Synthesizing Efficient Quasi-one-dimension Titanium Dioxide Nanocatalyst for Enhanced Photocatalytic Degradation of Aqueous Organic Pollutants and Hydrogen Production
Bibliographic record
Abstract
This dissertation is focused on synthesizing Q1D TiO2-based nanocatalysts for degrading aqueous organic pollutants and producing H2. A facile alkaline hydrothermal process was used to synthesize Q1D TiO2 under different hydrothermal synthesis factors (reaction temperature, NaOH concentration and TiO2 precursor concentration). Hydrothermal synthesis factors significantly affected the Q1D TiO2 phase structure, crystal size, specific surface area (SSA), bandgap, photocatalytic activities. A Box-Behnken design (BBD) model was used to optimize the hydrothermal factors for synthesizing Q1D TiO2 with maximum photodegradation rate and H2 production rate. The optimized Q1D TiO2 with maximum photodegradation rate was further enhanced with partially reduced graphene oxide (RGO) (designated as GT) for degrading aqueous hazardous pollutants. The study also examined the impact of the RGO atomic oxygen-to-carbon (O/C) ratio on GT photocatalytic activities. The highest photocatalytic activity was observed when the RGO atomic O/C ratio was 0.130±0.003. Next, the GT photocatalyst was enhanced with Ag NPs (designated as Ag-GT). The highest photocatalytic activity was observed for a silver content of 10 wt% in the photocatalyst film. Finally, an atmospheric pressure plasma jet (APPJ) was employed to synthesize micrometer thick Ag nanoparticles modified TiO2 (Ag-TiO2) coatings, presenting a core-shell structure for degrading RhB and different pharmaceutical compounds using a solar light source. Ag-TiO2 coatings were characterized having a porous anatase phase, improved charge separation and visible light absorption. The highest photodegradation rate was observed for a silver content of 0.4wt% in the composite.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".