Rational Design of a Molecularly Imprinted Sensor on a Biomass Carbon Platform for Glyphosate Monitoring in Traditional Chinese Medicines

A molecularly imprinted electrochemical sensor was developed for the selective and sensitive detection of glyphosate in Traditional Chinese Medicine samples. An excellent conductive hierarchical porous carbon substrate made from sodium alginate and ammonium chloride co-carbonization was used to build the sensor. The molecularly imprinted polymer layer was systematically designed using Density Functional Theory calculations, which identified nicotinamide as the optimal functional monomer. A deep eutectic solvent was utilized as an effective green eluent for template removal. Under optimized conditions, the sensor demonstrated a wide linear detection range from 1.0 × 10<sup>-9</sup> to 1.0 × 10<sup>-6</sup> M with an exceptionally low detection limit of 8.8 × 10<sup>-10</sup> M. The sensor exhibited satisfactory reproducibility (RSD = 3.35%, <i>n</i> = 6), repeatability (RSD = 5.0% over 6 cycles), and robust stability (retaining >90% initial response after 10 days). The sensor displayed satisfactory recovery rates of 94.47-112.23% and RSD values ranging from 1.37-3.01% when applied to real traditional Chinese medicine samples, thereby confirming its accuracy and practical utility for glyphosate residue analysis in complex matrices. This study introduces an effective sensing platform that integrates rational design principles with environmentally friendly synthesis strategies for quality control in traditional medicine applications.

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Publication Details

Journal
Polymers
Published
2025-12-22
DOI
https://doi.org/10.3390/polym18010021
Primary Topic
Pesticide and Herbicide Environmental Studies
Type
article
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article

Rational Design of a Molecularly Imprinted Sensor on a Biomass Carbon Platform for Glyphosate Monitoring in Traditional Chinese Medicines

Guodi Lu, Xin Wang, Delai Zhou, Xuxia Liu et al.
Polymers
Pesticide and Herbicide Environmental Studies
article

Rational Design of a Molecularly Imprinted Sensor on a Biomass Carbon Platform for Glyphosate Monitoring in Traditional Chinese Medicines

Guodi Lu, Xin Wang, Delai Zhou, Xuxia Liu, Fude Yang, Jia Hou, Jian Xu
article en

Abstract

A molecularly imprinted electrochemical sensor was developed for the selective and sensitive detection of glyphosate in Traditional Chinese Medicine samples. An excellent conductive hierarchical porous carbon substrate made from sodium alginate and ammonium chloride co-carbonization was used to build the sensor. The molecularly imprinted polymer layer was systematically designed using Density Functional Theory calculations, which identified nicotinamide as the optimal functional monomer. A deep eutectic solvent was utilized as an effective green eluent for template removal. Under optimized conditions, the sensor demonstrated a wide linear detection range from 1.0 × 10<sup>-9</sup> to 1.0 × 10<sup>-6</sup> M with an exceptionally low detection limit of 8.8 × 10<sup>-10</sup> M. The sensor exhibited satisfactory reproducibility (RSD = 3.35%, <i>n</i> = 6), repeatability (RSD = 5.0% over 6 cycles), and robust stability (retaining >90% initial response after 10 days). The sensor displayed satisfactory recovery rates of 94.47-112.23% and RSD values ranging from 1.37-3.01% when applied to real traditional Chinese medicine samples, thereby confirming its accuracy and practical utility for glyphosate residue analysis in complex matrices. This study introduces an effective sensing platform that integrates rational design principles with environmentally friendly synthesis strategies for quality control in traditional medicine applications.

PolymersVol. 18(1)
Chinese Academy of Sciences (CN), Lanzhou Institute of Chemical Physics (CN), Gansu University of Traditional Chinese Medicine (CN)
Life in Land
Openalex Percentile: Top 51%
Pesticide and Herbicide Environmental Studies
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Rational Design of a Molecularly Imprinted Sensor on a Biomass Carbon Platform for Glyphosate Monitoring in Traditional Chinese Medicines — Guodi Lu, Xin Wang, et al. · Polymers (2025) | TGRS Research Map | TGRS