MoSe <sub>2</sub> /SWNT core–shell hybrids with space-charge-limited conduction and nonlinear dynamics for in-materio physical reservoir computing
This study presents the synthesis, characterization, and application of molybdenum diselenide/single-walled carbon nanotube (MoSe<sub>2</sub>/SWNT) core-shell structures as a new platform for in<i>-</i>materio physical reservoir computing. The hybrid material was fabricated <i>via</i> a modified hydrothermal process, yielding a conductive SWNT network uniformly coated with semiconducting MoSe<sub>2</sub>. Structural and electrical characterization studies (XPS, XRD, SEM, TEM, <i>I</i>-<i>V</i>, and EIS) confirm a crystalline fibrous core-shell morphology that exhibits a voltage-driven transition from a capacitive high-resistance state to a space-charge-limited conduction (SCLC) regime. Physical reservoir computing based on MoSe<sub>2</sub>/SWNTs thus leverages SCLC dynamics, where trap-controlled transport generates higher harmonics and short-term memory, providing the essential nonlinearity and fading memory required for temporal processing. Consequently, the MoSe<sub>2</sub>/SWNT device achieves strong performance in benchmark tasks, including waveform reconstruction (NMSE < 0.1 across multiple periodic functions), NARMA2 time-series prediction (90% accuracy), and memory capacity evaluation. These results establish a direct link between device physics and computational capability, highlighting MoSe<sub>2</sub>/SWNT hybrids as a scalable candidate for next-generation neuromorphic hardware.
Authors
- Saman Azhari (ORCID: https://orcid.org/0000-0002-7043-0909)
- Alif Syafiq Kamarol Zaman (ORCID: https://orcid.org/0000-0001-7121-8136)
- Hirofumi Tanaka (ORCID: https://orcid.org/0000-0002-4378-5747)
- Muzhen XU (ORCID: https://orcid.org/0009-0001-7103-4019)
- Yuki Usami
Institutions
- Waseda University (JP)
- Kyushu Institute of Technology (JP)
Publication Details
- Journal
- Nanoscale
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1039/d5nr03974f
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science
- Core Research for Evolutional Science and Technology
- Advanced Low Carbon Technology Research and Development Program
- ACT-X