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Prediction of Acid Red 138 solubility in supercritical CO₂ with water co-solvent.

ABSTRACT: Acid Red 138, as a weak acid azo dye with a long alkyl chain, is widely used for protein fiber dyeing while it cannot dissolve in supercritical carbon dioxide. The objective of this study is to investigate the solubility of Acid Red 138 with water at the temperatures of 353.15, 363.15, 373.15, 393.15 and 413.15 K and over a pressure range of 20 to 26 MPa. The test results revealed that the phase equilibrium of water and Acid Red 138 was affected by the competition between pressure and density of supercritical carbon dioxide. Furthermore, the experimental solubilities were correlated by three types of density-based model. Good agreement with less than 3.36% of average absolute relative deviation between the calculated and the experimental data of water was achieved. In addition, the Chrastil model, Mendez-Santiago-Teja model and Sung-Shim model exhibited excellent correlation results for Acid Red 138 solubilities with the AARD values of 8.58%, 6.06% and 5.19%. Better understanding of the solubility behavior of Acid Red 138 in supercritical carbon dioxide displays potential for developing a microemulsion system for the eco-friendly dyeing of natural fibers.

SUBMITTER: Ye F

PROVIDER: S-EPMC9076466 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Prediction of Acid Red 138 solubility in supercritical CO<sub>2</sub> with water co-solvent.

Ye Fang F Zhao Yuping Y Mao Zhiping Z Zheng Laijiu L Zheng Huanda H Ke Huizhen H

RSC advances 20191216 71

Acid Red 138, as a weak acid azo dye with a long alkyl chain, is widely used for protein fiber dyeing while it cannot dissolve in supercritical carbon dioxide. The objective of this study is to investigate the solubility of Acid Red 138 with water at the temperatures of 353.15, 363.15, 373.15, 393.15 and 413.15 K and over a pressure range of 20 to 26 MPa. The test results revealed that the phase equilibrium of water and Acid Red 138 was affected by the competition between pressure and density of ...[more]

PMID: 35541593

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Project description:In the industrial sector, understanding the behavior of block copolymers in supercritical solvents is crucial. While qualitative agreement with polymer solubility curves has been evaluated using complex theoretical models in many cases, quantitative predictions remain challenging. This study aimed to create a rapid and accurate artificial neural network (ANN) model to predict the lower critical solubility and upper critical solubility space of an atypical block copolymer, poly(styrene-co-octafluoropentyl methacrylate) (PSOM), in different supercritical solvent systems over a wide range of temperatures (51.75-182.05 °C) and high pressure (3.28-200.86 MPa). The experimental data set used in this study included one copolymer, five supercritical solvents, one cosolvent, and one initiator. It consisted of seven unique copolymer-solvent combinations (252 cloud point pressures) used to predict the model quantitatively and qualitatively. To predict the PSOM-solvent interactions, the study considered two different input systems: a six-variable system, a five-variable system, and one target output. Initially, we used a three-layer feed-forward neural network to select the best learning algorithm (Levenberg-Marquardt) from 14 different algorithms, considering one sample PSOM-solvent system. Then, the network topology was optimized by varying hidden neuron numbers from 2 to 80 for all seven PSOM-solvent combination systems. The predicted cloud point pressures were in excellent agreement with the experimental cloud point pressures, confirming the model's accuracy. It is clear from the results of a minimum mean square error (≤1.90 × 10-27) and maximum linear regression R 2 (≥0.99) during training, validation, and testing of all the data sets. Further, the ANN model accuracy was tested by statistical analysis, confirming the model's ability to accurately capture the miscibility regions of polymers, enabling efficient processing of various polymer materials. This data-driven approach facilitates the prediction of coexistence curves for other polymers and complex macromolecular systems.

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Prediction of Acid Red 138 solubility in supercritical CO₂ with water co-solvent.

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Prediction of Acid Red 138 solubility in supercritical CO<sub>2</sub> with water co-solvent.

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Prediction of Acid Red 138 solubility in supercritical CO2 with water co-solvent.

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Prediction of Acid Red 138 solubility in supercritical CO<sub>2</sub> with water co-solvent.

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OmicsDI is part of the ELIXIR infrastructure

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Prediction of Acid Red 138 solubility in supercritical CO₂ with water co-solvent.