Published in The Journal of The Textile Institute (Vol. 114, No. 6, 2023)

Published in The Journal of The Textile Institute (Vol. 114, No. 6, 2023)

AbstractThis paper investigated the development and characterization of conductive textured and non-textured polyester fabrics with various cross-sections. To impart conductivity on fabric structures, the electroless copper plating method was chosen. Electrical conductivity, thickness, electron scanning microscopy (SEM), microscopic morphology, and energy dispersive X-ray spectroscopy were used to characterize the deposition of copper nanoparticles on textured and non-textured polyester fabrics (EDX). SEM images revealed a thin film of uniform copper nanoparticle coating on textured and non-textured polyester fabrics. Electrical conductivity, wear resistance, thickness, and durability of conductive textured polyester fabrics were compared to non-textured conductive polyester fabrics. Electrical conductivity measurements show that both textured and non-textured polyester fabrics have good electrical conductivity values of 27Ω/cm, 85Ω/cm, 52Ω/cm, 9Ω/cm, 98Ω/cm, 133Ω/cm. When conductive polyester fabrics were tested for durability against washing and rubbing fastness, the textured polyester fabrics retained copper nanoparticles well by maintaining their electrical conductivity level after 250 abrasion and washing cycles, with best electrical conductivity values of 177Ω/cm and 29Ω/cm, respectively.

AbstractGraphene-coated textiles have attracted great interest in recent years for the development of advanced piezo-resistive materials. In this work, rGO (reduced graphene oxide) treated nylon nonwoven is prepared by the dip coating method. The influence of treatment parameters viz. graphene oxide (GO) concentration, sodium dithionite (Na2S2O4) concentration, and, pH of GO dispersion on rGO add-on and electrical conductivity is analysed and optimized by using Box–Behnken response surface design to prepare electroconductive nylon nonwoven for pressure sensing application. An rGO add-on of 9.12% and surface resistivity of 3.62 kΩ sq−1 have been achieved after two dippings at the optimized dipping condition viz. 0.6% GO concentration, 2 pH, and 15 mM Na2S2O4 concentration. Surface resistivity of 3.62 kΩ sq −1 is achieved after two dippings at the optimized dipping condition with the rGO add-on of 9.12%. The piezoresistive properties of this fabric are investigated. Resistance and thickness of rGO-coated nylon nonwoven treated fabric show a strong negative correlation with the applied pressure. A maximum gauge factor of 2.56 is observed which is in line with pure compression-based pressure sensors. A high-pressure sensitivity of − 0.34 kPa −1 is observed at 2 kPa pressure with a recovery time of 1.2 s implying that it can be used for pressure sensing. After rGO dipping, bending rigidity, bending modulus, and breaking strength of nylon nonwoven are increased whereas the breaking elongation decreased. Bending rigidity, bending modulus, and breaking strength of nylon nonwoven increase after rGO dipping, while breaking elongation decreases. Surface resistivity and rGO add-on of treated nylon nonwoven remain unchanged even after multiple washing, rubbing, and atmospheric ageing. Nylon nonwovens are characterised by FT-IR, XRD, Raman, FESEM, and LEICA image analyser analysis are used to characterise the developed nonwovens.

Published in The Journal of The Textile Institute (Vol. 114, No. 6, 2023)

AbstractCreation of a knit fabric-based, stable sensor using dipping and encapsulation processes is feasible, but where deposition occurs in the structure is not yet well understood. Using two graphene treatments and three encapsulants applied to single jersey (100% wool, 100% cotton), relative merits of a range of surface characterization tools (e.g., photographic - optical microscopy, scanning electron microscopy, image analysis (pixel intensity, fiber dimensions), change in lightness (L*, ΔL*, ΔE),) and chemical tools (e.g. vibrational spectroscopy - infrared and Raman) were applied to determine evidence of the treatments. Fabrics, yarns extracted from the treated fabrics and fibers from yarns were each examined. Structure of the fabric, yarns in the fabric, and fibers in the yarns remained clearly identifiable using microscopic methods, suggesting these physical features were largely unchanged. With respect to probable chemical composition, Raman analysis was found preferable to FTIR. HIGHLIGHTSConferring electrical conductivity directly on knit fabric by simple dipping and stabilizing is feasible.Deposition sites of graphene-related treatments and encapsulants have been identified, with some observed differences dependent on structure, whether fabric, yarn, or fiber.Photographic images (optical, SEM), and FTIR and Raman spectra provide evidence, with Raman superior to FTIR.

AbstractThe aim of using protective clothing against penetration of sharp objects such as spikes is to absorb and dissipate the kinematic energy of the object to prevent body injury. In this study, the role of friction coefficient and tensile behaviour of the constituent yarns of three-layer woven fabrics prepared with various weft-wadded yarns including wool, jute, nylon and polypropylene on its resistance against penetration of three spikes with various geometrical characteristics were examined, in both states of cloth fit including tight and loose conditions. The obtained outcomes reveal that increment of the tensile modulus of the weft-wadded yarns leads to the reduction of the penetration depth. Moreover, utilizing yarns with higher friction coefficient such as wool decrease the penetration depth due to the yarn’s resistance against movement during penetration. For all prepared samples, the geometry of the spikes especially tip angle is a parameter that can affect the penetration process and fabric damage. In addition, the results of penetration test of samples demonstrate that the penetration energy of tight state is higher than loose state and in turn the severity of fabric damage in the tight samples is more than loose samples.

AbstractThe utilization of plasma treatment as a technique for fading the colours on textiles is an environmentally friendly approach. However, the challenge lies in the continuous adjustment of its parameters to attain desired fading colour effects. This is due to the unclear mechanism causing changes in fading effect as a result of alterations in plasma treatment parameters. This research endeavours to predict the alteration of fading effect after plasma treatment of cotton fabrics under varying parameters through the development of a prediction system based on a Bayesian Regulated Neural Network (BRNN) with 10-fold cross-validation. Through a modular approach, the inputs for the training of BRNN models include initial values of plasma treatment parameters such as colour depth, air (oxygen) concentration, water content, treatment time, and one of the CIE L*a*b* values and K/S values corresponding to cotton materials. The outputs, trained individually by four independent BRNN models, are the final values of four colour variables: CIE L*, CIE a*, CIE b* and K/S. The models were trained and validated through the Bayesian Regularization Algorithm and 10-fold cross-validation on 192 data sets, yielding fitted coefficients of determination R2 of 0.9956, 0.9976, 0.9980 and 0.9687, respectively. Approximately 87.5% − 91.67% of predicted colours were within the range of imperceptible or acceptable differences from actual colours. Hence, the developed artificial intelligence system can aid textile finishers in adjusting the plasma colour fading machine’s parameter settings and selected recipes, thus enhancing efficiency and reducing cost and trial time.

AbstractDue to the intimate relationship with the yarn’s properties, rubber rollers are an integral component of the drafting process. An innovative method for generating colored Siro yarns with regularly variable linear density is discussed in this article. To manufacture Siro yarns, the procedure includes altering the rubber rollers on a ring-spinning machine. By modifying the sorts of back-top rubber rollers and including roving slivers of different hues, this form of Siro yarns may be manufactured. These alterations to the shape of the rubber rollers produce exquisite yarn. Due to the employment of rubber rollers with dynamically varying diameters, a jaw with an imperfect engagement is formed. This leads to an incomplete draft, which forms a coarse knot with a continuous variance in yarn density along the yarn length direction. The Siro fancy yarns are spun using two distinct colors of roving slivers and drafting multiples. The objective of this study was to examine the impact of two rubber rollers of different diameters on the coarse knot cycles, the rear zone drafting multiplier, and the linear density of slub Siro colored yarns.

AbstractIn order to solve the problem of single-color change when thermochromic materials are used at present, this study designs continuous thermochromic camouflage fabric based on the subtractive color matching of dyes, which provides the basis for realizing the integrated transformation of the three camouflage color systems of jungle color, desert color and ocean color. In this study, the electrothermal fabric with silver-coated fiber as the electrothermal element is combined to achieve the purpose of controllable heat source in the process of use, and the electrothermal color change double-layer fabric that can continuously change color through electrothermal control is prepared. Under the voltage of 4 ∼ 6 V, the color change layer fabric can reach the temperature of 34 ∼ 44 °C, and the first color change occurs when the voltage is greater than 4 V but less than 5 V, and the second color change occurs when the voltage is greater than 5 V, realizing continuous thermal color change. It has certain fabric flexibility and wearability, and can supply heat source by itself to meet the demand of product color change at any time. This has reference significance for the development of military camouflage textile products, and provides convenience for the reduction of military reserves in combat.

AbstractThis paper introduces a developed CAD-CAM program that is named as ‘DNC.GARM.DOBBY WEAVING DESIGN’. It was developed using JAVA SE. The software DNC.GARM.DOBBY WEAVING DESIGN is available for individual studies in designing dobby patterns and it generates machine readable files for semi-automated sampling looms manufactured by Gulas Weaving Machines Company (Istanbul/Türkiye). Pattern design and development tools are used in program menu enabling to design the weave with warp/weft colors also. The weave pattern, drafting and lifting plan can be generated manually or automatically. This article represents technical information, features and usage of the developed software DNC.GARM.DOBBY WEAVING DESIGN for designing and production planning of woven patterns and generating the machine readable files for sampling looms.

AbstractIn this study, the ecologically pre-treated wool and cotton fabrics were digitally printed with eco-friendly plant-based inks of blue, red, yellow, and black colours obtained from the plant extracts of bio indigo leaves, quebracho red bark and the flame of the forest flowers and evaluated for coffee-ring effect. The plant-based inks constituted from the Quebracho red bark and the flame of the forest flowers extract both demonstrated reduced dispersion diameter (i.e. diffused) for the Plasma Surface Treated (PST) wool and cotton fabric than the untreated. These findings could be applied for digital printing on photonics to combat the Coffee-ring effect. The possible explanation for the reduced coffee ring effect is the phenomenon known as Marangoni flow. The predominant phytochemicals functioning as chromophores namely indigotin, tannin and butein enclosed in inks constituted from the plant extracts of bio indigo leaves, quebracho red bark and the flame of the forest flowers respectively are highlighted. Additionally, the synthesis of plant-based ink, the colour scheme adapted for determining the inks colours, and the technique of print process as implemented in the study are explained. Likewise, the Colour patch, Percentage (%) Reflectance, and, Colour difference graphs as acquired from Datacolor tools are demonstrated. To end, the wash fastness, rub fastness, and light fastness test results obtained on wool and cotton fabrics digitally printed with innovative plant-based inks are detailed. The Life Cycle Analysis and assessment of functional phytochemicals on the resultant printed fabrics are suggested for future work.

AbstractThe tensile behaviour dependency on the process parameters of blended nonwoven fabric is highly non-linear. This paper explores the potentiality of fuzzy logic inference in such a nonlinear environment as predictive process modelling with minimal experimental data. A model based on fuzzy inference engine has been attempted using recycled polyester fibre blend percentage, needling density and depth of needle penetration as the input variables and tensile strength in both machine direction (MD) and cross direction (CD) as the output variables. Proposed model was built in MAT LAB/Simulink and has been verified by experimental sample data set. Mean relative error percentage and correlation coefficient between the fuzzy model predicted and actual experimental sample values were found to be around ±2.5% and up to 0.97 respectively. These results validate that the model may be applied with high accuracies for the prediction of tensile behaviour of the blended nonwoven fabric in the textile industries.

Published in The Journal of The Textile Institute (Vol. 114, No. 6, 2023)

AbstractA key aspect of textile production is the use of automatic defect detection methods for quality control, which is a critical part of the current fabric defect detection system. Benefiting from the explosive development of convolutional neural networks, it has been proved to be feasible to apply it to salient object detection model in fabric defect detection, but how to learn more robust features and better feature fusion for saliency are still complicated tasks. This article presents a novel saliency detection method for fabric defect detection based on both top-down and bottom-up saliency inference combined with two-way information. The top-down process is to infer high-level saliency by gradually using higher-level and richer semantic features on the basis of the backbone. Then a self-fusion enhanced representation module is proposed, which simulates the parallel processing mechanism with residual connection in a top-down manner to generate robust features and effectively characterize the texture features of complex fabrics. In addition, in the down-top process, the interactive feature fusion module is designed, which is used for coarse-to-fine saliency estimation, where the high-level saliency is gradually integrated with finer lower-level features to obtain a fine-grained result. Finally, fabric defect detection is localized by segmenting the generated saliency map. Extensive experimental results on two kinds of fabric image datasets have demonstrated that the proposed TBINet can localize the defect region with high accuracy, and perform favorably against seven state-of-the-art methods on eight widely tested metrics.

AbstractHigh-efficiency warmth retention materials are urgently needed by human living in extremely cold environment to maintain their health and physical function. However, most existing fibrous warmth retention materials suffer from large weight, inefficient warm retention performance, and poor antibacterial ability. Here, we propose an innovative strategy to construct ultrafine fibrous sponges with interlaced crimped-fiber architecture by direct electrospinning technology. The stable three-dimensional (3D) network structures assemble from tangly curled ultrafine fibers, endowing the obtained sponges with ultralight characteristics (4.5 mg cm−3), robust elastic resilience (nearly full recovery from 100-cycle compressive deformations), and low thermal conductivity (24.1 mW m−1 K−1). Moreover, the in situ doping of chlorhexidine and fluoropolymer enables the sponges with efficient and durable antibacterial performance after washing processes (antibacterial rate up to 95.6% after 10 recycle washing experiments). This work opens a new way for the design of 3D fibrous materials with remarkable antibacterial performance for various fields.

AbstractA model of stitch tightening process in lockstitch machine is formulated to predict the needle thread peak tension during stitch tightening and the position of needle and bobbin thread crossover point i.e. y-value. This y-value is then used to predict the thread consumption in a lockstitch seam. A computer program in C language is developed based on the model formulated in lockstitch machinery to predict needle thread stitch tightening tension and the position of needle and bobbin thread crossover point i.e. y-value. The developed model is validated qualitatively by studying the effect of different variables on peak value of needle thread tension. The model is also validated quantitatively by comparing the experimental and predicted values of peak tension and stitch length. A satisfactory predictive power of the model is observed which validates the developed model.

AbstractPolypyrrole (PPy) is most widely used conductive polymer for conductive textile applications owing to its excellent electrical conductivity, good electromagnetic shielding, good anti-bacterial properties, etc. However, it has not been explored much as thermoelectric material. PPy can be coated on cotton fabric in aqueous media from its monomer to prepare electro-conductive fabrics, which will be a flexible organic thermoelectric material. In this study, PPy-coated cotton fabrics are prepared by in situ chemical polymerisations of pyrrole in presence of ferric chloride. The polymerisation process is optimised by employing Box–Behnken response surface design and method of steepest ascent to achieve minimum electrical resistivity. The lowest resistivity of PPy-coated cotton fabric is achieved to be 1.23 Ω/cm after the optimisation. The PPy-coated cotton fabric with copper thermocouple shows Seebeck coefficient as high as 11.95 µV/K, power factor 0.139 µW m−1 K−2, and thermoelectric figure of merit 1.22 × 10−4 at an absolute temperature of 300K. The thermoelectric performance of this coated fabric is found to be comparable to other costly conductive polymers and inorganic semiconductors. The surface morphology of PPy-coated cotton fabric is observed by field-emission scanning electron microscopy. Chemical interaction between cotton and PPy is investigated by FTIR spectroscopy. Analysis of semi-crystalline structure is done by X-ray diffraction and thermo-gravimetric analyser is used for analysis of thermal degradation.

AbstractHerein, novel cellulose-based biosorbent with dual-particles system was fabricated through a rapid and facile etching-loading method. The results shown the adsorbent exhibited efficient and stable adsorption performance which is owing to the firm embedding of functional particles without interference on the oxidized matrix. And the maximum adsorption capacity for Pb2+ reached 372.4 mg/g. The corresponding adsorption behavior was found to accord well with the pseudo-second-order kinetic model and Langmuir isotherm model, which reveals the rate-limiting phase of adsorption process is attributed to chemical adsorption type, and Pb2+ was adsorbed onto biosorbent surface by monolayer spontaneous chemical reaction. Surface physical attraction and chemical complexation bond are dominant adsorption mechanisms. And the recycling efficiency of biosorbent reached above 83% after 5 cycles. The above results indicate the hybrid functional structure by etching-loading route provided a novel method for rapid construction of promising and recyclable adsorbent for wastewater treatment.

AbstractThis article presents the design, fabrication, and analysis of a textile-based band-stop frequency selective surface (FSS), in GSM, Wi-Fi, LTE, and WiMAX bands where the electromagnetic (EM) pollution is intense. The unit cell of the proposed FSS has been designed and simulated via a full-wave EM solver; CST Microwave Studio at the frequency of interest. In this study, embroidered textile-based FSSs were designed as an alternative to conventional FSSs. In the study, high textile detailed (from thread to embroidery direction) modeling was performed and the results of the modeling and experiments were compared. The results of this study showed that the embroidery direction has a dramatic effect on the electromagnetic behavior of the FSS. Both experimental and high textile detailed modeling results indicated that vertical or horizontal embroideries are not sufficient for band-stopping FSS. To overcome this problem, vertical-over-horizontal embroidery was used. The highly textile detailed model and its experimental results have proven that horizontal-over-vertical embroidery is successful in band-stopping FSS.