Effects of Zn Doping and High Energy Ball Milling on the Photocatalytic Properties of TiO<sub>2</sub>
Bibliographic record
Abstract
TiO2 photocatalysis is been widely studied for air and water purification applications; titanium dioxide is the most used semiconductor principally because its low cost, stability and chemical properties. However it only utilizes the UV portion of the solar spectrum as an energy source (less than 4% of the total sunlight energy). This behavior is due to its high band gap value of 3.2 eV. The modification of light harvesting properties of TiO2 by doping has become an important research topic to achieve an efficient operating range under UV and visible light. In addition, the structure and surface properties of photocatalysts play an important role.\nThis thesis explores the effects of Zn doped TiO2, prepared by the sol-gel method, on its photocatalytic activity to decompose organics and the characterization of the doped samples. Since this study is part of a collaborative initiative, the samples were synthesized and provided by Dr. A. R. Phani from the Department of Physics, University of L'Aquila. Preliminary examination revealed a relatively low photocatalytic efficiency of the samples. The objective is to modify/improve its properties by high energy ball milling which is expected to generate accumulations of defects, particle size reduction and an increase in the active surface area.\nThe characterization of doped and mechanochemically treated materials will be analyzed by optical diffuse reflectance measurements and optical absorption calculations using the Kubelka-Munk approach. The phase structure and particle size of the materials will be determined using X-ray diffraction (XRD). The BET surface area of the samples will be obtained using an Autosorb instrument. The photocatalytic properties will be studied by the analysis of decomposition of Methyl Orange in an aqueous solution. An aqueous photocatalytic tubular reactor with capability of operation using UV and/or fluorescent light will be designed and built.
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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 | 0.001 |
| 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) | 0.001 | 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".