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

Sustainable flame retardant treatment for cotton fabric using non formaldehyde cross linking agent

Most flame-retardant finishing agents have been found to have an adverse effect on our environment and human skin because of the carcinogenic chemicals in their structure. Pyrovatex CP New is an Organophosphorus based flame retardant (FR) agent widely used in FR treatment of combustible. However, the main problem related to it is the release of high formaldehyde content (a known carcinogen). When used with methylated melamine (MM) an effective cross-linker. The objective of this research was to use citric acid (CA) and its integration with sodium hypophosphite (NaH2PO2) and two different co-catalyst Titanium dioxide (TiO2) and phosphoric acid (PA) as a flame‐retardant finishing for cotton fabrics. The flammability of cotton fabric was assessed by a manual vertical flammability test, it is found that the combination of co-catalysts in FR formulation lowers the flammability of cotton. The pyrolysis characteristics and char residue yield of the treated cotton shows that the flame retardancy improves as the amount of catalyst is increased. The whiteness index, crease recovery and tensile strength of the treated cotton fabric was also significantly improved with our suggested recipe formulation. The finished cotton has significant variations in terms of its tensile strength, crease recovery, and whiteness index

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