JOURNAL ARTICLE

Magnetic Carbon as an Adsorbent for the Enrichment of Carbamate Pesticides in Magnetic Solid Phase Extraction Prior to High Performance Liquid Chromatography.

  • Published In: Journal of Chromatographic Science, 2025, v. 63, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Homchun, Poonsiri; Gamonchuang, Jirasak; Burakham, Rodjana 3 of 3

Abstract

The article focuses on the development and validation of a simple one-step synthesis method to prepare magnetic carbon material used as an adsorbent in magnetic solid phase extraction (MSPE) for preconcentrating carbamate pesticides prior to their determination by high performance liquid chromatography with photodiode array detection (HPLC/PDA). The optimized MSPE procedure employed 50 mg of magnetic carbon to extract carbamates from 30 mL fruit sample solutions with rapid vortex-assisted adsorption and elution steps, achieving linear detection ranges of 5–200 μg L⁻¹, limits of detection between 3–40 μg L⁻¹, and satisfactory recoveries (70.2%–120.0%) with relative standard deviations below 11.6%. Characterization confirmed the composite nature of the magnetic carbon, which exhibited strong ferromagnetism facilitating easy magnetic separation. The method demonstrated effective enrichment and precision for carbamate pesticide analysis in various fruit samples, offering a rapid, eco-friendly, and straightforward alternative to more complex sorbent preparation techniques.

Additional Information

  • Source:Journal of Chromatographic Science. 2025/05, Vol. 63, Issue 5, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2025
  • ISSN:0021-9665
  • DOI:10.1093/chromsci/bmaf030
  • Accession Number:188155009
  • Copyright Statement:Copyright of Journal of Chromatographic Science is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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