The g-C3N4-TiO2 nanocomposite for non-volatile memory and artificial synaptic device applications

S. L. Patil, O. Y. Pawar, H. S. Patil, S. S. Sutar, G. U. Kamble, Deok kee Kim, Jin Hyeok Kim, Tae Geun Kim, R. K. Kamat, T. D. Dongale, N. L. Tarwal

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Recently, 2D layered materials like graphitic carbon nitride (g-C3N4) are gaining significant attention due to their excellent structural and electronic properties. Metal oxide and g-C3N4 composite can enhance chemical and electronic properties which can ultimately enhance device performance. Given this, we report the synthesis of g-C3N4–TiO2 nanocomposite by the ex-situ (solid state reaction) method for resistive switching (RS) applications. Initially, the TiO2 nanoparticles (NPs) were optimized by using annealing them at different temperatures and composited with the g-C3N4. The devices fabricated using g-C3N4–TiO2 nanocomposite shows good RS properties at very low SET and RESET voltages (< 0.5 V), similar to the biological voltage scale. The distribution of switching voltages of all devices was studied using the cumulative probability and Weibull distribution techniques. Moreover, switching voltages were modeled and predicted using the statistical time series analysis method. All fabricated devices demonstrated the double-valued charge flux characteristics, suggesting the presence of a non-ideal memristor effect. The optimized g-C3N4-TiO2 (TiO2 NPs annealed at 450 °C) device shows good endurance and retention memory properties. Moreover, the optimized g-C3N4-TiO2 device can mimic the various bio-synaptic properties such as potentiation-depression, excitatory postsynaptic current, and paired-pulse facilitation. The charge transport results reveal that Ohmic and Child's square law dominated the conduction of the device. Based on electrical and charge transport results, a plausible filamentary RS effect is presented. This study suggested that the g-C3N4-TiO2 is beneficial for both memory and neuromorphic computing applications.

Original languageEnglish
Article number171024
JournalJournal of Alloys and Compounds
Publication statusPublished - 2023 Nov 5

Bibliographical note

Funding Information:
Ms. Snehal L. Patil would like to thank Chhatrapati Shahu Maharaj Research, Traning and Human Development Institute (SARTHI), Pune for providing the financial Support. Dr. N. L. Tarwal is thankful to DST-SERB for providing financial assistance through the Teachers Associateship for Research Excellence (TARE) scheme ( TAR/2021/000307 ). The financial assistance through DST-PURSE Phase-II ( 2018–2023 ) and UGC DSA-Phase II ( 2018–2023 ) is highly acknowledged. Dr. Tukaram D. Dongale would like to thank the Science and Engineering Research Board (DST-GoI) for providing the research grant through State University Research Excellence (SERB–SURE) scheme ( SUR/2022/000765 ). Prof. Jin Hyeok Kim is thankful to the priority research Centres Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology ( 2018R1A6A1A03024334 ) as well as the National Research Foundation of Korea (NRF) grant funded by the government of the Republic of Korea (MSIT) (No. 2022R1A2C2007219 ).

Publisher Copyright:
© 2023 Elsevier B.V.


  • Ex-Situ method
  • g-CN-TiO nanocomposite
  • Memristor
  • Resistive switching
  • Synaptic learning
  • Time series analysis

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys
  • Materials Chemistry


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