Machine-Embroidered Textile Electrodes: Parametric Engineering for Lab-on-Glove Electrochemical Pesticide Detection

Abstract

Wearable Lab-on-glove systems offer the possibility of in-situ electrochemical analysis for non-destructive detection by enabling direct human-surface interaction. However, scalable fabrication and mechanistic understanding of textile electrode architectures remain underdeveloped. Here, we establish computerised embroidery as a scalable electrochemical microfabrication strategy and demonstrate its integration into a wearable glove-based pesticide-sensing platform. Using a multi-needle embroidery system, the influence of fabrication parameters on electrochemical behaviour was studied, statistically analysed, and machine-learning-assisted feature analysis was employed to elucidate an electrochemical performance framework based on the embroidery parameters. The optimised embroidered textile-based biosensor was functionalised and validated for the detection of monocrotophos via inhibition-based electrochemical sensing. Electrochemical performance characterisation proved that the fabricated biosensor exhibited high repeatability, reusability and reproducibility. It also showed selective and sensitive detection of monocrotophos over the range of 5-100 µg/L, with a detection limit of 1.55 µg/L. The developed sensor also showed excellent storage stability over 90 days, retaining 99% of its initial response. Thus, the developed lab-on-glove platform enables in-situ detection while integrating manufacturing scalability, structural control, and mechanistic electrochemical insight. This work redefines computerised embroidery as a programmable electrochemical microfabrication strategy and provides design principles for next-generation wearable textile biosensors.

Supplementary files

Article information

Article type
Paper
Accepted
11 Jun 2026
First published
12 Jun 2026

Lab Chip, 2026, Accepted Manuscript

Machine-Embroidered Textile Electrodes: Parametric Engineering for Lab-on-Glove Electrochemical Pesticide Detection

K. S. Deepak, A. Javed, S. Goel and S. K. Dubey, Lab Chip, 2026, Accepted Manuscript , DOI: 10.1039/D6LC00452K

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