Theoretical Study of Work Function Modification by Organic Molecule-Derived Linear Nanostructure on H−Silicon(100)-2 × 1
Bibliographic record
Abstract
Tuning of the electronic properties of semiconductors can be achieved by surface modification with organic molecules. In this work, we study, by periodic density functional theory, the change in work function that occurs upon the modification of nominally hydrogen-terminated Si(100)-2 × 1 by chemisorption of substituted styrene molecules. Our results show that monolayers derived from 4-X-styrene molecules, with X being electron donating groups or hydrogen, decrease the work function of the system. Conversely, monolayers derived from 4-X-styrene molecules, with X being electron withdrawing groups, increase the work function of the system. For the molecules used in the modeling, the calculations indicate that the work function can be substantially modified from −1.4 eV (X N(CH 3 ) 2 ) to +1.9 (X NO 2 ) eV relative to H−Si(100)-2 × 1. Because the direction and magnitude of charge transferred upon chemisorption is the same for all molecules, the work function changes are not the result of band bending. The work function modification comes exclusively from the inherent dipoles of the molecules chemisorbed on the surface. The computed dipoles for the monolayers range from −1.3 (X N(CH 3 ) 2 ) to +1.4 (X NO 2 ) Debye. We conclude that substantial local control over some of the electronic properties of silicon can be achieved by the chemisorption of dipole-containing molecules.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".