A Study of Self-Healing Polymer Nanocomposites with Filler Effect

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.

Volume: 12 | Issue: 1 | Year 2026 |
International Journal of Applied Nanotechnology
Received Date: 03/06/2026
Acceptance Date: 03/09/2026
Published On: 2026-03-15
First Page: 26
Last Page: 35

Journal Menu


By: Kazi Kutubuddin Sayyad Liyakat.

Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane
Institute of Technology, Solapur, Maharashtra, India.

Abstract

Considering their versatility, polymers sometimes lack the barrier properties, mechanical strength, and thermal stability required for high-performance applications. The creation of polymer nanocomposites is an advanced solution to this restriction. To make these materials, nanoscale fillers like carbon nanotubes, graphene, silica, clay, and metal oxide nanoparticles are added to a polymer matrix in small amounts (typically 1–5% weight percentage). The nanoparticles’ incredibly high surface area-to-volume ratio, as well as their unique interactions with polymer chains, make nanocomposites so remarkable. This frequently results in a synergistic increase in qualities that outperforms what can be achieved with simple additive effects or macroscopic fillers. It is feasible to significantly improve properties including stiffness, tensile strength, electrical, and thermal conductivity, and gas barrier performance. The “filler effect” refers to the significant impact that precisely chosen and engineered nanoparticles have on the healing process, despite the fact that self-healing mechanisms in polymers can be extrinsic (e.g., embedded microcapsules, vascular networks) or intrinsic. This influence facilitates, accelerates, or improves self- repair capacities rather than simply increasing strength. The never-ending pursuit of materials with higher durability, lower maintenance requirements, and improved safety has fueled material science innovation. Self-healing materials – substances that, like biological systems, can mend themselves – are among the most promising advances. Polymer nanocomposites are unique in this intriguing subject, especially when their self-healing ability is intentionally enhanced by the “filler effect.” Conventional materials decay over time due to environmental exposure, wear, and fatigue. Cracks, microfractures, and other types of damage accumulate over time, leading to failure. This raises safety concerns in critical applications, such as aerospace, automotive, electronics, and biomedical implants, necessitating costly repairs or early replacement. Self-healing materials offer groundbreaking technology that improves system sustainability and dependability while reducing waste and increasing product lifespan.

Keywords: Self-healing, polymer, nanocomposites, composite material, filler effect, intrinsic healing extrinsic healing.

Loading

Citation:

How to cite this article: Kazi Kutubuddin Sayyad Liyakat A Study of Self-Healing Polymer Nanocomposites with Filler Effect. International Journal of Applied Nanotechnology. 2026; 12(1): 26-35p.

How to cite this URL: Kazi Kutubuddin Sayyad Liyakat, A Study of Self-Healing Polymer Nanocomposites with Filler Effect. International Journal of Applied Nanotechnology. 2026; 12(1): 26-35p. Available from:https://journalspub.com/publication/uncategorized/article=24828

Refrences:

1. Gaikwad A, Chendke A, Mulani N, Sarika M. Submersible pump theft indicator. IEJRD Int Multidiscip J. 2020;5(4):5. Available from: https://www.iejrd.com/index.php/%20/article/view/627.

2. Raut A, Mali M, Mashale T, Kazi KS. Bagasse level monitoring system. Int J Trend Sci Res Dev. 2018;2(3):1657–9. Available from: https://www.ijtsrd.com/papers/ijtsrd11469.pdf.

3. Mulani AO, Patil RM. Discriminative appearance model for robust online multiple target tracking. Telematique. 2023;22(1):24–43.

4. Kumar MS, Ganesh D, Turukmane AV, Batta U. Deep convolution neural network based solution for detecting plant diseases. J Pharm Negat Results. 2022;13(Special Issue 1):464–71.

5. Halli UM. Nanotechnology in IoT security. J Nanosci Nanoeng Appl. 2022;12(3):11–16.

6. Wale AD, Rokade D, et al. Smart agriculture system using IoT. Int J Innov Res Technol. 2019;5(10):493–7.

7. Kazi KS. Significance and usage of face recognition system. Sch J Humanit Sci Engl Lang. 2017;4(20):4764–72.

8. Dixit AJ, et al. Iris recognition by Daugman’s method. Int J Latest Technol Eng Manag Appl Sci. 2015;4(6):90–3.

9. Kazi KSL. Significance of projection and rotation of image in color matching for high-quality panoramic images used for aquatic study. Int J Aquat Sci. 2018;9(2):130–45.

10. Halli UM. Nanotechnology in E-vehicle batteries. Int J Nanomater Nanostruct. 2022;8(2):22–27.

11. Hotkar PR, Kulkarni V, et al. Implementation of low power and area efficient carry select adder. Int J Res Eng Sci Manag. 2019;2(4):183–4.