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1,507 results on '"Shi, Liyi"'

26. Antioxidant activity of NSAIDs-Se derivatives: predictive QSAR-machine learning models.

Author

Fu, Zhihui, Wiriyarattanakul, Amphawan, Xie, Wanting, Jantorn, Pattamon, Toopradab, Borwornlak, Shi, Liyi, Rungrotmongkol, Thanyada, and Maitarad, Phornphimon

Subjects
ARTIFICIAL neural networks, MACHINE learning, STANDARD deviations, QSAR models, RANDOM forest algorithms
Abstract

The study employed the random forest (RF) and artificial neural network (ANN) methods based on the quantitative structure–activity relationship (QSAR) techniques to analyze NSAIDs-Se derivatives and their antioxidant abilities. The best predictive models by the RF method yielded a coefficient of determination (R2) value of 0.868 for the training set, and the root mean square error (RMSE) of the test set was only 0.053. For the QSAR-ANN, the best predictive models resulted in an R2 value of 0.935, and the RMSE of the test set was 0.068. Based on the best models, the steric, electrostatic, and enthalpy descriptors are found to be related to antioxidant prediction. Thus, to extend the predictive ability of the obtained QSAR-ML models, an external set was collected from a later publication of NSAIDs-Se derivatives with experimental antioxidant abilities. The efficacy of two QSAR models in forecasting the antioxidant abilities of an external set of NSAIDs-Se derivatives was evaluated. The QSAR with machine learning models demonstrated high efficiency in predicting the antioxidant abilities of the external NSAIDs-Se set with an RMSE of the external set in the range of 0.074–0.087. Therefore, the results suggest that fine-tuning machine learning-based QSAR studies can aid in the design of novel NSAIDs-Se derivatives with highly efficient antioxidant prediction. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Electrodeposition of a 3D dendritic Cu–Sn bimetallic alloy for electrocatalytic nitrate reduction to ammonia.

Author

Zhao, Jinxiu, Xu, Jingyi, and Shi, Liyi

Subjects
ALLOY plating, DENITRIFICATION, COPPER-tin alloys, ELECTROPLATING, STANDARD hydrogen electrode, ALLOYS
Abstract

The development of effective and selective catalysts is the key to improving the multi-electron transfer nitrate reduction reaction (NO3−RR) to ammonia (NH3). Herein, we prepared a copper–tin bimetallic alloy using the dynamic hydrogen bubble template electrodeposition technique for electrocatalytic NO3− reduction to NH3. The NH3 yield rate and the faraday efficiency were up to 293.12 μmol h−1 cm−2 and 78.57% at −1.2 V versus reversible hydrogen electrode in 0.1 M Na2SO4 (containing 0.01 M NaNO3) electrolyte. This paper will provide new suggestions for the use of bimetallic alloys for electrochemical removal of nitrate and ammonia synthesis from water. [ABSTRACT FROM AUTHOR]

Published
2024
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