Supercritical CO2 dyeing for nylon, acrylic, polyester, and casein buttons and their optimum dyeing conditions by design of experiments

Coloration Modeling and Processing of Commodity Plastic Buttons in Supercritical Carbon Dioxide

We examined the color processing of the commodity plastic buttons made of acrylic, polyester, nylon, and casein with scCO₂. The buttons' dyeing color depth (K/S) was measured over a wide range of scCO₂ and correlated accurately with the response surface method. Moreover, we measured the solubility of C.I. Disperse Red 22 in scCO₂ to formulate a dye-sorption model for the K/S value in the color processing of the plastic buttons. Finally, the dye-sorption model for the K/S value combining the dye solubility in scCO₂ with the dye diffusion inside the buttons successfully represented the color processing of the buttons.

Red Disperse Azo Dye Side Chains Influence on Polyethylene Terephthalate Dyeing Performances in Supercritical Carbon Dioxide Media

Supercritical carbon dioxide dyeing (SDD) as a dyeing media not only provides a friendly dyeing environment but also significantly increases polymeric dyeing performances ascribed to strong azo dye affinity. Disperse azo dyes have shown to be highly efficient dyeing agents due to their facile coupling synthesis, side chains position, and length tunability to optimize absorption properties. Herein, we first synthesize two series of disperse red azo dyes via a coupling chemical route. Further, we investigate the position of the electron withdrawing group and alkyl chains length impact onto the absorption and color fastness properties. Upon synthesis, ¹H NMR and mass spectroscopy were used to characterize our newly synthesized series dye structure. Also, according to spectroscopic characterization, the functional group positions as well as the alkyl chains length have a major impact on the dye series maximum light absorption wavelength and performance. We have performed SDD dyeing of polyethylene terephthalate woven and determined each dye color fastness, we find that a reduced electron withdrawing effect and alkyl chains increase reduce color-fastness performances. Overall, our dyes exhibited a good resistance against detergent water, perspiration, abrasion, and friction.

Synthesis of Azo Disperse Dyes with High Absorption for Efficient Polyethylene Terephthalate Dyeing Performances in Supercritical Carbon Dioxide

Supercritical carbon dioxide dyeing (SCDD) not only enables strong dyeing performance for a versatile range of polymer material but is also regarded as a green chemical media due to its low environmental impact as well as low risk of product denaturation. Over the decades, azo disperse dyes have been revealed to be efficient dyes and represent the wide majority of dyeing material. Azo dyes possess a wide variety of functional groups to optimize dye synthesis and tune the light absorption properties. Using SCDD, end-chain of different lengths, and functional group exhibiting various electronic affinity, six disperse red azo dyes were synthesized to investigate dyeing performances as woven fabric type, color strain, and color fastness after dyeing are discussed. Dye structure synthesized through a coupling reaction was confirmed by ¹H NMR and mass spectroscopy. We found that the light absorption wavelength and absorption coefficient value variation are associated to the nature of the functional group. From the color strength values of the polyethylene terephthalate woven after dyeing, we find that the fiber host and dye dopant chemical structure greatly influence the dyeing process by providing enhanced woven, color strain, and color fastness. In comparison with commercial products, our approach not only improves the dyeing process but also guarantees a strong resistance of the dyed product against water, detergent, perspiration, abrasion, and friction.

A Dissolution Kinetic Study of Disperse Dye in Supercritical Carbon Dioxide to Design an Efficient Supercritical Dyeing Process

The dissolution behavior of dye in supercritical carbon dioxide influences the overall mass transfer that controls a supercritical dyeing process. Increasing the dissolution rate of the dye leads to shortening of the dyeing process time and can improve the efficiency of the process. Controlling the properties of the carbon dioxide flow is a good method to improve the dissolution rate of dyes. In this study, a dissolution kinetic model was designed by quantitatively analyzing and formulating the dissolution phenomenon of dyes using an in situ UV/Vis spectrometer. Through this model, the dissolution rate was compared by varying the geometric shape of the column containing the dye and the flow rate of carbon dioxide. Moreover, the correlation equation between the Reynolds number and Sherwood number was obtained through mass transfer coefficients derived under various conditions. In order to verify the utility of this equation, it was applied to a scaled-up device and the precise result could be predicted. This study can be useful in the design of dyeing processes and make-up equipment.

Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.