Prediction and analysis of fabric-evoked prickle properties of different textile woven fabrics using Artificial Neural Networks method

This paper aims to discuss the design and development of an Artificial Neural Networks (ANNs) model to understand a human perception of the tactile prickliness properties of textile wear fabric materials, and create an objective system to express those prickle perceptions in terms of measurable mechanical properties. The objective and also subjective hand measurement of the textile materials used for wear fabric has been check up on with consideration given the aspects of both dermatitis and comfort. In this study, attempt to predict the prickliness (itchiness) of wear fabric by their physical properties using a back-propagation network and a stepwise regression. Handle properties of fabrics were measured by universal test equipment (KES-F) and total prickle-score (TPS) values of the wear fabrics were determined by a group of panelists consisting of some textile experts. The optimum construction of neural network was investigated through the change of layer and neuron number. The results showed that the back-propagation network could predict the (TPS) values of wear fabric with a meaningful difference. These wear fabrics were used to show that the results of neural network were in good agreement with subjective test results

Complex Aerogels Generated from Nano-Polysaccharides and Its Derivatives for Oil–Water Separation

The complex aerogel generated from nano-polysaccharides, chitin nanocrystals (ChiNC) and TEMPO-oxidized cellulose nanofibers (TCNF), and its derivative cationic guar gum (CGG) is successfully prepared via a facile freeze-drying method with glutaraldehyde (GA) as cross-linkers. The complexation of ChiNC, TCNF, and CGG is shown to be helpful in creating a porous structure in the three-dimensional aerogel, which creates within the aerogel with large pore volume and excellent compressive properties. The ChiNC/TCNF/CGG aerogel is then modified with methyltrichlorosilane (MTCS) to obtain superhydrophobicity/superoleophilicity and used for oil–water separation. The successful modification is demonstrated through FTIR, XPS, and surface wettability studies. A water contact angle of 155° on the aerogel surface and 150° on the surface of the inside part of aerogel are obtained for the MTCS-modified ChiNC/TCNF/CGG aerogel, resulting in its effective absorption of corn oil and organic solvents (toluene, n-hexane, and trichloromethane) from both beneath and at the surface of water with excellent absorption capacity (i.e., 21.9 g/g for trichloromethane). More importantly, the modified aerogel can be used to continuously separate oil from water with the assistance of a vacuum setup and maintains a high absorption capacity after being used for 10 cycles. The as-prepared superhydrophobic/superoleophilic ChiNC/TCNF/CGG aerogel can be used as a promising absorbent material for the removal of oil from aqueous media