Ligand impacts on band edge energies and excited state splittings of silicane
Bibliographic record
Abstract
Two-dimensional (2D) silicanes are promising semiconductors for applications in optoelectronics and photochemistry. Covalent functionalization presents a facile strategy to customize silicanes to attain desired properties. A comprehensive understanding of the impacts of ligands on silicanes is thus pivotal. In this study, we perform density functional theory (DFT) and time-dependent DFT (TDDFT) calculations to investigate the effects of three typical classes of ligands: (1) σ-withdrawing and π-donating, (2) σ-withdrawing and π-withdrawing, and (3) σ-donating and π-withdrawing, on the geometric structure, electronic structure, and band edge excited states of silicanes. Covalent functionalization of silicanes enables a wide range of band edge energies. The band gaps can be tuned between indirect and direct, and the underlying mechanism is explained for the first time. Additionally, TDDFT calculations confirm that the band edge optical absorptions can be adjusted by the ligands broadly from the near-infrared to the visible light region. These desirable properties enhance the functionalities of silicanes. Methodologically, we discuss the applicability of using the conventional one-particle orbital model to describe the excited states of silicanes, and nail down the ∼0.1 eV inaccuracy of the model in describing excited state splittings. We present empirical functions for quick estimations of band edge energies of covalently functionalized silicanes. Through careful comparisons, we justify the use of the carbon-adapted σ/π-donating/accepting indices of typical ligands in silicon chemistry and present a set of silicon-based indices for future prudent applications in inorganic computational studies of silicon-based materials.
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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 | 0.001 |
| 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".