ArticleRSC advances2025
High sensitivity terahertz metamaterial sensor for trace pesticide detection.
Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Terahertz Metasurface with Four-Degree-of-Freedom Geometric Encoding for Broadband Multichannel Fingerprint Sensing.Nanomaterials (Basel, Switzerland) · 2026Article
- A terahertz metamaterial-based approach for detecting trace 1-naphthaleneacetic acid residuesRSC advances · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pesticides play a vital role in agriculture by protecting crops from pests and diseases and ensuring high yields. However, improper use poses severe risks to human health and the environment, emphasizing the need for the precise detection of pesticide residues. Methods like chromatography and biosensors are commonly used, but each has limitations in stability, selectivity, or applicability. THz metamaterial (MM) sensors offer high sensitivity through changes in spectral response, making them a promising alternative to address many of these challenges. They enable the identification of specific pesticide molecules at extremely low concentrations through resonance frequency shifts. By incorporating innovative design features, these sensors can detect trace pesticide residues with ease, affordability, and rapid response. In this work, we designed an MM sensor with near-perfect absorption at its resonance frequency. The high absorption of 99.43% resulted from impedance matching with the free space. The MM demonstrated an impressive figure of merit, highlighting its effectiveness as a sensing device. This approach has significant potential for food safety and environmental monitoring applications, providing efficient and reliable detection solutions.
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Registered trials
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