Versatile laccase-mimicking enzyme for dye decolorization and tetracyclines identification upon a colorimetric array sensor

Exploring the progressive change in transformation and toxicity of polycyclic dyes during aerobic biodegradation

Structure-activity models can rapidly assess the biodegradability and toxicity of dye. However, these properties dynamically change during biodegradation due to byproduct formation. In this study, the aerobic biodegradation of common polycyclic dyes (PDs) and their precursors, including anthraquinone dyes, triarylmethane dyes, azo dyes, substituted naphthalene, and tricyclic aromatic hydrocarbons was studied. We used combined in vivo and silico approaches to analyze their biodegradation kinetics and toxicity evolution. Most compounds were rapidly degraded within 6-8 h, with substituted naphthalene exhibiting the highest median maximum degradation rate (kmax = 0.278 h-1). Our molecular dynamics simulations quantified the binding energies between compounds and oxidoreductases (-20.27 ± 2.61 to -53.24 ± 3.57 kcal/mol), revealing that stronger binding interactions correlated with lower kmax values. Furthermore, we developed a novel toxicity assessment method using the inhibition/TOC (I/TOC) ratio, revealing increased toxicity post-biodegradation for most compounds. Triarylmethane dyes exhibited significantly higher median I/TOC values (p < 0.05). HPLC-TOF-MS analysis identified 18 major transformation products. Toxicity estimation software tool (T.E.S.T) predictions confirmed that the transformation products exhibited higher toxicity than parent compounds. Our integrated analytical approach, combining experimental biodegradation kinetics, molecular simulation, and toxicity evolution, provides crucial insights for evaluating and managing environmental risks of emerging pollutants during wastewater treatment.

Machine learning assisted nanozyme sensor array for accurate identification and discrimination of flavonoids in healthy tea

Identifying flavonoids in herbs is of great significance for elucidating their biological activity and pharmacological effects. However, distinguishing and detecting multiple flavonoids simultaneously remains a challenge. Here, an innovative citric acid-Cu (CA-Cu) nanozyme with peroxidase mimic (POD) and laccase mimic (LAC) activities was successfully synthesized. Due to the varying inhibitory effects of flavonoids on CA-Cu dual-enzyme mimicking activities, and the degree of inhibition increasing with prolonged reaction time, a nanozyme sensor array was constructed based on reaction kinetics and applied to the identification of five flavonoids. This technique further streamlines the building of sensing channels. Moreover, by integrating various machine learning algorithms with the sensor arrays, accurate identification and prediction of five flavonoids in multiple herb samples have been successfully achieved. Finally, the sensor array successfully achieved the differentiation and recognition of multiple healthy tea, demonstrating its feasibility in efficiently distinguishing and detecting flavonoids in complex samples.