Xinzhong Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Xinzhong Liu has a total of 20 co-authors. Here are the top co-authors by number of shared publications:
Dongdong Huan
Fujian University of Technology, China
2 shared publicationsQian Zhang
Fuzhou University, China
2 shared publicationsYuxiang Zhao
Fujian University of Technology, China
2 shared publicationsBiwen Liu
School of Ecological Environment and Urban Construction, Fujian University of Technology, Fuzhou 350000, China
1 shared publication- Biwen Liu
Fujian University of Technology, China
1 shared publication Chenhui Qiu
School of Ecological Environment and Urban Construction, Fujian University of Technology, Fuzhou 350000, China
1 shared publication
Latest Publications
- Open Access
- 11 April 2023
- Published by Royal Society of Chemistry (RSC) in RSC Advances
- Vol. 13 (17) , 11762-11770
Influence of high altitude on the strength of aerated concrete
The specimens prepared with different temperature have different content and crystallinity of tobermorite, resulting in different strength.
- 11 April 2023
- Published by Royal Society of Chemistry (RSC) in RSC Advances
- Vol. 13 (17) , 11762-11770
Open AccessInfluence of high altitude on the strength of aerated concrete
The specimens prepared with different temperature have different content and crystallinity of tobermorite, resulting in different strength.
- Open Access
- 1 January 2021
- Published by Wiley in Wireless Communications and Mobile Computing
- Vol. 2021 (1)
Image Analysis Technology in the Detection of Particle Size Distribution and the Activity Effect of Low‐Silicon Copper Tailings
To speed up the comprehensive utilization and treatment of copper tailings, the digital image processing technology is proposed in this study to detect the low‐silicon copper tailings (LSCT) using a scanning electron microscope (SEM), and the particle size distribution (PSD) and the activity of LSCT are analysed under the action of mechanical force. Firstly, the current status and application of copper tailings are introduced, and the influence of the particle size of LSCT on its practical application performance is explained. Secondly, the LSCT SEM image target recognition model is designed based on the convolutional neural network (CNN), and the model parameters and the reference CNN are selected. Finally, the experimental process is designed, a SEM image data set of LSCT is prepared, the model is trained through the training set, and the image recognition test is performed on the produced data set. The experimental results show that when the number of iterations of the CNN is 10, the accuracy of model recognition can be guaranteed. After the action of mechanical force, the PSD of LSCT is mainly concentrated at 1 μm~100 μm; that around 1.4 μm~10 μm is the largest, and the PSD of LSCT around 1.4 μm increases with the increase of action time of mechanical force, but the PSD of the LSCT begins to increase when the grinding time exceeds 150 minutes, and the activity of LSCT reaches the maximum (75.545%) at 150 minutes. The average accuracy of SEM image detection of the model is 86.97%, and the model based on DenseNet shows better recognition accuracy than other models. This study provides a reference for analysing the PSD of LSCT.
- 1 January 2021
- Published by Wiley in Wireless Communications and Mobile Computing
- Vol. 2021 (1)
Open AccessImage Analysis Technology in the Detection of Particle Size Distribution and the Activity Effect of Low‐Silicon Copper Tailings
To speed up the comprehensive utilization and treatment of copper tailings, the digital image processing technology is proposed in this study to detect the low‐silicon copper tailings (LSCT) using a scanning electron microscope (SEM), and the particle size distribution (PSD) and the activity of LSCT are analysed under the action of mechanical force. Firstly, the current status and application of copper tailings are introduced, and the influence of the particle size of LSCT on its practical application performance is explained. Secondly, the LSCT SEM image target recognition model is designed based on the convolutional neural network (CNN), and the model parameters and the reference CNN are selected. Finally, the experimental process is designed, a SEM image data set of LSCT is prepared, the model is trained through the training set, and the image recognition test is performed on the produced data set. The experimental results show that when the number of iterations of the CNN is 10, the accuracy of model recognition can be guaranteed. After the action of mechanical force, the PSD of LSCT is mainly concentrated at 1 μm~100 μm; that around 1.4 μm~10 μm is the largest, and the PSD of LSCT around 1.4 μm increases with the increase of action time of mechanical force, but the PSD of the LSCT begins to increase when the grinding time exceeds 150 minutes, and the activity of LSCT reaches the maximum (75.545%) at 150 minutes. The average accuracy of SEM image detection of the model is 86.97%, and the model based on DenseNet shows better recognition accuracy than other models. This study provides a reference for analysing the PSD of LSCT.
- Open Access
- 1 January 2020
- Published by Elsevier in Chemical Physics Letters
- Vol. 738
Photocatalytic degradation of toluene by In2S3/g-C3N4 heterojunctions
- 1 January 2020
- Published by Elsevier in Chemical Physics Letters
- Vol. 738
Open AccessPhotocatalytic degradation of toluene by In2S3/g-C3N4 heterojunctions
- 6 June 2018
- Published by Springer Nature in Journal of Materials Science
- Vol. 53 (18) , 12795-12806
One-step mechanochemical synthesis of plasmonic Ag/Zn–Al LDH with excellent photocatalytic activity
- 6 June 2018
- Published by Springer Nature in Journal of Materials Science
- Vol. 53 (18) , 12795-12806
One-step mechanochemical synthesis of plasmonic Ag/Zn–Al LDH with excellent photocatalytic activity
- 3 March 2016
- Published by Taylor & Francis in Connective Tissue Research
- Vol. 57 (4) , 270-276
Effect of γ-PGA on the formation of collagen fibrilsin vitro
The effect of γ-poly(glutamic acid) (γ-PGA) on the self-assembly of collagen was studied. Under physiological conditions, the kinetic curves for fibril formation showed that the turbidity of collagen/γ-PGA blends at 313 nm was increased with the addition of γ-PGA. Furthermore, it was shown using both field-emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) that fibrils with a larger diameter were obtained following the addition of γ-PGA, probably due to the electrostatic and hydrogen bond interactions between collagen and γ-PGA, which promoted the lateral association of collagen molecules. In addition, both the thermal stability and viscoelastic properties of the hybrid hydrogels, which were evaluated by differential scanning calorimetry and rheological measurements, respectively, were improved by the addition of γ-PGA.
- 3 March 2016
- Published by Taylor & Francis in Connective Tissue Research
- Vol. 57 (4) , 270-276
Effect of γ-PGA on the formation of collagen fibrilsin vitro
The effect of γ-poly(glutamic acid) (γ-PGA) on the self-assembly of collagen was studied. Under physiological conditions, the kinetic curves for fibril formation showed that the turbidity of collagen/γ-PGA blends at 313 nm was increased with the addition of γ-PGA. Furthermore, it was shown using both field-emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) that fibrils with a larger diameter were obtained following the addition of γ-PGA, probably due to the electrostatic and hydrogen bond interactions between collagen and γ-PGA, which promoted the lateral association of collagen molecules. In addition, both the thermal stability and viscoelastic properties of the hybrid hydrogels, which were evaluated by differential scanning calorimetry and rheological measurements, respectively, were improved by the addition of γ-PGA.