Real-Time Phosphate Monitoring via Plant-Derived Graphene Ink FET Sensors Integrated with Deep Learning

Rapti Ghosh , Fengxue Zhang , Hyun-June Jang , Janan Hui , Kayla Vittore , Haoyang You , Rozyyev Vepa , Wen Zhuang , Xingkang Huang , Haihui Pu , Jeffrey W. Elam , Stuart J. Rowan , DoKyoung Lee , Elizabeth A. Ainsworth , Mark C. Hersam , Yuxin Chen , Junhong Chen

Energy & Environmental Materials ›› 2026, Vol. 9 ›› Issue (2) : e70144

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Energy & Environmental Materials ›› 2026, Vol. 9 ›› Issue (2) :e70144 DOI: 10.1002/eem2.70144
Research Article
Real-Time Phosphate Monitoring via Plant-Derived Graphene Ink FET Sensors Integrated with Deep Learning
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Abstract

Real-time monitoring of plant nutrient levels, particularly phosphate, is essential for optimizing plant growth and addressing nutrient imbalances in precision agriculture. Conventional sensors mostly suffer from poor stability, reproducibility, matrix effects, and high costs, limiting their scalability and practical application. To overcome these challenges, a deep learning-integrated remote-gate field-effect transistor sensor utilizing a plant-derived graphene electrode is introduced for enhanced performance and reliability. These solution-processed graphene electrodes, composed of cellulose nanocrystals from plant fibers, are functionalized with phosphate-capturing ferritin and serve as the sensing surface, capacitively coupled to a commercial n-type field-effect transistor to address device variability issues. Deep learning integration significantly improved accuracy, enabling robust and precise phosphate detection. The sensor demonstrates a sensitivity of 14.1 mV dec−1 after the pH correction, a coefficient of variation of responses below 5%, and a 1 ng mL−1 (1 ppb) detection limit. As a proof-of-concept, phosphate levels in Hoagland solution, a standard plant nutrient medium, were monitored, achieving an r2 of 0.951 and a coefficient of variation of 5.39%. A handheld prototype system further demonstrates its potential for on-site continuous monitoring. This sustainable and cost-effective approach provides a scalable solution for real-time phosphate detection with high sensitivity and reproducibility, meeting agricultural demands.

Keywords

deep learning / graphene-based FET / handheld prototype sensor / phosphate ion sensing / remote-gate FET

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Rapti Ghosh, Fengxue Zhang, Hyun-June Jang, Janan Hui, Kayla Vittore, Haoyang You, Rozyyev Vepa, Wen Zhuang, Xingkang Huang, Haihui Pu, Jeffrey W. Elam, Stuart J. Rowan, DoKyoung Lee, Elizabeth A. Ainsworth, Mark C. Hersam, Yuxin Chen, Junhong Chen. Real-Time Phosphate Monitoring via Plant-Derived Graphene Ink FET Sensors Integrated with Deep Learning. Energy & Environmental Materials, 2026, 9 (2) : e70144 DOI:10.1002/eem2.70144

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2025 The Author(s). Energy & Environmental Materials published by John Wiley & Sons Australia, Ltd on behalf of Zhengzhou University.

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