Predicting hydrophobicity of silica sol-gel coated dyed cotton fabric by artificial neural network and regression

Artificial neural network (ANN) and multiple linear regression (MLR) have been used to predict the hydrophobicity of silica sol-gel coated dyed cotton fabric using different nanoparticle concentrations, dye concentrations, dye types and cross linker types as predictors. A total of 32 samples have been dyed with reactive and direct dyes using two dye concentrations at HT dyeing machine. To develop nano roughness on dyed fabric, with an aim to create super hydrophobic dyed cotton, different concentrations of silica nanoparticles with a combination of silane hydrophobes (alkyltrialkoxysilanes), and silane cross-linkers, i.e. tetraethoxysilane (TEOS) and teramethoxysilane (TMOS) are applied by sol-gel technique using dip-dry-cure process. The hydrophobicity is measured by AATCC spray rating technique. The coefficient of determination (R2) indicates that there is a strong correlation between the measured and the predicted values with a trivial mean absolute error; ANN is found to be more powerful predicting method than MLR. The most influencing variables revealed through correlation coefficient and P-values of regression model are silica nanoparticle and dye concentration. Empirical and statistical models have been proposed to predict dyed cotton fabric hydrophobicity without any prior trials, which reduces cost and time

Study of Physical Properties of Nano-Silica Coated Cotton Textiles

This research was aimed to investigate the effect of silica sol-gel coating on air permeability, stiffness and tensile properties of dyed cotton fabric. Various concentrations of silica nanoparticles were applied on dyed cotton substrate using two different cross-linkers through sol-gel method. The homogenous sol-gel coating dispersions were prepared by using an ultrasonicator. Coated samples were tested for mechanical and comfort properties such as tensile strength, stiffness, crease recovery and air permeability. It was found that tensile strength and crease recovery of coated substrate were slightly improved. On other hand, it was observed that fabric stiffness and air permeability were affected slightly by increasing concentration of silica nano particle. It was also observed that type of cross-linker has strong influence on coated fabric?s strength and flexural rigidity

Predicting hydrophobicity of silica sol-gel coated dyed cotton fabric by artificial neural network and regression

67-72Artificial neural network (ANN) and multiple linear regression (MLR) have been used to predict the hydrophobicity of silica sol-gel coated dyed cotton fabric using different nanoparticle concentrations, dye concentrations, dye types and cross linker types as predictors. A total of 32 samples have been dyed with reactive and direct dyes using two dye concentrations at HT dyeing machine. To develop nano roughness on dyed fabric, with an aim to create super hydrophobic dyed cotton, different concentrations of silica nanoparticles with a combination of silane hydrophobes (alkyltrialkoxysilanes), and silane cross-linkers, i.e. tetraethoxysilane (TEOS) and teramethoxysilane (TMOS) are applied by sol-gel technique using dip-dry-cure process. The hydrophobicity is measured by AATCC spray rating technique. The coefficient of determination (R2) indicates that there is a strong correlation between the measured and the predicted values with a trivial mean absolute error; ANN is found to be more powerful predicting method than MLR. The most influencing variables revealed through correlation coefficient and P-values of regression model are silica nanoparticle and dye concentration. Empirical and statistical models have been proposed to predict dyed cotton fabric hydrophobicity without any prior trials, which reduces cost and time. </span

Hydrophobic lipophilic modified cotton fabric for oil absorption applications

In this paper, the method of “hydrophobic modified coating” was used by introducing the low surface energy hydrophobic groups and silica nanoparticles on the surface of towel fabric. A hydrophobic lipophilic cotton fabric with biodegradability and super absorbent capacity was prepared by modifying the surface by silica nanoparticles. The surface structure was characterized by Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM). The performance indexes such as oil absorption ratio and oil retention rate were tested and evaluated. The results revealed that the hydrophobicity and oil absorption properties of the cotton fabrics were significantly improved after hydrophobic modification. The water contact angle is 140° and the oil contact angle is 0°, showing strong hydrophobicity of the modified fabric. In addition, the modified cotton fabric had an oil retention rate of 84.6%, which can be avoided as secondary pollution during the recycling of saturated pure cotton fabric. Moreover, the cotton fabric with residual oil can be used for power generation fuel, high fever, high enthalpy, and flammable ashless products

Optimizing Synergistic Silica–Zinc Oxide Coating for Enhanced Flammability Resistance in Cotton Protective Clothing

This study reports process optimization studies of silica and zinc oxide-based flame-retardant (FR) coatings on cotton fabric for protective clothing and enhanced flammability properties. The experiments were designed by central composite design (CCD) using response surface methodology (RSM) to assess the synergistic protective effects of silica and zinc oxide FR coating. These prepared sols were coated on cotton fabrics by a simple dip dry cure process. The resulting FR-finished fabrics were characterized by SEM, mechanical properties, flame retardancy, and air permeability. SEM results confirmed the homogenous spreading of particles on cotton fabrics. From TGA results, it was noticed that the incorporation of silica and ZnO in the prepared nano-sols results in improved thermal stability of the FR-finished fabrics. These sol–gel-treated FR cotton fabrics showed excellent comfort properties, which shows their suitability for fire-retardant protective clothing. RSM analysis proved that the predicted values are in good agreement with the experimental values since R2 values for time to ignite, flame spread time, and air permeability were greater than 0.90. The optimized concentration of silica and ZnO in FR-finished fabrics was found to be 0.302% and 0.353%, respectively, which was further confirmed by confirmatory experiments. The optimization analysis successfully optimized the process for synergistic coating of silica and zinc oxide nanoparticles for enhanced flammability properties of FR cotton fabric for protective clothing

Bioinspired microstructure-reorganized behavior of carbon nanotube yarn induced by cyclic stretching training

Microstructure-reorganized behavior is where the microstructure of a material can be reorganized under some conditions, such as temperature or moisture changes, electrical or mechanical stimulation. Human muscle, comprising an exceptional hierarchical structure, is a representative example whose flexibility and strength can be enhanced remarkably after cyclic stretching training owing to the mechanically reorganized structural arrangement and redistribution (alignment and elongation). The hierarchical structure (bundles and threads) of the yarn, which is similar to that (thick and thin filaments) of human muscle, can also be microstructure-reorganized. Herein, bioinspired by the structure-reorganized behavior of muscle, for the first time, a novel strain engineering strategy (cyclic stretching or cyclic loading) is adopted to tune the hierarchical structure and properties of CNT yarns. By applying an optimized tensile strain (10%) for cyclic stretching, the CNT yarn exhibits much enhanced mechanical and electrical properties of tensile strength (+64%), Young's modulus (+148%), conductivity (+30%) and piezo-resistive sensitivity (+35%), as compared with pristine CNT yarn. Moreover, a comprehensive structural mechanism is proposed and confirmed to interpret the microstructure-reorganized mechanism. The microstructure-reorganized CNT yarn can be generally applied in advanced wearable textiles, flexible electronics and multifunctional composites with much improved mechanical and electrical performance especially, under cyclic loading conditions

Bio-Inspired Hierarchical Carbon Nanotube Yarn with Ester Bond Cross-Linkages towards High Conductivity for Multifunctional Applications

The cross-linked hierarchical structure in biological systems provides insight into the development of innovative material structures. Specifically, the sarcoplasmic reticulum muscle is able to transmit electrical impulses in skeletal muscle due to its cross-linked hierarchical tubular cell structure. Inspired by the cross-linked tubular cell structure, we designed and built chemical cross-links between the carbon nanotubes within the carbon nanotube yarn (CNT yarn) structure by an esterification reaction. Consequently, compared with the pristine CNT yarn, its electrical conductivity dramatically enhanced 348%, from 557 S/cm to 1950 S/cm. Furthermore, when applied with three voltages, the electro-thermal temperature of esterified CNT yarn reached 261 &deg;C, much higher than that of pristine CNT yarn (175 &deg;C). In addition, the esterified CNT yarn exhibits a linear and stable piezo-resistive response, with a 158% enhanced gauge factor (the ratio of electrical resistance changing to strain change ~1.9). The superconductivity, flexibility, and stable sensitivity of the esterified flexible CNT yarn demonstrate its great potential in the applications of intelligent devices, smart clothing, or other advanced composites