Abstract:The textile industry needs a high volume of water to carry out its processes, which among them; textile chemical finishing is the main responsible for high water expenditure. However, nowadays there is a worldwide concern regarding the scarcity of fresh water. This research aims to study the use of seawater in the cotton bleaching process and compare the results obtained with the bleaching process made with distilled water. The characterization of the substrates was performed through the following analyses: re… Show more
“…Untreated cotton fabrics’ morphology was also observed by Zhang and co-workers and Ferreira and co-workers. 48 , 49 Fig. 5 SEM micrographs of cotton fabrics before and after they had been treated with different contents of chitosan nanocapsule: (a) untreated, (b) PC-1, (c) PC-2, (d) PC-4, and (e) PC-8.…”
Chitosan is a cationic polysaccharide with intrinsic antimicrobial properties that can be used as an ecological alternative to develop functional materials to inhibit the proliferation of microorganisms. This work evaluates chitosan nanocapsules (CNs) as a self-disinfecting agent to provide bactericidal activity on cotton fabrics (CF), using polyacrylate to bind the CNs on the CF surface. The fabrics were characterized by Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), contact angle (CA), moisture retention, and antimicrobial tests against
Escherichia coli
and
Bacillus subtilis
. The FTIR results showed new peaks related to chitosan structure, indicating the adequate fixation of the CNs on the cotton fibers. SEM images corroborated the polyacrylate binder's efficient adhesion, connecting the CNs and the cotton fiber surface. The CF surface properties were considerably modified, while CN/polyacrylate coating promoted antibacterial activity against the
B. subtilis
(gram-positive bacteria) for the developed wipe, but they do not display bactericidal effects against
E. coli
(gram-negative bacteria).
Graphical abstract
Supplementary Information
The online version contains supplementary material available at 10.1007/s11998-023-00761-y.
“…Untreated cotton fabrics’ morphology was also observed by Zhang and co-workers and Ferreira and co-workers. 48 , 49 Fig. 5 SEM micrographs of cotton fabrics before and after they had been treated with different contents of chitosan nanocapsule: (a) untreated, (b) PC-1, (c) PC-2, (d) PC-4, and (e) PC-8.…”
Chitosan is a cationic polysaccharide with intrinsic antimicrobial properties that can be used as an ecological alternative to develop functional materials to inhibit the proliferation of microorganisms. This work evaluates chitosan nanocapsules (CNs) as a self-disinfecting agent to provide bactericidal activity on cotton fabrics (CF), using polyacrylate to bind the CNs on the CF surface. The fabrics were characterized by Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), contact angle (CA), moisture retention, and antimicrobial tests against
Escherichia coli
and
Bacillus subtilis
. The FTIR results showed new peaks related to chitosan structure, indicating the adequate fixation of the CNs on the cotton fibers. SEM images corroborated the polyacrylate binder's efficient adhesion, connecting the CNs and the cotton fiber surface. The CF surface properties were considerably modified, while CN/polyacrylate coating promoted antibacterial activity against the
B. subtilis
(gram-positive bacteria) for the developed wipe, but they do not display bactericidal effects against
E. coli
(gram-negative bacteria).
Graphical abstract
Supplementary Information
The online version contains supplementary material available at 10.1007/s11998-023-00761-y.
“…The higher value of WI indicates the greater degree of whiteness of the cotton fabric. Whiter cotton fabric is desired before it is dyed, printed or other wettreatments (Ferreira et al 2019;Kabir et al 2014).…”
The textile bleaching process that involves hot hydrogen peroxide (H 2 O 2 ) solution is commonly practised in cotton fabric manufacture. The purpose of the bleaching process is to remove color from the cotton, achieving a permanent white before proceeding to dyeing or shape matching. Normally, the visual ratings of whiteness on the cotton are measured based on whiteness index (WI). However, it is found that there is little research on chemical predictive modelling of the cotton fabric's WI compared to experimental study. Analytics using predictive modelling can forecast the outcomes, leading to better-informed cotton quality assurance and control decisions. Up to date, there is limited study applying least square support vector regression (LSSVR) model in the textile domain. Hence, the present study aims to develop a multi-output LSSVR (MLSSVR) model using bleaching process variables and results obtained from two different case studies to predict the WI of cotton. The predictive accuracy of the MLSSVR model was measured by root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R 2 ). The obtained results were compared with other regression models including partial least square regression, predictive fuzzy model, locally weighted partial least square regression, and locally weighted kernel partial least square regression. Our findings indicate that the proposed MLSSVR model performed better than other models in predicting the WI as it showed significantly lower values of RMSE and MAE. Furthermore, it provided the highest R 2 values which are up to 0.9999.
“…The higher the value of WI indicates the greater the degree of whiteness of the cotton fabric. Whiter cotton fabric is desired before it is dyed, printed or other wet-treatments (Ferreira et al 2019;Kabir et al 2014). Many studies have reported that WI of cotton fabric can be increased by H2O2 concentrations and T (Abdul and Narendra 2013;Ferdush et al).…”
The textile bleaching process uses a hydrogen peroxide (H2O2) solution in alkali pH associated with high temperature is the commonly used bleaching procedure in cotton fabric manufacture. The purpose of the bleaching process is to remove the natural colour from cotton to obtain a permanent white colour before dyeing or shape matching. Normally, the visual ratings of whiteness on the cotton are measured by the whiteness index (WI). Notice that lesser research study is focusing on chemical predictive modelling of the WI of cotton fabric than its experimental study. Predictive analytics using predictive modelling can forecast the outcomes that can lead to better-informed cotton quality assurance and control decisions. Up to date, limited study applying least square support vector regression (LSSVR) based model in the textile domain. Hence, the present study was aimed to develop the LSSVR-based model, namely multi-output LSSVR (MLSSVR) using bleaching process variables to predict the WI of cotton. The predictive accuracy of the MLSSVR model is measured by root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), and its results are compared with other regression models including partial least square regression, predictive fuzzy model, locally weighted partial least square regression and locally weighted kernel partial least square regression. The results indicate that the MLSSVR model performed better than other models in predicting the WI as it has 60–1209% lower values of RMSE and MAE as well as it provided the highest R2 values which are up to 0.9985.
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