DFT investigation on interaction of chlorine with In<sub>2</sub>O<sub>3</sub> nanostructures
Bibliographic record
Abstract
The structural, electronic, and adsorption properties of chlorine on pristine, tin-, aluminum-, and fluorine-substituted In2O3 nanostructures are successfully optimized and computed using density functional theory along with the B2LYP/LanL2DZ basis set. The electronic properties of pristine, tin-, aluminum-, and fluorine-substituted In2O3 nanostructures are discussed in terms of ionization potential, HOMO–LUMO gap, and electron affinity. The dipole moment and point symmetry group of In2O3 nanostructures are also reported. The structural stability of pristine, tin-, aluminum-, and fluorine-substituted In2O3 nanostructures are investigated in terms of formation energy. The adsorption properties of chlorine on In2O3 are studied and the most appropriate adsorption sites of Cl2 on In2O3 nanostructures are reported. The adsorption properties of hydrogen on In2O3 nanostructures are also investigated and inferred that In2O3 exhibits good sensing characteristics towards hydrogen. The adsorbed energy, HOMO–LUMO gap, Mulliken population analysis, and average energy gap variation are used to identify the prominent adsorption site of Cl2 on In2O3 material. The substitution of fluorine in In2O3 nanostructures enhances the Cl2 adsorption properties in the mixed gas atmosphere.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".