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Synthesize degradation mechanisms, kinetic models, and comparative efficiencies across textile dye classes to address incomplete mineralization and persistent transformation product toxicity.

September 17, 2026Discover EnvironmentOpen Access

Textile dye degradation technologies with mechanistic insights, kinetic modeling, and comparative performance analysis

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Authors

MRManvi Rawat MJMurari Kumar Jha SRSumit Kumar Rai

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Overview

Review reveals incomplete mineralization and persistent mutagenicity across textile dye treatment systems, highlighting the superior efficacy of hybrid biological and advanced oxidation processes.

Key Points

Synthesize degradation mechanisms, kinetic models, and comparative efficiencies across textile dye classes to address incomplete mineralization and persistent transformation product toxicity.
Evaluated biological, chemical, and advanced oxidation degradation pathways across four major dye structural classes: azo, anthraquinone, triarylmethane, and phthalocyanine.
Assessed quantitative kinetic frameworks, including Haldane substrate-inhibition and Behnajady–Modirshahla–Ghanbery (BMG) models, alongside matrix interference from salts and organic matter.
Analyzed reactive radical speciation validated through electron paramagnetic resonance (EPR) spin-trapping and benchmarked toxicity outcomes across Ames, Daphnia magna, and zebrafish embryo assays.
Photocatalytic systems achieved 90% to 99% effluent decolorization under visible light, but post-treatment bioassays confirmed that non-mineralized transformation products such as aromatic amines maintained high mutagenicity.
Integrated sequential anaerobic–aerobic and bio-advanced oxidation systems achieved 85% to 97% total organic carbon (TOC) removal, outperforming standalone treatment technologies.
Common water matrix constituents, specifically chloride (10–100 mM) and humic acid (5–20 mg/L), significantly suppressed hydroxyl radical oxidation efficiency and hindered biological degradation rates.

Cite This Study

Rawat et al. (2026) studied this question.

synapsesocial.com/papers/6aabb7155f706d05830e5e27 https://doi.org/10.1007/s44274-026-00979-8

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September 17, 2026Discover EnvironmentOpen Access

Textile dye degradation technologies with mechanistic insights, kinetic modeling, and comparative performance analysis

View Full Paper

Ask AI

Bookmark

Authors

MRManvi Rawat MJMurari Kumar Jha SRSumit Kumar Rai

Discussion

Member takes

Overview

Review reveals incomplete mineralization and persistent mutagenicity across textile dye treatment systems, highlighting the superior efficacy of hybrid biological and advanced oxidation processes.

Key Points

Synthesize degradation mechanisms, kinetic models, and comparative efficiencies across textile dye classes to address incomplete mineralization and persistent transformation product toxicity.
Evaluated biological, chemical, and advanced oxidation degradation pathways across four major dye structural classes: azo, anthraquinone, triarylmethane, and phthalocyanine.
Assessed quantitative kinetic frameworks, including Haldane substrate-inhibition and Behnajady–Modirshahla–Ghanbery (BMG) models, alongside matrix interference from salts and organic matter.
Analyzed reactive radical speciation validated through electron paramagnetic resonance (EPR) spin-trapping and benchmarked toxicity outcomes across Ames, Daphnia magna, and zebrafish embryo assays.
Photocatalytic systems achieved 90% to 99% effluent decolorization under visible light, but post-treatment bioassays confirmed that non-mineralized transformation products such as aromatic amines maintained high mutagenicity.
Integrated sequential anaerobic–aerobic and bio-advanced oxidation systems achieved 85% to 97% total organic carbon (TOC) removal, outperforming standalone treatment technologies.
Common water matrix constituents, specifically chloride (10–100 mM) and humic acid (5–20 mg/L), significantly suppressed hydroxyl radical oxidation efficiency and hindered biological degradation rates.

Cite This Study

Rawat et al. (2026) studied this question.

synapsesocial.com/papers/6aabb7155f706d05830e5e27 https://doi.org/10.1007/s44274-026-00979-8

View Full Paper

Ask AI

Bookmark