The adsorption of SO<sub>2</sub> on TiO<sub>2</sub> anatase nanoparticles: a density functional theory study
Bibliographic record
Abstract
First-principles calculations have been carried out to investigate the adsorption properties of SO2 molecules on nitrogen-doped TiO2 anatase nanoparticles using the density functional theory method to fully exploit the gas-sensing capabilities of TiO2 particles. For this purpose, we have mainly studied the adsorption of the SO2 molecule on the dangling oxygen atom and doped nitrogen atom sites of the TiO2 nanoparticles because these sites are more active than other sites in the adsorption processes. The complex systems consisting of the SO2 molecule positioned toward the undoped and nitrogen-doped nanoparticles have been relaxed geometrically. The results presented include structural parameters such as bond lengths and bond angles and energetics of the systems such as adsorption energies. The electronic structure and its variations resulting from the adsorption process, including the density of states, molecular orbitals, and the charge transfer, are discussed. We found that the adsorption of the SO2 molecule on the nitrogen-doped TiO2 nanoparticles is energetically more favorable than the adsorption on the undoped ones. These results thus provide a theoretical basis for the potential applications of TiO2 nanoparticles in the removal and sensing of SO2 and give an explanation for helping in the optimization of improved gas removers and sensor devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".