A Facile Design of Colourimetric Polyurethane Nanofibrous Sensor Containing Natural Indicator Dye for Detecting Ammonia Vapour

Chemicals and industrial gases endanger both human health and the environment. The inhalation of colourless ammonia gas (NH3) can cause organ damage or even death in humans. Colourimetric materials are becoming more popular in the search for smart textiles for both fashion and specific occupational applications. Colourimetric textile sensors based on indicator dyes could be very useful for detecting strong gaseous conditions and monitoring gas leaks. In this study, black carrot extract (BCE) as a natural indicator dye and polyurethane (PU) polymer were used to develop a colourimetric sensor by electrospinning. The properties of the BCE/PU nanofibrous mats were characterized by the Fourier transform infrared spectrum (FTIR) and a scanning electron microscope (SEM). The BCE caused a change in the morphology of the PU nanofibrous mat. To evaluate the colour shift due to NH3 vapour, the BCE/PU nanofibrous mats were photographed by a camera, and software was used to obtain the quantitative colour data (CIE L*a*b). The BCE/PU nanofibrous exhibited a remarkable colour change from pink–red to green–blue under NH3 vapour conditions with a fast response time (≤30 s). These findings showed that colourimetric nanofibrous textile sensors could be a promising in situ material in protective clothing that changes colour when exposed to harmful gases.

Multifunctional Electrically Conductive Copper Electroplated Fabrics Sensitizes by In-Situ Deposition of Copper and Silver Nanoparticles

In this study, we developed multifunctional and durable textile sensors. The fabrics were coated with metal in two steps. At first, pretreatment of fabric was performed, and then copper and silver particles were coated by the chemical reduction method. Hence, the absorbance/adherence of metal was confirmed by the deposition of particles on microfibers. The particles filled the micro spaces between the fibers and made the continuous network to facilitate the electrical conduction. Secondly, further electroplating of the metal was performed to make the compact layer on the particle- coated fabric. The fabrics were analyzed against electrical resistivity and electromagnetic shielding over the frequency range of 200 MHz to 1500 MHz. The presence of metal coating was confirmed from the surface microstructure of coated fabric samples examined by scanning electron microscopy, EDS, and XRD tests. For optimized plating parameters, the minimum surface resistivity of 67 Ω, EMI shielding of 66 dB and Ohmic heating of 118 °C at 10 V was observed. It was found that EMI SH was increased with an increase in the deposition rate of the metal. Furthermore, towards the end, the durability of conductive textiles was observed against severe washing. It was observed that even after severe washing there was an insignificant increase in electrical resistivity and good retention of the metal coating, as was also proven with SEM images.

Cardiopulmonary coupling analysis using smart wearables and mobile computing

Cardiopulmonary coupling (CPC) analysis links heart and respiration rates to assess sleep-related parameters. Typically, the CPC is measured using multi-lead electrocardiography (ECG) and ECG-derived respiration (EDR). Novel textile shirts with embedded ECG sensors offer convenient and continuously monitored sleep at home. We investigate the feasibility of a shirt with textile sensors (Pro- Kit, Hexoskin, Quebec, Canada) for CPC analysis by mobile computing. ECG data is continuously transmitted from the shirt to a smartphone via Bluetooth Low Energy (BLE). We customize a CPC algorithm and use twelve whole-night recordings from four volunteers to perform qualitative and quantitative analysis. We compare EDR with respiratory inductive plethysmography (RIP). In average, EDR and RIP differ 17.22%. After one night, the batteries are reduced to approx. 70% (shirt) and 90% (smartphone). The run time for CPC processing is approx. 3 min. Hence, smart wearables in combination with mobile computing show technical feasibility for CPC analysis. Eventually, this could yield a useful solution for sleep analysis of non-expert users in a private environment.