用激光诱导击穿光谱研究石墨的化学指纹图谱

IF 3.1 2区 化学 Q2 CHEMISTRY, ANALYTICAL
Róbert Arató, Derrick Quarles, Gabriella Obbágy, Zsolt Dallos, Miklós Arató, Phillip Gopon and Frank Melcher
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引用次数: 0

摘要

石墨是可持续能源技术的关键原材料,建立其可追溯性对于确保未来负责任的采购至关重要。本研究展示了通过激光诱导击穿光谱(LIBS)获得的一组全面的天然石墨精矿的地图。LIBS以前所未有的速度生成多元素数据,即使是非理想烧蚀特性的样品,如压制石墨颗粒。生成的数据用于构建元素图,以阐明元素的化学分布以及进行多元分类。天然石墨精矿的化学成分不均匀。因此,石墨精矿LIBS指纹图谱是来自纯石墨和矿物杂质的LIBS信号的异质混合物,这些信号要么代表与石墨共生的晶体,要么是由于自然或人工过程而被吸附在石墨薄片上。观察到的化学非均质性是单个矿床的突出指纹,尽管在同一矿床的不同样品之间也普遍存在非均质性。生成的多变量数据集非常适合于多变量数据分析。随机森林分类器在广泛的超参数范围内表现出稳健的性能,实现了超过90%的分类精度。无论采用何种分析和分类方法,精矿的异质性都对分类提出了重大挑战。将化学性质不同的样品添加到同一分类组(即石墨沉积)并不一定妨碍正确分类,并使该方法的常规应用成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Towards a chemical fingerprint of graphite by laser-induced breakdown spectroscopy†

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.

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来源期刊
CiteScore
6.20
自引率
26.50%
发文量
228
审稿时长
1.7 months
期刊介绍: Innovative research on the fundamental theory and application of spectrometric techniques.
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