New sensing platforms based on screen-printed carbon electrodes modified with composites based on polystyrene sulfonate and oxidized multi-walled carbon nanotubes (PSS/MWCNTs-COOH/SPCE) have been used to develop a novel HPLC method with electrochemical detection (ECD) for the determination of the most used synthetic phenolic antioxidants in cosmetics: butylhydroxytoluene (BHT), butylhydroxyanisole (BHA), tert-butylhydroquinone (TBHQ) and propyl gallate (PG). Optimal separation conditions were achieved using methanol: 0.10 mol L acetate solution at pH 6 as mobile phase with a gradient elution program from 60 to 90% of methanol percentage in 15 min. The electrochemical detection was carried out in amperometric mode using the PSS/MWCNTs-COOH/SPCE at + 0.80 V vs. Ag. Under these optimal separation and detection conditions, the limits of detection (LOD) were between 0.11 and 0.25 mg L. These LOD values were better, especially for BHT, than those previously published in other HPLC methods. Linear ranges from 0.37 mg L, 0.83 mg L, 0.69 mg L and 0.56 mg L to 10 mg L were obtained for PG, TBHQ, BHA and BHT, respectively. RSD values equal or lower than 5% and 8% were achieved for repeatability and reproducibility, respectively. The HPLC-ECD method was successfully applied to analyze different cosmetic samples. Recovery values within 83-109% were obtained in the validation studies.
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http://dx.doi.org/10.1007/s00604-022-05552-7 | DOI Listing |
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School of Natural Sciences, Macquarie University, Macquarie Park, Sydney, Australia.
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Shandong University, Department of Chemistry, 27 South Shanda Road, 250100, Jinan, CHINA.
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Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin 14195, Germany.
In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model.
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December 2024
Department of Biochemistry, Government College Women University, Faisalabad, Pakistan.
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