Development Hollow Nanotubes as Catalysts for Advanced Oxidation Processes with Machine Learning Evaluation to Degrade Persistent Organic Pollutants in Wastewater Ref.No.SSTCRC2583
1. Introduction
Persistent organic pollutants (POPs) in wastewater, such as dyes, pharmaceuticals, and pesticides, pose significant environmental and health risks due to their resistance to conventional treatment methods. Graphitic carbon nitride (g-C3N4) is a promising metal-free photocatalyst for advanced oxidation processes (AOPs) due to its visible-light responsiveness, chemical stability, and cost-effectiveness. However, its photocatalytic efficiency is limited by low surface area and rapid electron-hole recombination. Doping g- C3N4 with boron (B) or co-doping with boron and manganese (B/Mn) in a hollow nanotube structure enhances charge separation, increases active sites, and tailors bandgap properties. Integrating ozonation into the AOP framework generates highly reactive hydroxyl radicals (?OH), complementing photocatalysis. Additionally, machine learning (ML) models can optimize and evaluate the treatment process by predicting degradation efficiency, identifying key operational parameters, and modeling complex interactions between catalyst properties, ozone levels, and pollutant characteristics. This project aims to develop B-doped and B/Mn-doped g- C3N4 hollow nanotubes for a combined photocatalysis-ozonation AOP system, with ML-based evaluation, to degrade POPs in wastewater, offering a sustainable and data-driven solution. While single-metal doping, ozonation, and ML applications in wastewater treatment have been studied separately, their integrated use with B/Mn co-doping in hollow nanotube morphology remains novel, promising enhanced efficiency and predictive accuracy.
2. Research Progress
From the perspective of advanced oxidation processes (AOPs), the degradation of persistent organic pollutants (POPs) in wastewater is highly sensitive to catalyst surface properties and reactive oxygen species (ROS) generation. Building on years of experience in photocatalyst development, our team has established robust methods for synthesizing and characterizing graphitic carbon nitride (g-C3N4)-based materials. Initial synthesis of B-doped g- C3N4 via thermal polymerization with boric acid as the boron source yielded a porous structure, confirmed through X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). These analyses revealed successful boron incorporation, increasing the surface area by 25% compared to pristine g- C3N4. Photocatalytic tests using dyes as a model pollutant demonstrated a 30-40% enhancement in degradation efficiency under visible light, attributed to improved charge separation and reduced electron-hole recombination. For the first time, we showed that boron doping introduces nitrogen vacancies, which act as active sites for ROS generation, as evidenced by electron paramagnetic resonance (EPR) spectroscopy.
Integration of ozonation into the AOP framework was explored using commercial g- C3N4, where ozone exposure increased POP degradation by 20-30% compared to photocatalysis alone, due to synergistic hydroxyl radical (?OH) formation. Preliminary tests with ozone-treated B-doped g- C3N4 showed a further 20-30% improvement, highlighting the role of surface vacancies in enhancing ozone activation. For B/Mn co-doping, initial experiments with manganese acetate as a precursor indicated partial formation of hollow nanotube morphology, confirmed by SEM, with ongoing optimization to achieve uniform nanotube structures. The team has also developed a novel method for controlling nanotube diameter via template-assisted synthesis, achieving a 40-50% increase in specific surface area compared to bulk g-C3N4, as measured by Brunauer-Emmett-Teller (BET) analysis.
In parallel, machine learning (ML) models have been implemented to evaluate and optimize the treatment process. Models, trained on a dataset of catalyst compositions, pH levels, and ozone concentrations, achieved 85% accuracy in predicting dyes degradation rates. Feature importance analysis revealed that ozone flow rate and boron content are critical parameters, guiding further experimental design. For the first time, we demonstrated that ML-driven optimization can reduce experimental iterations by 30-40%, accelerating catalyst development. Remaining steps include: (1) optimizing B/Mn co-doped g- C3N4 hollow nanotube synthesis, (2) conducting advanced characterization (e.g., transmission electron microscopy (TEM), BET, EPR) to validate nanotube morphology and electronic properties, (3) evaluating combined photocatalysis-ozonation performance against diverse POPs (e.g., tetracycline, phenols) under varied conditions, (4) developing advanced ML models (e.g., neural networks, gradient boosting) to predict degradation efficiency and optimize operational parameters, (5) assessing catalyst stability and reusability in ozonation environments, and (6) integrating ML predictions into pilot-scale testing.
3. Cooperation Required
-Access to advanced characterization facilities (e.g., TEM, EPR, synchrotron-based XPS) to analyze nanotube morphology, doping effects, and ozone-induced surface changes.
-Patent support: Assistance from intellectual property experts to draft, file, and secure a technical patent for the synthesis and application of B/Mn-doped g- C3N4 hollow nanotubes in combined photocatalysis-ozonation AOPs.
4. Benefits and Outputs
The project will develop highly efficient, cost-effective catalysts for a combined photocatalysis-ozonation AOP system, with ML models enhancing process optimization and predictive accuracy. This integrated approach could achieve superior POP degradation compared to standalone photocatalysis or ozonation, addressing critical environmental challenges. ML-driven insights will enable precise control of treatment parameters, reducing costs and improving scalability. Successful implementation will contribute to cleaner water resources, reduced ecological damage, and improved public health, with potential for commercialization in industrial wastewater treatment systems.
- 5 academic papers in high-impact journals, including one on ML applications.
- 1 technical patent on the synthesis and application of B/Mn-doped g-C3N4 hollow nanotubes for combined photocatalysis-ozonation AOPs with ML evaluation