Robust biodegradable synapse with sub-biological energy and extended memory for intelligent reflexive system

Yoojin Chang, Sangyun Na, Yun Goo Ro, Cheolhong Park, Seokhee Jung, Yong-Jin Park, Min Sub Kwak, Jeeyoon Kim, Hyeji Oh, Jaejun Kim and Hyunhyub Ko ()
Additional contact information
Yoojin Chang: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Sangyun Na: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Yun Goo Ro: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Cheolhong Park: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Seokhee Jung: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Yong-Jin Park: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Min Sub Kwak: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Jeeyoon Kim: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Hyeji Oh: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Jaejun Kim: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering
Hyunhyub Ko: Ulsan National Institute of Science and Technology (UNIST), School of Energy and Chemical Engineering

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Biodegradable artificial synapses hold great promise for sustainable neuromorphic electronics, yet combining long-term memory, ultralow energy consumption, and mechanical robustness remains challenging. Here, we report a fully biodegradable multilayer artificial synapse (M-AS) composed of crosslinked chitosan–guar gum (CS–GG) ion-active layers (IALs) and a cellulose acetate (CA) ion-binding layer (IBL). This trilayer architecture enhances ion trapping via ion-dipole coupling (IDC) at the IAL–IBL interface, while hydrogen-bonded crosslinking within the CS–GG matrix enhances mechanical and environmental stability. Sodium chloride, embedded in the IALs, serves as a mobile ionic species analogous to biological neurotransmitters, enabling low-voltage ion migration. Upon electrical stimulation, ion migration and dipole alignment induce IDC, leading to partial ion retention and cascade-like postsynaptic current responses that support memory formation. The M-AS supports key synaptic functionalities—including paired-pulse facilitation, short-term and long-term plasticity, multilevel memory encoding, and bidirectional modulation—under sub-millivolt operation. It achieves the longest long-term memory time (5944 s) reported among biodegradable artificial synapses and an energy consumption (0.85 fJ/event) lower than that of biological synapses. Integration with a thermistor and robotic actuator enables a bioinspired reflexive system capable of adaptive, stimulus-dependent learning and reflex-like behaviors. These results demonstrate the potential of M-AS for low-power, intelligent human–machine interfaces.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-66511-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-66511-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-66511-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().