Taguchi optimised next-generation electric field assisted patterned poly-(vinylidene difluoride)-based hybrid nanogenerators and OmniGait-sensor

Abstract

The present study provides a Taguchi design of experiments (DoE) employed to optimise the near-field electric patterning (NFEP) process, enabling the fabrication of PVDF-based micropatterns for hybrid nanogenerator and OmniGait-sensor applications. Through this study, NFEP establishes an improved method that addresses the shortcomings of conventional electrospinning and advanced electric-field-assisted fabrication techniques. NFEP-driven surface engineering enhances the β-phase fraction in patterned PVDF, enabling the fabrication of well-defined structures with versatile designs. By optimising NFEP through Taguchi DoE and variance analysis, we achieved a robust process that drastically cuts time, energy, and resource consumption. Using NFEP, a diamond-mesh PVDF/barium titanate nanocomposite pattern was created. These patterns were employed as a piezoelectric and triboelectric layer, with PDMS as the counter tribo layer, to form a flexible hybrid piezo–tribo nanogenerator and sensor. By optimising the processing parameters using Taguchi DoE, the present work establishes a direct correlation between process optimisation and the fabrication of patterned polymer nanocomposites for nanogenerator and OmniGait sensor applications. This advancement improves versatility and opens new application horizons for nanogenerator and sensor technology.

Graphical abstract: Taguchi optimised next-generation electric field assisted patterned poly-(vinylidene difluoride)-based hybrid nanogenerators and OmniGait-sensor

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Article information

Article type
Paper
Submitted
29 Jan 2026
Accepted
09 Apr 2026
First published
17 Apr 2026
This article is Open Access
Creative Commons BY license

RSC Appl. Interfaces, 2026, Advance Article

Taguchi optimised next-generation electric field assisted patterned poly-(vinylidene difluoride)-based hybrid nanogenerators and OmniGait-sensor

V. N. Sasi, M. Garg, P. Sravandas, S. Pratihar, A. M. Chandran, A. R. Bhattacharyya and P. K. S. Mural, RSC Appl. Interfaces, 2026, Advance Article , DOI: 10.1039/D6LF00026F

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