Organic-engineered MXenes enabling digital-to-analog switching for neuromorphic application

  • Shijie Chen
  • , Xunlu Li
  • , Zheng Xu
  • , Yanyu Zhao
  • , Qifeng Lu*
  • , Yiming Liu
  • , Cheng Zhang
  • , Xinli Cheng
  • , Shuai Yuan
  • , Nan Wang
  • , Shiqing Zhao
  • , Fangchao Li
  • , Yixiang Li
  • , Chunlan Ma
  • , Yang Li
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

MXene-based devices have emerged as a promising platform for high-speed and multifunctional resistive switching electronics. However, their intrinsic tendency toward oxidation severely restricts practical application. Here, we report a robust strategy to address this challenge by hybridizing MXene nanosheets with a soluble organic semiconductor of BTCN. The BTCN molecules interact with MXene through π–π stacking and electrostatic coupling, enabling stable passivation and tailored interfacial electronic states. Memristors based on inert Au/MXene-BTCN/ITO structure exhibit reproducible digital-type resistive switching with narrow distributions of set/reset voltages, highly stable endurance over 3 × 104 cycles, and ultrafast switching dynamics down to 5 ns. By replacing the top electrode with active Ag, the devices succeed to trigger digital-to-analog synaptic emulators, demonstrating polarity-dependent conductance modulation. Furthermore, a convolutional neural network (CNN) employing MXene-BTCN hybrid memristive arrays achieves a high digit recognition accuracy over 97 %. This work establishes a versatile design principle that couples solution-processable MXene–organic hybrids with electrode engineering, bridging digital memory and analog synaptic functionalities for brain-inspired computing applications.

Original languageEnglish
Article number139784
JournalJournal of Colloid and Interface Science
Volume708
DOIs
Publication statusPublished - 15 Apr 2026

Keywords

  • Memristive devices
  • MXenes
  • Neuromorphic computing
  • Surface engineering
  • Synaptic plasticity

Cite this