Robust biodegradable synapse with sub-biological energy and extended memory for intelligent reflexive system
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.
Authors
- Yoojin Chang (ORCID: https://orcid.org/0000-0002-7603-8668)
- Sangyun Na
- Hye Jeong Oh (ORCID: https://orcid.org/0009-0006-9365-9016)
- Cheolhong Park (ORCID: https://orcid.org/0000-0001-8992-6317)
- Yun Goo Ro (ORCID: https://orcid.org/0000-0002-7023-086X)
- Hyunhyub Ko (ORCID: https://orcid.org/0000-0003-2111-6101)
- Min Sub Kwak
- Yong‐Jin Park (ORCID: https://orcid.org/0000-0003-4345-7492)
- Jaeho Kim (ORCID: https://orcid.org/0009-0005-9978-9551)
- Jeeyoon Kim (ORCID: https://orcid.org/0000-0002-5373-4089)
- Seokhee Jung (ORCID: https://orcid.org/0009-0009-4942-588X)
Institutions
- Ulsan National Institute of Science and Technology (KR)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1038/s41467-025-66511-3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation