Simultaneous electrochemical detection of Gd, Dy, and Eu using boron-doped diamond electrode combined with machine learning

Santhy Wyantuti, Anisa Amalia Citra, Natasha Fransisca, Ari Hardianto,  Irkham, Uji Pratomo, Fajriana Shafira Nurrusyda, Nova Rachmadona, Rudiawan Edwin, Jacob Yan Mulyana,
Simultaneous electrochemical detection of Gd, Dy, and Eu using boron-doped diamond electrode combined with machine learning,
International Journal of Electrochemical Science,
Volume 20, Issue 11,
2025,
101189,
ISSN 1452-3981,
https://doi.org/10.1016/j.ijoes.2025.101189.
(https://www.sciencedirect.com/science/article/pii/S1452398125002640)
Abstract: Rare earth elements (REE) are currently high-demand mineral commodities for various countries. The electrochemical technique plays a crucial role in determining REE, offering high sensitivity compared to X-ray fluorescence spectrometry (XRF). This study aimed to detect the content of Gd, Dy, and Eu in a mixture without passing through a chemical separation, using the Differential Pulse Voltammetry (DPV) method and Boron Doped Diamond (BDD) working electrode combined with machine learning. A total of 125 variations of Gd, Dy, and Eu mixture solutions were prepared as the training set and measured using the DPV method. By employing the BDD working electrode, the current peak of Eu appeared separately from that of Gd and Dy, at a potential of −0.6 V. Meanwhile, Gd and Dy appeared in a single current peak at a potential of −1.4 V. Eu exhibited a Limit of Detection (LoD) and Limit of Quantification (LoQ) at 3.040 ppm and 9.211 ppm, Gd at 17.201 ppm and 7.475 ppm, as well as Dy at 22.652 ppm and 5.676 ppm, respectively. After algorithm selection and preprocessing in machine learning, the best model obtained was GLMNET for Eu with an R2 of 0.853, Dy at 0.376, and SVM for Gd with 0.557. These algorithms correctly predicted the closeness of the percentage recovery of the Gd, Dy, and Eu combination to the actual percentage recovery.
Keywords: Voltammetry; Boron Doped Diamond; Rare Earth Elements; Machine Learning; Differential Pulse Voltammetry