Neuromorphic Self-Learning Polymer–MXene Photovoltaic Composites with Embedded Memristive Energy Routing for Adaptive Solar Energy Harvesting

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 14 | 04 | Page :
By

Ajoy Kumar Nandy,

Rajkumar Jhapte,

Vishal Moyal,

V. Vinodhini,

Mahendra Kumar R,

D. Suresh,

Yogesh Diliprao Sonawane,

  1. Assistant Professor, Department of Mechanical Engineering, Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India
  2. Associate Professor, Department of Electrical Engineering, STME, SVKM NMIMS Global University, Dhule, Maharashtra, India
  3. Professor, Department of Electrical Engineering, STME, SVKM NMIMS Global University, Dhule, Maharashtra, India
  4. Associate Professor, Department of Electronics and Communication Engineering (ECE), Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Thiruvallur, Tamil Nadu, India
  6. Assistant Professor, Department of Mechanical Engineering, Indra Ganesan College of Engineering, Trichy, Tamil Nadu, India
  7. Assistant Professor, Department of Mechanical Engineering, School of Technology Management and Engineering, SVKM’s NMIMS Global University, (formerly- Shri Vile Parle Kelavani Mandal’s, Institute of Technology,) Dhule, Maharashtra, India

Abstract

This dynamic and fast-growing intelligent renewable energy system requires photovoltaic materials that can autonomously adapt to fast-changing environmental conditions. In this study, a novel system is proposed for adaptive harvesting of solar energy based on Neuromorphic Self-Learning Polymer–MXene Photovoltaic Composites (NSPMPCs) with embedded memristive energy routing networks. To boost the charge generation and charge transport in the polymer–MXene heterostructure, the flexibility and processability of conductive polymers are integrated with the extraordinary electrical conductivity, surface chemistry, and photothermal properties of MXene nanomaterials. The embedded memristive architecture learns continuously the variations of the irradiance, shading patterns and load variations, dynamically reconfiguring the energy pathways inside the device to optimise the power extraction and minimise the conversion losses. The proposed routing mechanism is a neuromorphic one that allows for self-optimisation without external supervising control, thus increasing the energy-conversion efficiency, response speed and operational reliability of the system, even in the presence of non-uniform illumination. Adaptive power tracking, energy retention and long-term stability are significantly improved using simulation studies when compared with conventional PV composites. The proposed framework paves the way for the development of next generation of PV materials which will be able to learn, heal and dynamically manage energy to form sustainable energy infrastructure.

Keywords: Neuromorphic photovoltaics; Adaptive solar energy harvesting; Polymer–MXene composites; Memristive energy routing; Self-learning energy systems.

How to cite this article: Ajoy Kumar Nandy, Rajkumar Jhapte, Vishal Moyal, V. Vinodhini, Mahendra Kumar R, D. Suresh, Yogesh Diliprao Sonawane. Neuromorphic Self-Learning Polymer–MXene Photovoltaic Composites with Embedded Memristive Energy Routing for Adaptive Solar Energy Harvesting. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL: Ajoy Kumar Nandy, Rajkumar Jhapte, Vishal Moyal, V. Vinodhini, Mahendra Kumar R, D. Suresh, Yogesh Diliprao Sonawane. Neuromorphic Self-Learning Polymer–MXene Photovoltaic Composites with Embedded Memristive Energy Routing for Adaptive Solar Energy Harvesting. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=251394

References

  1. Fu Y, Xu J. Applications of MXenes in neuromorphic computing and memristors: From material synthesis and physical mechanisms to integrated sensing, memory, and computation. J Low Power Electron Appl. 2026;16(1):8. doi:10.3390/jlpea16010008.
  2. Fotius JAA, Oliveira HP. MXene-based composites for energy harvesting and energy storage devices. Solids. 2025;6(3):41. doi:10.3390/solids6030041.
  3. Xu H, Zeng X, Qadir A. Advancing flexible optoelectronic synapses and neurons with MXene-integrated polymeric platforms. Nanomaterials. 2025;15(19):1481. doi:10.3390/nano15191481.
  4. Alhamada TF, Azmah Hanim MA, Jung DW, Saidur R, Nuraini A, Hasan WZW. MXene based nanocomposites for recent solar energy technologies. Nanomaterials. 2022;12(20):3666. doi:10.3390/nano12203666.
  5. Ho A, Besiroglu T, Erdil E, Owen D, Rahman R, Guo ZC, et al. Algorithmic progress in language models. arXiv [Preprint]. 2024. arXiv:2403.05812.
  6. Lu L, Sun B, Wang Z, Meng J, Wang T. Two-dimensional MXene-based advanced sensors for neuromorphic computing intelligent application. Nano-Micro Lett. 2026;18:64.
  7. Ye J, Wang W, Shi C, Cui X, Qu W, Lin P, et al. An overhead-reduced, efficient, fully analog neural-network computing hardware. Sci Adv. 2025;11:eadv7555.
  8. Li Y, Ding G, Zhai Y, Lv Z, Yan Y, Xue S, et al. MXene-based flexible memory and neuromorphic devices. Small. 2025;2410914.
  9. Antolini A, Zavalloni F, Lico A, Quqa S, Greco L, Mangia M, et al. The role of phase-change memory in edge computing and analog in-memory computing: An overview of recent research contributions and future challenges. Sensors. 2025;25:3618.
  10. Su X, Guo Z, Zhang G, Tsai H, Li N. Progress and challenges of phase change memory for in-memory computing. Curr Opin Solid State Mater Sci. 2025;37:101225.
  11. Rehman MM, Samad YA, Gul JZ, Saqib M, Khan M, Shaukat RA, et al. 2D materials-memristive devices nexus: From status quo to impending applications. Prog Mater Sci. 2025;152:101471.
  12. Lu Z, Fan Y, Sun Z, He X, Yang C, Yin H, et al. A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN. J Adv Ceram. 2025;14:9221050.
  13. Teixeira H, Dias C, Silva AV, Ventura J. Advances on MXene-based memristors for neuromorphic computing: A review on synthesis, mechanisms, and future directions. ACS Nano. 2024;18:21685-21713.
  14. Alhabeb M, Maleski K, Anasori B, Lelyukh P, Clark L, Sin S, et al. Guidelines for synthesis and processing of two-dimensional titanium carbide (Ti3C2Tx MXene). Chem Mater. 2017;29:7633-7644.
  15. Gosai J, Patel M, Liu L, Lokhandwala A, Thakkar P, Chee MY, et al. Control-etched Ti3C2Tx MXene nanosheets for a low-voltage-operating flexible memristor for efficient neuromorphic computation. ACS Appl Mater Interfaces. 2024;16:17821-17831.
  16. Kundale SS, Abubakr M, Park J, Morales FO, Devara IKG, Lestari WA, et al. Chemically configurable analogue memristors, via the chemiresistive response of oxidized MXene. Mater Horiz. 2025;12:5677-5688.
  17. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Eng. 2021;10(1):592-599.
  18. Refaai MRA, Dhanesh L, Ganthia BP, Mohanty M, Subbiah R, Anbese EM. Design and implementation of a floating PV model to analyse the power generation. Int J Photoenergy. 2022;2022:3891881.
  19. Gu J, Wang W, Yin R, Truong CV, Ganthia BP. Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Eng. 2021;10(1):555-562.
  20. Ganthia BP, Hanumanthakari S, Gudimindla H, Anandaram H, Ramkumar MS, Mohanty M, et al. Machine learning strategy to achieve maximum energy harvesting and monitoring method for solar photovoltaic panel applications. Int J Photoenergy. 2022;2022:4493116.
  21. Zheng W, Mehbodniya A, Neware R, Wawale SG, Ganthia BP, Shabaz M. Modular unmanned aerial vehicle platform design: Multi-objective evolutionary system method. Comput Electr Eng. 2022;99:107838.
  22. Chakole N, Remamany KP, Mohan G, Sasirekha P, Kumar NMG, Kumar CR, et al. Optimal energy management for hybrid PV-wind-battery microgrids through Markov decision processes technique. Int J Smart Grid. 2025;9(3):127-145.
  23. Fu Y, Kumar J, Ganthia BP, Neware R. Nonlinear dynamic measurement method of software reliability based on data mining. Int J Syst Assur Eng Manag. 2022;13(Suppl 1):273-280.
  24. Thenmalar K, Kiruba K, Raj P, Ganthia BP. A real time implementation of ANN controller to track maximum power point in solar photovoltaic system. Ann Rom Soc Cell Biol. 2021;25(6):10592-10607.
  25. Ganthia BP, Panda S, Remamany KP, Chaturvedi A, Begum AY, Mohan G, et al. Experimental techniques for enhancing PV panel efficiency through temperature reduction using water cooling and colour filters. Electr Eng. 2025;107(9):11349-11375.
Ahead of Print Subscription Original Research
Volume 14
04
Received 25/07/2026
Accepted 29/07/2026
Published 01/08/2026
Publication Time 7 Days

Login

My IP

PlumX Metrics