Ecofriendly Printed Wood-Based Honey-Gated Transistors for Artificial Synapse Emulation

• Autor(a) principal: Vieira, Douglas Henrique [UNESP]
• Data de Publicação: 2025
• Outros Autores: Carlos, Emanuel, Ozório, Maíza Silva [UNESP], Morais, Maria, Fortunato, Elvira, Alves, Neri [UNESP], Martins, Rodrigo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1002/aisy.202400760; https://hdl.handle.net/11449/303498
• topic: artificial synapses; honey-gated transistors; printed electronics; sustainable electronics; wood substrates

Printed electronics have traditionally used substrates and materials derived from fuel-based or less abundant and toxic resources, raising environmental concerns. Wood as a substrate reduces processing steps and enables the integration of intelligent functionalities in wooden furniture, offering biodegradability, nontoxicity, and derivation from renewable sources. In this work, sustainably printed transistors using zinc oxide nanoparticles as the active layer and honey electrolyte on wood substrates are demonstrated as a promising approach to reduce the environmental footprint of electronics. Despite the substrate's high roughness, the transistor exhibits excellent performance for screen-printed devices, with low on-voltage of 0.32 ± 0.12 V and high Ion/Ioff of (2.4 ± 0.9) × 104. Further analysis of hysteresis in transfer curves under varying scan rates and sweep ranges reveals the device's ability to adjust memory windows and on-current. Notably, these devices successfully emulate synapses, exhibiting neural facilitation and plasticity, indicating a shift toward sustainable computing. The device's dynamic response to single and successive presynaptic pulses demonstrates its ability to adjust synaptic weight, transition from transient to persistent memory, and pulse width-, frequency-, voltage-, and number-dependent excitatory postsynaptic currents. The successful emulation of the learning–forgetting–relearning–forgetting process underscores the device's potential for use in sustainable high-performance neuromorphic systems.