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Intelligent machine learning enabled sensor for acyclovir using NiMnO3 flower-like electrocatalyst
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DOI：10.1016/j.mseb.2024.117668.png)
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Hierarchical flower-like NiMnO3 was prepared using a hydrothermal route for oxidative detection of acyclovir (ACV), an antiviral pharmaceutical pollutant. The flower-like structure with an electroactive surface area (EASA) of 0.0952 cm(2) enables non-revisable oxidation of ACV, with differential pulse voltammetry (DPV) and amperometry confirming its robust analytical capability in both high (15-75 mu M) and low (0.1-1.0 mu M) concentration ranges, respectively. Using amperometry, the sensor achieved an estimated limit of detection (LOD) of 1.59 nM (S/N=3) with selective oxidation of ACV and a sensitivity of 1.039 mu A mu M-1 cm(2) in the presence of other common interferants. The adaptation of machine learning (ML) algorithms like random forest, XGBoost, linear regression, and ANN validated sensors' performance and confirmed ANN's superiority in DPV signal interpretation. NiMnO3, as an electrocatalyst for ACV oxidation, validated by ANN modeling, highlights bimetallic oxides' potential as a cost-effective, versatile platform for detecting pharmaceutical pollutants.
Keyword:
Electrocatalyst
Bimetallic oxides
Antiviral drugs
Machine learning
Emerging pollutant
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4.6
论文数:
6.7K
被引数:
2.0W
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