Zhang Yongsheng, Yuan Jian, Gao Rui, Zhao Yang, Ye Zefu, Zhu Zhujun, Zhang Peihua, Zhang Lei, Yin Wangbao, Jia Suotang
State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan, 030006, China; Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, 030006, China.
Beijing Research Institute of Uranium Geology, Beijing, 100029, China.
Talanta. 2024 Dec 1;280:126747. doi: 10.1016/j.talanta.2024.126747. Epub 2024 Aug 22.
Ash content, as a crucial indicator of coal quality, its rapid and accurate determination is pivotal to improve the energy utilization of coal and reduce environmental pollution. Traditional spectroscopic methods face significant challenges in acquiring accurate information from coal samples due to the notorious matrix effects arising from their complex composition, vast molecular structure, and diverse coal types. In this study, the feasibility of total reflection X-ray fluorescence (TXRF) combined with partial least squares (PLS) for the determination of coal ash was firstly investigated based on the TXRF being unaffected by matrix effects. Firstly, coal samples were prepared as suspensions, and the effects of sample particle size and different dispersants on the results of TXRF analyses were evaluated. The accuracy and applicability of the chosen sample preparation strategies were further validated using inductively coupled plasma mass spectrometry (ICP-MS) and two certified reference materials (CRMs). Subsequently, based on the analysis of 19 coal samples, the impact of three different predictive variables on the performance of the PLS model was investigated: (a) TXRF full spectrum normalized by the net intensity of the internal standard; (b) net intensity of characteristic peaks for 12 elements (Al, Si, K, Ca, Ti, Fe, Cr, Mn, Cu, Ni, and Sr) normalized by the net intensity of the internal standard; (c) concentrations of the aforementioned 12 elements. The results demonstrate that the PLS model constructed usingthe TXRF full spectrum normalized by the net intensity of the internal standard exhibits the best predictive capabilities, with the determination coefficient of calibration set (R) and mean square error (MSE) of the prediction set reaching 0.9736 and 0.99 %, respectively. Moreover, the measurement accuracy of this model was six times greater than that obtained with traditional X-ray fluorescence (XRF). Presented analytical results display the possibilities of combining TXRF with PLS for coal quality evaluation.
灰分作为煤炭质量的关键指标，其快速准确测定对于提高煤炭能源利用效率和减少环境污染至关重要。传统光谱方法由于煤炭样品组成复杂、分子结构庞大且煤种多样而产生的显著基体效应，在从煤炭样品中获取准确信息方面面临重大挑战。本研究基于全反射X射线荧光光谱法（TXRF）不受基体效应影响的特点，首先研究了TXRF结合偏最小二乘法（PLS）测定煤灰分的可行性。首先，将煤样品制备成悬浮液，并评估了样品粒度和不同分散剂对TXRF分析结果的影响。使用电感耦合等离子体质谱法（ICP-MS）和两种有证标准物质（CRM）进一步验证了所选样品制备策略的准确性和适用性。随后，基于对19个煤样品的分析，研究了三种不同预测变量对PLS模型性能的影响：（a）以内标净强度归一化的TXRF全谱；（b）以内标净强度归一化的12种元素（Al、Si、K、Ca、Ti、Fe、Cr、Mn、Cu、Ni和Sr）特征峰的净强度；（c）上述12种元素的浓度。结果表明，使用以内标净强度归一化的TXRF全谱构建的PLS模型具有最佳预测能力，校正集的决定系数（R）和预测集的均方误差（MSE）分别达到0.9736和0.99%。此外，该模型的测量精度比传统X射线荧光光谱法（XRF）提高了6倍。所呈现的分析结果展示了TXRF与PLS相结合用于煤炭质量评价的可能性。
