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 st...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 proofof-concept,phosphate levels in Hoagland solution,a standard plant nutrient medium,were monitored,achieving an r 2 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 costeffective approach provides a scalable solution for real-time phosphate detection with high sensitivity and reproducibility,meeting agricultural demands.展开更多
Background:Decisions regarding the conversion of land from an existing crop to bioenergy crops are critical for the sustainable production of both food and fuels.This study seeks to establish criteria for delineating ...Background:Decisions regarding the conversion of land from an existing crop to bioenergy crops are critical for the sustainable production of both food and fuels.This study seeks to establish criteria for delineating land as“economically marginal”,and thus suited for growing switchgrass.Methods:In this case study of an Illinois agricultural field,the profitability of switchgrass,with farmgate prices of$44 Mg−1,$66 Mg−1,or$88 Mg−1,was compared to corn and soybean crop prices.Further,the study also evaluates the profitability of switchgrass when replacing corn‐based yield estimates from the Soil Productivity Index(SPI)of Illinois.Results:Based on a dry‐matter yield of 10.45 Mg ha−1,switchgrass can compete with soybeans only at the high price of$88 Mg−1,but depending on location,can compete with corn at$66 Mg−1.Across Illinois,at$88 ha−1,all Illinois land with SPI<100%and 95%of land under SPI class C(SPI 100–116)is profitable under switchgrass.Switchgrass may not be profitable relative to corn grown in the SPI class A(SPI>133)and only 7%of class B(SPI 117–132).Conclusions:Our results show that land with drainage and erosion limitations is economically marginal when corn and soybean yields are low,and the farmgate price for switchgrass is greater than$66 Mg−1.However,this may not be possible on land where switchgrass is replacing frequent soybean rotations(corn–soybean ratio≤1).Land used to produce only soybeans may only be marginal at the farmgate price of$88 Mg−1.Further studies need to be conducted to identify how much land can be converted to switchgrass without harming corn production.展开更多
基金financially supported by the National Science Foundation MADEPUBLIC Future Manufacturing Research Grant Program(NSF Award CMMI2037026)SEM and AFM characterization were performed at the Center for Nanoscale Materials,a U.S.Department of Energy(DOE)Office of Science User Facility,supported by the U.S.DOE,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357This work made use of the shared facilities at the University of Chicago Materials Research Science and Engineering Center,supported by the National Science Foundation under award number DMR-2011854.
文摘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 proofof-concept,phosphate levels in Hoagland solution,a standard plant nutrient medium,were monitored,achieving an r 2 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 costeffective approach provides a scalable solution for real-time phosphate detection with high sensitivity and reproducibility,meeting agricultural demands.
基金supported by the University of Illinois at Urbana‐Champaign Hatch Project(1001878)the US Department of Energy,Energy Efficiency and Renewable Energy(EERE),Bioenergy Technologies Office(BETO),grant number DE‐EE0008521.
文摘Background:Decisions regarding the conversion of land from an existing crop to bioenergy crops are critical for the sustainable production of both food and fuels.This study seeks to establish criteria for delineating land as“economically marginal”,and thus suited for growing switchgrass.Methods:In this case study of an Illinois agricultural field,the profitability of switchgrass,with farmgate prices of$44 Mg−1,$66 Mg−1,or$88 Mg−1,was compared to corn and soybean crop prices.Further,the study also evaluates the profitability of switchgrass when replacing corn‐based yield estimates from the Soil Productivity Index(SPI)of Illinois.Results:Based on a dry‐matter yield of 10.45 Mg ha−1,switchgrass can compete with soybeans only at the high price of$88 Mg−1,but depending on location,can compete with corn at$66 Mg−1.Across Illinois,at$88 ha−1,all Illinois land with SPI<100%and 95%of land under SPI class C(SPI 100–116)is profitable under switchgrass.Switchgrass may not be profitable relative to corn grown in the SPI class A(SPI>133)and only 7%of class B(SPI 117–132).Conclusions:Our results show that land with drainage and erosion limitations is economically marginal when corn and soybean yields are low,and the farmgate price for switchgrass is greater than$66 Mg−1.However,this may not be possible on land where switchgrass is replacing frequent soybean rotations(corn–soybean ratio≤1).Land used to produce only soybeans may only be marginal at the farmgate price of$88 Mg−1.Further studies need to be conducted to identify how much land can be converted to switchgrass without harming corn production.
