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

## TL;DR

A new sensor was created to detect glyphosate in traditional Chinese medicines using a carbon-based platform and molecular imprinting for accurate and sensitive results.

## Contribution

The study introduces a rationally designed, eco-friendly molecularly imprinted sensor for glyphosate detection in complex traditional medicine samples.

## Key findings

- The sensor has a wide linear detection range from 1.0 × 10−9 to 1.0 × 10−6 M with a low detection limit of 8.8 × 10−10 M.
- The sensor showed high reproducibility (RSD = 3.35%) and retained >90% of its initial response after 10 days.
- It achieved recovery rates of 94.47–112.23% in real traditional Chinese medicine samples, confirming its accuracy.

## 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 × 10−9 to 1.0 × 10−6 M with an exceptionally low detection limit of 8.8 × 10−10 M. The sensor exhibited satisfactory reproducibility (RSD = 3.35%, n = 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.