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.

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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

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article

MoSe 2 /SWNT core–shell hybrids with space-charge-limited conduction and nonlinear dynamics for in-materio physical reservoir computing

Saman Azhari, Alif Syafiq Kamarol Zaman, Hirofumi Tanaka, Muzhen XU et al.
Nanoscale
Neural Networks and Reservoir Computing
article

MoSe 2 /SWNT core–shell hybrids with space-charge-limited conduction and nonlinear dynamics for in-materio physical reservoir computing

Saman Azhari, Alif Syafiq Kamarol Zaman, Hirofumi Tanaka, Muzhen XU, Yuki Usami
article en

Abstract

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.

NanoscaleVol. 18(10)
Waseda University (JP), Kyushu Institute of Technology (JP)
Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology, Advanced Low Carbon Technology Research and Development Program, ACT-X
Openalex Percentile: Top 92%
Neural Networks and Reservoir Computing
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