Binary functionalization of H:Si(111) surfaces by alkyl monolayers with different linker atoms enhances monolayer stability and packing
Bibliographic record
Abstract
Alkyl monolayer modified Si forms a class of inorganic-organic hybrid materials with applications across many technologies such as thin-films, fuel/solar-cells and biosensors. Previous studies have shown that the linker atom, through which the monolayer binds to the Si substrate, and any tail group in the alkyl chain, can tune the monolayer stability and electronic properties. In this paper we study the H:Si(111) surface functionalized with binary SAMs: these are composed of alkyl chains that are linked to the surface by two different linker groups. Aiming to enhance SAM stability and increase coverage over singly functionalized Si, we examine with density functional theory simulations that incorporate vdW interactions, a range of linker groups which we denote as -X-(alkyl) with X = CH2, O(H), S(H) or NH(2) (alkyl = C6 and C12 chains). We show how the stability of the SAM can be enhanced by adsorbing alkyl chains with two different linkers, e.g. Si-[C, NH]-alkyl, through which the adsorption energy is increased compared to functionalization with the individual -X-alkyl chains. Our results show that it is possible to improve stability and optimum coverage of alkyl functionalized SAMs linked through a direct Si-C bond by incorporating alkyl chains linked to Si through a different linker group, while preserving the interface electronic structure that determines key electronic properties. This is important since any enhancement in stability and coverage to give more densely packed monolayers will result in fewer defects. We also show that the work function can be tuned within the interval of 3.65-4.94 eV (4.55 eV for bare H:Si(111)).
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".