Róbert Arató, Derrick Quarles, Gabriella Obbágy, Zsolt Dallos, Miklós Arató, Phillip Gopon and Frank Melcher
{"title":"用激光诱导击穿光谱研究石墨的化学指纹图谱","authors":"Róbert Arató, Derrick Quarles, Gabriella Obbágy, Zsolt Dallos, Miklós Arató, Phillip Gopon and Frank Melcher","doi":"10.1039/D5JA00053J","DOIUrl":null,"url":null,"abstract":"<p >Graphite is a critical raw material for sustainable energy technologies, and establishing its traceability is crucial for ensuring responsible sourcing in the future. This study presents maps acquired on a comprehensive set of natural graphite concentrates <em>via</em> Laser-induced Breakdown Spectroscopy (LIBS). LIBS generates multi-elemental data at an unprecedented speed even from samples with non-ideal ablation characteristics, such as pressed graphite pellets. The generated data is used for constructing elemental maps to shed light on the chemical distribution of elements as well as for multivariate classification. Natural graphite concentrates exhibit inhomogeneous chemical composition. As such, the graphite concentrate LIBS-fingerprint is a heterogeneous mixture of LIBS signals from pure graphite and mineral impurities, which either represent crystal intergrowth with graphite, or they are adsorbed on graphite flakes as a result of natural or artificial processes. The observed chemical heterogeneity serves as a prominent fingerprint of individual deposits, although the heterogeneity is also omnipresent between different samples of the same deposit. The generated multivariate dataset is well suited for multivariate data analysis. Random forest classifiers show a robust performance across a broad range of hyperparameters, achieving over 90% classification accuracy. The heterogeneity of the concentrates presents a significant challenge for classification, regardless of the analytical and classification approach used. The addition of chemically different samples to the same classification group (<em>i.e.</em>, graphite deposit) does not necessarily hinder correct classification and renders the routine application of the method possible.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":" 9","pages":" 2526-2537"},"PeriodicalIF":3.1000,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/ja/d5ja00053j?page=search","citationCount":"0","resultStr":"{\"title\":\"Towards a chemical fingerprint of graphite by laser-induced breakdown spectroscopy†\",\"authors\":\"Róbert Arató, Derrick Quarles, Gabriella Obbágy, Zsolt Dallos, Miklós Arató, Phillip Gopon and Frank Melcher\",\"doi\":\"10.1039/D5JA00053J\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Graphite is a critical raw material for sustainable energy technologies, and establishing its traceability is crucial for ensuring responsible sourcing in the future. This study presents maps acquired on a comprehensive set of natural graphite concentrates <em>via</em> Laser-induced Breakdown Spectroscopy (LIBS). LIBS generates multi-elemental data at an unprecedented speed even from samples with non-ideal ablation characteristics, such as pressed graphite pellets. The generated data is used for constructing elemental maps to shed light on the chemical distribution of elements as well as for multivariate classification. Natural graphite concentrates exhibit inhomogeneous chemical composition. As such, the graphite concentrate LIBS-fingerprint is a heterogeneous mixture of LIBS signals from pure graphite and mineral impurities, which either represent crystal intergrowth with graphite, or they are adsorbed on graphite flakes as a result of natural or artificial processes. The observed chemical heterogeneity serves as a prominent fingerprint of individual deposits, although the heterogeneity is also omnipresent between different samples of the same deposit. The generated multivariate dataset is well suited for multivariate data analysis. Random forest classifiers show a robust performance across a broad range of hyperparameters, achieving over 90% classification accuracy. The heterogeneity of the concentrates presents a significant challenge for classification, regardless of the analytical and classification approach used. The addition of chemically different samples to the same classification group (<em>i.e.</em>, graphite deposit) does not necessarily hinder correct classification and renders the routine application of the method possible.</p>\",\"PeriodicalId\":81,\"journal\":{\"name\":\"Journal of Analytical Atomic Spectrometry\",\"volume\":\" 9\",\"pages\":\" 2526-2537\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.rsc.org/en/content/articlepdf/2025/ja/d5ja00053j?page=search\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Analytical Atomic Spectrometry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://pubs.rsc.org/en/content/articlelanding/2025/ja/d5ja00053j\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Analytical Atomic Spectrometry","FirstCategoryId":"92","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/ja/d5ja00053j","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Towards a chemical fingerprint of graphite by laser-induced breakdown spectroscopy†
Graphite is a critical raw material for sustainable energy technologies, and establishing its traceability is crucial for ensuring responsible sourcing in the future. This study presents maps acquired on a comprehensive set of natural graphite concentrates via Laser-induced Breakdown Spectroscopy (LIBS). LIBS generates multi-elemental data at an unprecedented speed even from samples with non-ideal ablation characteristics, such as pressed graphite pellets. The generated data is used for constructing elemental maps to shed light on the chemical distribution of elements as well as for multivariate classification. Natural graphite concentrates exhibit inhomogeneous chemical composition. As such, the graphite concentrate LIBS-fingerprint is a heterogeneous mixture of LIBS signals from pure graphite and mineral impurities, which either represent crystal intergrowth with graphite, or they are adsorbed on graphite flakes as a result of natural or artificial processes. The observed chemical heterogeneity serves as a prominent fingerprint of individual deposits, although the heterogeneity is also omnipresent between different samples of the same deposit. The generated multivariate dataset is well suited for multivariate data analysis. Random forest classifiers show a robust performance across a broad range of hyperparameters, achieving over 90% classification accuracy. The heterogeneity of the concentrates presents a significant challenge for classification, regardless of the analytical and classification approach used. The addition of chemically different samples to the same classification group (i.e., graphite deposit) does not necessarily hinder correct classification and renders the routine application of the method possible.