Active Ruthenium-Based High-Entropy Nanozyme Through a "Chemical Tongue" for Recognizing Bioactive Components in...

The accurate identification and discrimination of multiple endogenous bioactive ingredients in medicinal and edible honeysuckle are of considerable importance for food quality control and human health. Herein, the integration of a high-entropy nanozyme-based "chemical tongue" with a machine learning algorithm is proposed for the first time to recognize seven phenolic components of honeysuckle. First, we have rationally designed a quinary metal element (RuNiFeMnCo) high-entropy layered double hydroxide (HE-LDH) nanozyme with peroxidase-like (POD-like) performance. The specific activity of quinary HE-LDH with up to 34.85 U/mg is significantly higher than binary NiFe-LDH, ternary NiFeMn-LDH, and quaternary NiFeMnCo-LDH, which is approximately 50 times that of binary NiFe-LDH. The enormously boosted catalytic performance benefits from both metal Ru as the main active site and the "cocktail effect" from multiple transition elements' synergistic interaction. Second, the HE-LDH nanozyme can catalyze three substrates to their corresponding color-oxidized products as three channels of "chemical tongue". The endogenous phenolic compounds of honeysuckle can inhibit the chromogenic process to different extents due to their discrepant antioxidant properties, offering distinct "fingerprints" for each species. Furthermore, the fingerprint decryption displays the excellent recognition of seven phenolic compounds at concentrations as low as 10 nM and the wide linear range of 0.01-50 μM for individual species with the assistance of the machine learning algorithm. Finally, the robust recognition model with simultaneous concentration-independent and matrix-independent performance is first constructed to discriminate seven phenolic compounds with concentrations ranging from 0.01 to 10 μM spiked into four actual honeysuckle samples. The identification accuracy of phenolic compounds against the colorimetric sensor array is increased from 58.84% by linear discrimination analysis (LDA) to 98.04% by the random forest (RF) algorithm. More importantly, this strategy provides a new avenue of high-entropy nanozyme as a fascinating platform for the configuration of the sensor array.