journal article·2022-01

Producing garment based multichromic smart sensors through dyeing cotton fabrics with chromic dyes

Rıza Atav, Uğur Ergünay, Eray Akkuş

Cellulose· Volume 29· Issue 1
Publisher page

Abstract

The importance of technical, functional and smart textiles with high added value is gradually increasing. One class of smart textiles is chromic materials. Chromism is a reversible change in color caused by certain factors (temperature, pH, etc.). In this study, it is aimed to develop a process that will ensure the dyeability of cotton fabrics with halo-, thermo- and photochromic dyes by using padding method. After determining the optimum conditions for each chromic dyes, their binary and triple combinations were also performed. A cabinet design in which the color change of chromic fabrics can be observed with the effect of pH (acid and base vapors), temperature and/or light is also realized. In this cabinet, the color change of the fabrics is detected by camera depending on pH, temperature and time. It is determined that the chromic fabrics produced in this study can react well to changes in pH, temperature and light, and maintain color changing ability as long as 20 cycles. Using the triple combination of thermochromic, halochromic and photochromic dyes, a method for producing cotton smart sensor clothes (or a wristband etc.) that can provide one or both of the following functions; informing whether the ambient temperature is uncomfortable or risky for the worker (thermochromic), notifying if there is a dangerous chemical vapor (acid–base vapors) in the work environment (halochromic), and creating a visual effect (photochromic) by changing color with UV rays on one hand, while providing protection against UV rays on the other hand, was developed.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Photochromic and Fluorescence Chemistry · Polydiacetylene-based materials and applications · Olfactory and Sensory Function Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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