Nano-MnO2 Decoration of TiO2 Microparticles to Promote Gaseous Ethanol Visible Photoremoval
Bibliographic record
Abstract
TiO2-based photocatalysis under visible light is an attractive way to abate air pollutants. Moreover, developing photocatalytic materials on a large-scale requires safe and low-cost precursors. Both high-performance TiO2 nanopowders and visible-light active noble metals do not match these requirements. Here, we report the design of novel Mn-decorated micrometric TiO2 particles. Pigmentary TiO2 replaced unsafe nano-TiO2 and firmly supported MnOx particles. Mn replaced noble metals such as Au or Ag, opening the way for the development of lower cost catalysts. Varying Mn loading or pH during the impregnation affected the final activity, thus giving important information to optimize the synthesis. Photocatalytic activity screening occurred on the gas-phase degradation of ethanol as a reference molecule, both under ultraviolet (UV) (6 h) and Light Emitting Diode (LED) (24 h) irradiation. Mn-doped TiO2 reached a maximum ethanol degradation of 35% under visible light after 24 h for the sample containing 20% of Mn. Also, we found that an acidic pH increased both ethanol degradation and mineralization to CO2, while an alkaline pH drastically slowed down the reaction. A strict correlation between photocatalytic results and physico-chemical characterizations of the synthesized powders were drawn.
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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) | 0.001 | 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".