Guannan Zu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Guannan Zu has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Hong Wu
Shaanxi Key Laboratory of Nanomaterials and Nanotechnology, Xi’an Key Laboratory of Clean Energy, School of Mechanical & Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
1 shared publication - Hong Wu
School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
1 shared publication - Liangliang Wang
School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
1 shared publication - Meijuan Tong
School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
1 shared publication - Shuotao Xie
Shaanxi Key Laboratory of Nanomaterials and Nanotechnology, Xi’an Key Laboratory of Clean Energy, School of Mechanical & Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
1 shared publication - Xixiao Li
Xi’an Luoke Electronic Technology Co., Ltd., Xi’an 710048, China
1 shared publication
Latest Publications
- Open Access
An Additive-Free and Self-Supported MoS2/TiO2 Nanotube Array Composite for Enhancing the Li-Ion Storage Stability
Over the next decade, advanced lithium-based secondary batteries will require high-performance anodes to achieve superior energy density and cycling stability. In this work, anatase titanium dioxide nanotube arrays (TiO2 NTs) are fabricated on ultrathin Ti paper through anodization and subsequently thermal annealing. An urchin-like 2H-MoS2 coating is subsequently deposited onto the TiO2 NTs substrate through magnetic sputtering, constructing a self-supported hierarchical architecture without any additives. Electrochemical characterizations demonstrate that the MoS2/TiO2 NTs/Ti composite exhibits lower charge transfer resistance, enhanced rate capability, and improved cycling stability compared with bare TiO2 NTs/Ti and worm-like MoS2/Ti control groups. Structural analysis and density functional theory calculations further confirm that the strong interfacial interaction between MoS2 and TiO2 effectively stabilizes interfacial integrity during repeated cycling. Upon leveraging tunable geometric structure and mass loadings, this study offers a facile route for developing various types of advanced lithium-based secondary batteries.
Open AccessAn Additive-Free and Self-Supported MoS2/TiO2 Nanotube Array Composite for Enhancing the Li-Ion Storage Stability
Over the next decade, advanced lithium-based secondary batteries will require high-performance anodes to achieve superior energy density and cycling stability. In this work, anatase titanium dioxide nanotube arrays (TiO2 NTs) are fabricated on ultrathin Ti paper through anodization and subsequently thermal annealing. An urchin-like 2H-MoS2 coating is subsequently deposited onto the TiO2 NTs substrate through magnetic sputtering, constructing a self-supported hierarchical architecture without any additives. Electrochemical characterizations demonstrate that the MoS2/TiO2 NTs/Ti composite exhibits lower charge transfer resistance, enhanced rate capability, and improved cycling stability compared with bare TiO2 NTs/Ti and worm-like MoS2/Ti control groups. Structural analysis and density functional theory calculations further confirm that the strong interfacial interaction between MoS2 and TiO2 effectively stabilizes interfacial integrity during repeated cycling. Upon leveraging tunable geometric structure and mass loadings, this study offers a facile route for developing various types of advanced lithium-based secondary batteries.
- Open Access
Artificial Neural Network Modeling Enhancing Photocatalytic Performance of Ferroelectric Materials for CO2 Reduction: Innovations, Applications, and Neural Network Analysis
Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO2) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO2 into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis.
Open AccessArtificial Neural Network Modeling Enhancing Photocatalytic Performance of Ferroelectric Materials for CO2 Reduction: Innovations, Applications, and Neural Network Analysis
Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO2) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO2 into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis.