Graphene In-Sensor Compute Device for Plant Hydration Monitoring

Area of Science:

• Materials Science
• Biotechnology
• Neuromorphic Engineering

Background:

• Analog sensing devices with artificial synaptic behavior are key for scalable, energy-efficient environmental monitoring.
• Graphene-based materials offer unique electronic properties for advanced sensor applications.

Purpose of the Study:

• To develop and evaluate graphene-based, leaf-gated in-sensor compute devices for plant hydration monitoring.
• To demonstrate the devices' capability for both sensing hydration and synapse-like conductance modulation.

Main Methods:

• Fabrication of graphene-based, leaf-gated transistors for in-sensor computation.
• Utilizing channel conductance trends to measure plant water content and ion mobility.
• Encoding memory-like states through electrical stimulation for neuromorphic applications.

Main Results:

• Devices exhibited synapse-like conductance modulation, tracking hydration-dependent ion mobility in leaves.
• Demonstrated linear potentiation, depression, and short-term memory retention.
• Achieved ultralow power operation (23 aJ/μS write energy, 0.23 μW read power) and minimal weight (9 mg) on Monstera leaves without physiological disruption.

Conclusions:

• Graphene-based in-sensor compute devices provide a promising platform for real-time, long-term plant hydration monitoring.
• Integration of computation and sensing in a biocompatible platform minimizes data transmission and enables edge neuromorphic applications.
• The technology offers a scalable and energy-efficient solution for precision agriculture and environmental sensing.