Unverified paper record
Cobalt oxide based microneedle sensor for direct detection of glucose from plant leaves
Micro & Nano Manufacturing · 2 Apr 2026 · 10.1007/s44374-026-00017-w
Abstract
Glucose is a crucial biomarker reflecting plant metabolism and growth. While glucose sensing is well-established in human healthcare, continuous in situ measurement in living plants remains challenging. In this study, we developed a non-enzymatic microneedle (MN) electrochemical sensor for the direct, real-time monitoring of glucose in plant leaves. To achieve this, we utilized a highly customizable and cost-effective fabrication strategy. A three-electrode MN system was fabricated by combining SU-8 micromolding with 3D printing of a carbon paste circuit, significantly reducing the reliance on complex cleanroom processes. The working electrode was then functionalized via a single-step co-electrodeposition of Co 3 O 4 catalysts and multiwall carbon nanotubes in a chitosan matrix. This co-deposition effectively compensated for the inherently low electrical conductivity of Co 3 O 4 . The optimized sensor exhibited reliable in vitro analytical performance. Furthermore, the MN sensor was inserted into living leaves to monitor glucose levels under varying light and dark conditions. The sensor successfully recorded dynamic real-time current signals, showing an average of 71.5 ± 11.0 nA in the dark and 9.6 ± 2.8 nA in the light. This customizable MN platform provides a practical diagnostic tool for continuous plant health monitoring.
Plant phenotyping relevance
植物葉内のグルコースをリアルタイム測定するマイクロニードル電気化学センサーを開発し、生葉での連続モニタリングまで実証しており、植物生理状態の取得方法が研究の中心である。
abstractwe developed a non-enzymatic microneedle (MN) electrochemical sensor for the direct, real-time monitoring of glucose in plant leaves.
abstractFurthermore, the MN sensor was inserted into living leaves to monitor glucose levels under varying light and dark conditions.
Code and data availability
The paper reports a Co3O4/MWCNT microneedle glucose sensor for plant leaves, but the Data availability statement explicitly says no datasets were generated or analysed, and no public phenotype data, images, code, models, or supplementary deposits with authors' URLs are mentioned anywhere in the supplied blocks.
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