{"title":"The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives.","authors":"Rick S van den Hurk, Bob W J Pirok, Tijmen S Bos","doi":"10.1007/s10337-025-04406-7","DOIUrl":null,"url":null,"abstract":"<p><p>The application of artificial intelligence (AI) and machine learning (ML) is rapidly expanding and has begun to make a significant impact on polymer development and characterization. This perspective article explores the current state of AI in this field and highlights areas where its potential remains underutilized. While the optimization of polymer synthesis to achieve desired properties and the classification of polymer types are well-established, opportunities for AI integration in detailed characterization, analytical method development, and data processing remain largely untapped. Greater automation of the analytical laboratory, whether through dedicated algorithms or AI-driven solutions, will enable analytical chemists to focus more on addressing research questions and interpreting results, rather than on method development and routine measurements.</p>","PeriodicalId":518,"journal":{"name":"Chromatographia","volume":"88 5","pages":"357-363"},"PeriodicalIF":1.2000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116698/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chromatographia","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1007/s10337-025-04406-7","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/4 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
The application of artificial intelligence (AI) and machine learning (ML) is rapidly expanding and has begun to make a significant impact on polymer development and characterization. This perspective article explores the current state of AI in this field and highlights areas where its potential remains underutilized. While the optimization of polymer synthesis to achieve desired properties and the classification of polymer types are well-established, opportunities for AI integration in detailed characterization, analytical method development, and data processing remain largely untapped. Greater automation of the analytical laboratory, whether through dedicated algorithms or AI-driven solutions, will enable analytical chemists to focus more on addressing research questions and interpreting results, rather than on method development and routine measurements.
期刊介绍：
Separation sciences, in all their various forms such as chromatography, field-flow fractionation, and electrophoresis, provide some of the most powerful techniques in analytical chemistry and are applied within a number of important application areas, including archaeology, biotechnology, clinical, environmental, food, medical, petroleum, pharmaceutical, polymer and biopolymer research. Beyond serving analytical purposes, separation techniques are also used for preparative and process-scale applications. The scope and power of separation sciences is significantly extended by combination with spectroscopic detection methods (e.g., laser-based approaches, nuclear-magnetic resonance, Raman, chemiluminescence) and particularly, mass spectrometry, to create hyphenated techniques. In addition to exciting new developments in chromatography, such as ultra high-pressure systems, multidimensional separations, and high-temperature approaches, there have also been great advances in hybrid methods combining chromatography and electro-based separations, especially on the micro- and nanoscale. Integrated biological procedures (e.g., enzymatic, immunological, receptor-based assays) can also be part of the overall analytical process.
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