Theory of Covalent Adsorbate Frontier Orbital Energies on Functionalized Light-Absorbing Semiconductor Surfaces
Bibliographic record
Abstract
Functional hybrid interfaces between organic molecules and semiconductors are central to many emerging information and solar energy conversion technologies. Here we demonstrate a general, empirical parameter-free approach for computing and understanding frontier orbital energies - or redox levels - of a broad class of covalently bonded organic-semiconductor surfaces. We develop this framework in the context of specific density functional theory (DFT) and many-body perturbation theory calculations, within the GW approximation, of an exemplar interface, thiophene-functionalized silicon (111). Through detailed calculations taking into account structural and binding energetics of mixed-monolayers consisting of both covalently attached thiophene and hydrogen, chlorine, methyl, and other passivating groups, we quantify the impact of coverage, nonlocal polarization, and interface dipole effects on the alignment of the thiophene frontier orbital energies with the silicon band edges. For thiophene adsorbate frontier orbital energies, we observe significant corrections to standard DFT (∼1 eV), including large nonlocal electrostatic polarization effects (∼1.6 eV). Importantly, both results can be rationalized from knowledge of the electronic structure of the isolated thiophene molecule and silicon substrate systems. Silicon band edge energies are predicted to vary by more than 2.5 eV, while molecular orbital energies stay similar, with the different functional groups studied, suggesting the prospect of tuning energy alignment over a wide range for photoelectrochemistry and other applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".