstrate that ML techniques can significantly enhance and simplify the task of nanomaterials characterisation, while minimising data analysis bias and uncertainty. This approach has the potential to derive a comprehensive understanding of a given material's properties by integrating information obtained from diverse characterisation techniques. The synergistic coupling between microscopic imaging and Machine Learning techniques provides new quality, perspectives, and opportunities for future materials characterisation.  \n1. Introduction  \nNowadays variety of different types on nano-materials are used all around us in different fields of science, technology and industry. Because of their size in single or tens of nanometres the appropriately matched techniques are used for their visualisation and later also characterisation. Microscopy techniques like transmission or scanning electron microscopy (TEM, SEM) together with atomic force microscopy (AFM) are commonly used. Due to the digital acquisition and storage big amount of data are collected by these techniques in the form of microscopy images or hyperspectral data from different type of spectroscopy measurements at nano-scale, etc. It is usually a challenging and tedious task to get reliable information on nano-materials characterisation such as quantitative chemical composition at the nano-scale, structure changes, and various other properties, from all the collected data. In the following, the applications of the selected Machine Learning (ML) techniques, which improve and simplify the task of characterisation of nano-materials via chosen microscopy techniques will be shown.  \n2. Machine Learning supported SEM EDS chemical quantification at nanoscale  \nEnergy-dispersive X-ray spectrometry in the SEM (SEM-EDS), together with Machine Learning data processing could be used to quantify chemical composition at the nanoscale [1] . Systems such as AuIn2 metal alloy nano-wires on an InSb substrate (AIIIBV semiconductor) could be successfully quantified.  \nContent from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nPublished under licence by IOP Publishing Ltd 1  \nIOP Conf. Series: Materials Science and Engineering 1324 (2025) 012007 doi:10.1088/1757-899X/1324/1/012007  \nThe AuIn2 nano-wires were synthesised in the processes of thermally induces self-assembly of  \n2 monolayers of gold on atomically flat InSb(001) surface at 330 °C in ultra-high vacuum conditions (UHV) [2] . The formed nano-wires are of an average width of ~ 70 nm. The first step towards the quantification is to collect the EDS data at the nano-scale from single nano-wires. The measurement condition (beam current and beam energy) were care","cbCaio3MhBSzkXhM","https://ap.wps.com/l/cbCaio3MhBSzkXhM","pdf",4795788,1,9,"English","en",105,"# 1. Introduction\n# 2. Machine Learning supported SEM EDS chemical quantification at nanoscale","[{\"question\":\"Why is nanomaterial characterisation challenging at the nanoscale?\",\"answer\":\"Nanomaterials exhibit unique, size-dependent behaviours, so extracting reliable quantitative chemical composition and structural or property information from microscopy and spectroscopy data is typically difficult and time-consuming.\"},{\"question\":\"How does SEM-EDS combined with machine learning support chemical quantification?\",\"answer\":\"By processing SEM-EDS hyperspectral spectrum images with ML methods, chemical composition at the nanoscale can be quantified, enabling analysis such as mapping and separating signals from nanowire and substrate regions.\"},{\"question\":\"What benefits do machine learning techniques bring to microscopic characterisation workflows?\",\"answer\":\"ML techniques enhance and simplify characterisation while minimizing data analysis bias and uncertainty, and they help integrate information from diverse microscopy-based methods to derive a more comprehensive understanding of material properties.\"}]","Machine Learning Techniques in Microscopic Characterisation of Nanomaterials | 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is nanomaterial characterisation challenging at the nanoscale?","Question",{"text":75,"@type":76},"Nanomaterials exhibit unique, size-dependent behaviours, so extracting reliable quantitative chemical composition and structural or property information from microscopy and spectroscopy data is typically difficult and time-consuming.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SEM-EDS combined with machine learning support chemical quantification?",{"text":80,"@type":76},"By processing SEM-EDS hyperspectral spectrum images with ML methods, chemical composition at the nanoscale can be quantified, enabling analysis such as mapping and separating signals from nanowire and substrate regions.",{"name":82,"@type":73,"acceptedAnswer":83},"What benefits do machine learning techniques bring to microscopic characterisation workflows?",{"text":84,"@type":76},"ML techniques enhance and simplify characterisation while minimizing data analysis bias and uncertainty, and they help 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