Issue 16, 2023

A machine learning-based nano-photocatalyst module for accelerating the design of Bi2WO6/MIL-53(Al) nanocomposites with enhanced photocatalytic activity

Abstract

It is a great challenge to acquire novel Bi2WO6/MIL-53(Al) (BWO/MIL) nanocomposites with excellent catalytic activity by the trial-and-error method in the vast untapped synthetic space. The degradation rate of Rhodamine B dye (DRRhB) can be used as the main parameter to evaluate the catalytic activity of BWO/MIL nanocomposites. In this work, a machine learning-based nano-photocatalyst module was developed to speed up the design of BWO/MIL with desirable performance. Firstly, the DRRhB dataset was constructed, and four key features related to the synthetic conditions of BWO/MIL were filtered by the forward feature selection method based on support vector regression (SVR). Secondly, the SVR model with radical basis function for predicting the DRRhB of BWO/MIL was established with the key features and optimal hyperparameters. The correlation coefficients (R) between predicted and experimental DRRhB were 0.823 and 0.884 for leave-one-out cross-validation (LOOCV) and the external test, respectively. Thirdly, potential BWO/MIL nanocomposites with higher DRRhB were discovered by inverse projection, the prediction model, and virtual screening from the synthesis space. Meanwhile, an online web service (http://1.14.49.110/online_predict/BWO2) was built to share the model for predicting the DRRhB of BWO/MIL. Moreover, sensitivity analysis was brought into boosting the model's explainability and illustrating how the DRRhB of BWO/MIL changes over the four key features, respectively. The method mentioned here can provide valuable clues to develop new nanocomposites with the desired properties and accelerate the design of nano-photocatalysts.

Graphical abstract: A machine learning-based nano-photocatalyst module for accelerating the design of Bi2WO6/MIL-53(Al) nanocomposites with enhanced photocatalytic activity

Supplementary files

Article information

Article type
Paper
Submitted
24 fev 2023
Accepted
20 may 2023
First published
06 iyn 2023
This article is Open Access
Creative Commons BY-NC license

Nanoscale Adv., 2023,5, 4065-4073

A machine learning-based nano-photocatalyst module for accelerating the design of Bi2WO6/MIL-53(Al) nanocomposites with enhanced photocatalytic activity

X. Zhai and M. Chen, Nanoscale Adv., 2023, 5, 4065 DOI: 10.1039/D3NA00122A

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements