The Method of Measuring Particle Size Distribution of Layered Double Hydroxides（LDHs） Using Ultrasonic Attenuation Spectroscopy

doi:10.7503/cjcu20230463

Abstract

Abstract:

Layered double hydroxides（LDHs）， as a high-performance non-precious metal electrocatalyst， possess remarkable advantages in enhancing the oxygen evolution rate and reducing hydrogen production costs. The particle size of LDHs directly impacts the effective exposed surface area and intrinsic structure of catalytically active sites， thereby significantly influencing the activity duration and catalytic efficiency of LDHs in the electrocatalytic oxygen evolution reaction. Consequently， achieving online detection of LDHs particle size distribution holds significant importance for controlling LDHs synthesis and enhancing their catalytic activity. Based on the unique characteristics of LDHs， such as small particle size and distinctive morphology， this study introduces a novel approach for online measurement of particle size distribution（PSD） in LDHs suspensions via ultrasonic attenuation spectroscopy， marking the first-time application of this method in the field of LDHs particle characterization. The principal challenge in measuring the PSD of LDHs suspensions using ultrasonic attenuation spectroscopy lies in the requirement of system-specific properties for both the dispersed and continuous phases， which are often elusive to acquire. To address this challenge， this research utilizes principal component analysis（PCA） in conjunction with error backpropagation（BP） neural networks to establish a predictive model. Additionally， by incorporating genetic algorithm（GA） for model optimization， the challenges associated with ultrasonic attenuation spectroscopy are successfully addressed. The model is validated using a CoFeAl-LDHs suspension system. The results demonstrate the effectiveness of the PCA-GA-BP neural network in the online prediction of LDHs particle size distribution within the suspension system. The predicted values exhibit a high degree of resemblance to the true values in terms of peak shape， displaying minimal deviation in peak height. The mean squared error（MSE） between the predicted and true values is calculated as 0.1497， and the model fitting coefficient R² stands at 0.9768， thus indicating the efficacy of this method as an accurate approach for online measurement of LDHs particle size distribution.

Key words: Layered double hydroxides（LDHs）, Ultrasonic attenuation spectrum, Online detection of particle size distribution, bp Neural network, Genetic algorithm

CLC Number:

O657

TrendMD:

ZHANG Mingfeng, WU Bo, HOU Guanghao, ZHOU Lei, WANG Xuezhong. The Method of Measuring Particle Size Distribution of Layered Double Hydroxides（LDHs） Using Ultrasonic Attenuation Spectroscopy[J]. Chem. J. Chinese Universities, 2024, 45(3): 20230463.

Figures/Tables 11

References 33

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Network structure	MSE of training model	MSE of testing model
17⁃5⁃4	0.1334	0.1244
17⁃6⁃4	0.1377	0.1225
17⁃7⁃4	0.1371	0.1159
17⁃8⁃4	0.1406	0.1136
17⁃9⁃4	0.1365	0.1191
17⁃10⁃4	0.1295	0.1342
17⁃11⁃4	0.1416	0.1479
17⁃12⁃4	0.1397	0.1268
17⁃13⁃4	0.1610	0.1252
17⁃14⁃4	0.1332	0.1335

Network structure	MSE of training model	MSE of testing model
17⁃5⁃4	0.1334	0.1244
17⁃6⁃4	0.1377	0.1225
17⁃7⁃4	0.1371	0.1159
17⁃8⁃4	0.1406	0.1136
17⁃9⁃4	0.1365	0.1191
17⁃10⁃4	0.1295	0.1342
17⁃11⁃4	0.1416	0.1479
17⁃12⁃4	0.1397	0.1268
17⁃13⁃4	0.1610	0.1252
17⁃14⁃4	0.1332	0.1335

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