Title: Integration of smart, ultrathin and flexible cellulose-based printed sensors for process control and health monitoring

Abstract

Integration of facile, thin, and flexible sensors is an emerging trend in production control and structural engineering. Among various sensors, highly porous cellulose-based printed sensors offer several advantages including biodegradability, versatility, flexibility, high sensitivity and chemically inert with minimal or no effect on the mechanical integrity of the structures. Printed cellulose sensors are suitable for determining the cross-linking reactions, moisture content, temperature and health monitoring of polymers, wood, composites and adhesives. However, it is vital to optimizing the paper type and printing parameters depending on the type of material and sensor functionality. This work demonstrates the potential application of cellulose-based printed sensors for their integration in polymers, composites and adhesives for various purposes. Sensors integrated into polymer matrix such as epoxy, phenol-formaldehyde, urea-formaldehyde etc., allows real-time in-situ analysis of the cross-linking of the resin. The ability of cellulose-based sensors of tracking the real-time changes in the physical state or cross-linking of resin is an essential prerequisite for the production control of polymer composites, especially fiber-reinforced thermosets. Since, physical properties of the material hugely depend on the degree of cross-linking. On the other hand, integrated sensors are effectively used to monitor the humidity and temperature of the thermoplastic and thermoset polymer composite. For instance, the printed sensor on abaca paper is ideal for measuring the degree of cure of wood adhesives at various temperatures. Once the curing is completed, the integrated sensor was used to monitor the moisture content in wood. Cellulose-based sensors offer significant potential and are suitable for integration into structural components taking into consideration that it not only allows tracking of real-time data, but also ideal for minimal invasive integration.

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