Impact of fluorine and chlorine doping on the structural, electronic, and optical properties of SnO<sub>2</sub>: first-principles study
Bibliographic record
Abstract
In this work, we carried out in-depth study of the structural, electronic, and optical properties of intrinsic, fluorine (F)- and chlorine (Cl)-doped SnO2, using a pseudo-potential plane-wave scheme in the framework of the density functional theory. We found that the substitution of oxygen by F or Cl elements slightly modified the crystalline parameters without altering the stability of SnO2 compounds. The doping of tin oxide by these two halogens is confirmed by the displacement of the Fermi level position to the conduction band. Consequently, the doped materials are strongly degenerated as illustrated by the Moss-Burstein shift: 2.310 and 2.332 eV for F:SnO2 and Cl:SnO2, respectively. On the other hand, the density of states and Mulliken population analysis show that the covalent character of Sn–O bond is maintained after doping, while Sn–X (X = F or Cl) bond reveals an ionic nature. In terms of optical properties after doping, intrinsic SnO2 exhibits low absorption, while the doped ones are transparent in the visible range, making them more efficient in photovoltaic applications. Moreover, in the ultraviolet (UV) scale, pure and doped tin oxide compounds show better absorption, which may be beneficial for use in devices of protection against UV light and UV absorbers or sensors. Finally, the plasma frequencies of 28.22, 29.16, and 27.67 eV for pure, F-, and Cl-doped SnO2, respectively, were obtained.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".