| Author | : Zhuoying Jiang |
| File Size | : 55,5 Mb |
| Publisher | : Unknown |
| Language | : English |
| Release Date | : 03 May 2024 |
| ISBN | : OCLC:1315489691 |
| Pages | : 206 pages |
Smart Photocatalytic Building Materials for Autogenous Improvement of Indoor Environment by Zhuoying Jiang Book PDF Summary
As people spend most of their time inside the buildings, the improvement of the indoor air quality has received considerable attention. The major contaminants inside the building is volatile organic compounds (VOCs) referred to the carbon-contained organic substances in the air. VOCs are usually not acutely toxic, but they cause an adverse health effect when human are exposed to a concentration of ppmv level of VOCs. Thus, it is critical to mitigate the VOCs level inside the building. To achieve the purpose of removing VOCs and improving the indoor environment, an innovative photocatalytic membrane we designed and fabricated. This new photocatalytic material can be applied to the indoor surface and used as a smart functional surface. Furthermore, the fundamentals related to its photocatalytic activities and practical applications were explored by integrating the experimental, physics-based and data-driven approaches. Nitrogen-doped TiO2 photocatalysts were synthesized using a sol-gel method and a post-annealing heat treatment. The annealing temperature and time affect their microstructures and surface chemical compositions. It was found that these characteristics are relevant to the adsorption and photocatalytic activities of the nitrogen-doped TiO2 photocatalysts. Therefore, a physics-based kinetic model was developed to distinguish the impact of three different mechanisms, including adsorption, photocatalysis, and direct light photolysis, on the removal of VOCs. The kinetic modeling and experimental results show that a higher annealing temperature leads to not only less adsorption, but also nitrogen loss. To predict the kinetics of contaminant degradation and facilitate the choice of the optimal photocatalyst, three data-driven machine-learning (ML) models were developed to predict the photocatalytic degradation performance. The ML model inputs include tens of organic contaminants and other experimental variables, including light level, photocatalyst dosage, concentration of contaminant, initial pH of the solution, and experimental temperature. It was found that the model predictions match reasonably well with the experimental results with the R2 of around 0.8. The functional surface, containing photocatalyst particles and nanofiber substrates, was fabricated. Three methods were used to fabricate the functional surface: direct-dispersing, post-dipping and post-electrospraying method. Post-electrospraying is a better approach since it is able to load TiO2 without reducing photocatalytic activity, and the amount of TiO2 on fiber can be tuned by electrospraying time. To evaluate the performance of the functional surface activated by visible light in removing indoor VOCs, a holistic model was developed. The kinetic simulations show that VOCs can be decomposed using nitrogen-doped TiO2 functional surface by 80% in an actual room size. As a whole, our work demonstrates the promise of photocatalyst driven by visible light to effectively remove VOCs and create a healthy indoor environment.