Artificial Neural Network Modeling Enhancing Photocatalytic Performance of Ferroelectric Materials for CO2 Reduction: Innovations, Applications, and Neural Network Analysis
Abstract
1. Introduction
2. Photocatalytic CO2 Conversion Optimization
2.1. Photocatalysis for Sustainable CO2 Conversion
2.1.1. Light Absorption
2.1.2. Load Separation
2.1.3. Surface Reactivity
2.2. Materials and Methods
3. Enhancing Ferroelectric Catalytic Reactivity
3.1. Surface Engineering
3.2. Defect Engineering
3.3. Surface Optimization Strategies
3.4. Adjusting the Electronic Structure
4. Advantage of Ferroelectric Materials as Photocatalysts
4.1. Electrical Properties
4.2. Optical Properties
4.3. Sustainability
5. Ferroelectric Material as a Photocatalyst to Reduce Carbon Dioxide
5.1. Bismuth Ferrite (BiFeO3)
5.2. Barium Titanate
5.3. Lead Zirconate Titanate (Pb(Zr,Ti)O3 or PZT)
5.4. Strontium Titanate (SrTiO3)
5.5. Barium Strontium Titanate (Ba,Sr)TiO3
6. Results and Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Application | Description |
---|---|
Air Purification | Photocatalytic systems can decompose volatile organic compounds (VOCs), bacteria, and viruses using TiO2 under UV light. |
Water Treatment | It can break down harmful contaminants into harmless byproducts, making it an essential technology for clean water initiatives. |
Hydrogen Production | Water splitting using photocatalysts can generate hydrogen fuel from water under sunlight. |
CO2 Reduction | Converting CO2 into hydrocarbons or alcohols provides a method for carbon recycling and energy storage. |
Environmental Remediation | Photocatalytic systems can be used for the decomposition of pollutants in air and water. |
Energy Conversion | Photocatalysts can be used in the production of renewable energy, such as hydrogen fuel. |
Utilizing Polarization Effects | Switchable Polarization The inherent polarization in ferroelectric materials can influence surface chemistry. By switching the polarization direction, the adsorption strengths and reaction pathways for reactants can be altered, leading to enhanced catalytic activity. This phenomenon allows selective adsorption of reactants based on the polarization state, effectively overcoming limitations imposed by traditional catalytic principles like Sabatier’s principle. |
Band Bending The polarization-induced band bending at the ferroelectric/semiconductor interface promotes charge separation and transfer, enhancing photocatalytic performance. This built-in electric field facilitates the separation of photogenerated electrons and holes, increasing their availability for redox reactions. | |
Surface Modification | Atomic Dispersion Modifying the surface with atoms that bind strongly can increase reactivity. These atoms can remain dispersed on the surface, enhancing catalytic properties without clustering, which often diminishes reactivity. |
Functional Group Introduction The incorporation of various functional groups (e.g., -OH, -O, -F) on the surfaces of ferroelectric materials like MXenes can modify their electronic properties and enhance their chemical reactivity. This approach tailors the surface for specific reactions by adjusting the electronic environment. | |
Nanostructuring and Thin Films | Thin Film Technologies Utilizing ultrathin ferroelectric films (e.g., less than 3 unit cells thick) allows for easier polarization switching and enhanced surface reactivity due to increased surface-to-volume ratios. This also facilitates better control over the material’s properties and enhances its catalytic performance. |
2D Ferroelectric Materials Emerging 2D ferroelectric materials exhibit high stability and larger reaction surfaces compared to traditional bulk materials. Their unique properties make them promising candidates for high-efficiency catalysis processes. | |
Electrostatic Potential Engineering | Charge Imbalance Compensation The charge imbalance created by polarization can be compensated through electronic reconstruction or atomic rearrangement on the material’s surface. This process can enhance the catalytic efficiency by optimizing the active sites available for reactions. |
Hybrid Systems with Catalytic Metals | Supported Catalytic Metals Integrating ferroelectric materials with metal catalysts can enhance overall reactivity. However, challenges arise from metal clustering; thus, maintaining a well-dispersed state is crucial for effective interaction between the metal and ferroelectric surfaces. |
Material | Synthesis Method | Morphology | Band Gap (eV) | CO2 Conversion Rate (%) |
---|---|---|---|---|
BiFeO3 | Sol–gel | NPs | 2.1 | 20 |
BaTiO3 | Hydrothermal | Nanosheets | 3.2 | 15 |
PZT | Solvothermal | Nanofibers | 3.9 | 25 |
SrTiO3 | Co-precipitation | NPs | 3.2 | 18 |
KNbO3 | Solid-state | Nanosheets | 3.0 | 22 |
PbTiO3 | Sol–gel | Nanofibers | 3.4 | 30 |
Bi2WO6 | Hydrothermal | Nanosheets | 2.9 | 24 |
LaFeO3 | Solvothermal | NPs | 2.5 | 21 |
CaTiO3 | Co-precipitation | Nanosheets | 3.1 | 19 |
ZnO | Solid-state | Nanofibers | 3.2 | 27 |
Material | Synthesis Method | Morphology | Band Gap (eV) | Polarization (μC/cm2) | Charge Separation (%) | Light Absorption (%) | Surface Area (m2/g) | CO2 Conversion Rate (μmol/g·h) | Product Selectivity (%) | Stability (h) | Sensor Integration (%) |
---|---|---|---|---|---|---|---|---|---|---|---|
BiFeO3 | Sol–gel | Nanoparticles | 2.2 | 90 | 75 | 80 | 85 | 120 | 85 (CH4) | 100 | 92 |
BaTiO3 | Hydrothermal | Nanofibers | 3.2 | 26 | 65 | 70 | 95 | 90 | 75 (HCOOH) | 80 | 88 |
Pb(Zr,Ti)O3 | Solvothermal | Nanosheets | 3.4 | 35 | 72 | 75 | 90 | 110 | 80 (CH4) | 90 | 90 |
SrTiO3 | Solid-state | Nanocubes | 3.2 | 18 | 68 | 65 | 80 | 85 | 72 (HCOOH) | 70 | 85 |
(Ba,Sr)TiO3 | Co-precipitation | Nanoparticles | 3.0 | 30 | 70 | 75 | 92 | 100 | 82 (CH4) | 85 | 92 |
Bi4Ti3O12 | Molten-salt | Nanosheets | 2.9 | 40 | 75 | 80 | 88 | 115 | 78 (HCOOH) | 95 | 88 |
KNbO3 | Hydrothermal | Nanofibers | 3.1 | 22 | 62 | 68 | 90 | 80 | 70 (CH4) | 75 | 85 |
Pb(Mg1/3Nb2/3)O3 | Sol–gel | Nanoparticles | 3.0 | 38 | 68 | 72 | 85 | 95 | 75 (HCOOH) | 80 | 90 |
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Tong, M.; Li, X.; Zu, G.; Wang, L.; Wu, H. Artificial Neural Network Modeling Enhancing Photocatalytic Performance of Ferroelectric Materials for CO2 Reduction: Innovations, Applications, and Neural Network Analysis. Processes 2025, 13, 2670. https://doi.org/10.3390/pr13092670
Tong M, Li X, Zu G, Wang L, Wu H. Artificial Neural Network Modeling Enhancing Photocatalytic Performance of Ferroelectric Materials for CO2 Reduction: Innovations, Applications, and Neural Network Analysis. Processes. 2025; 13(9):2670. https://doi.org/10.3390/pr13092670
Chicago/Turabian StyleTong, Meijuan, Xixiao Li, Guannan Zu, Liangliang Wang, and Hong Wu. 2025. "Artificial Neural Network Modeling Enhancing Photocatalytic Performance of Ferroelectric Materials for CO2 Reduction: Innovations, Applications, and Neural Network Analysis" Processes 13, no. 9: 2670. https://doi.org/10.3390/pr13092670
APA StyleTong, M., Li, X., Zu, G., Wang, L., & Wu, H. (2025). Artificial Neural Network Modeling Enhancing Photocatalytic Performance of Ferroelectric Materials for CO2 Reduction: Innovations, Applications, and Neural Network Analysis. Processes, 13(9), 2670. https://doi.org/10.3390/pr13092670