From Molecules to Surfaces: Radical-Based Mechanisms of Si–S and Si–Se Bond Formation on Silicon
Bibliographic record
Abstract
The derivatization of silicon surfaces can have profound effects on the underlying electronic properties of the semiconductor. In this work, we investigate the radical surface chemistry of silicon with a range of organochalcogenide reagents (comprising S and Se) on a hydride-terminated silicon surface, to cleanly and efficiently produce surface Si-S and Si-Se bonds, at ambient temperature. Using a diazonium-based radical initiator, which induces formation of surface silicon radicals, a group of organochalcogenides were screened for reactivity at room temperature, including di-n-butyl disulfide, diphenyl disulfide, diphenyl diselenide, di-n-butyl sulfide, diphenyl selenide, diphenyl sulfide, 1-octadecanethiol, t-butyl disulfide, and t-butylthiol, which comprises the disulfide, diselenide, thiol, and thioether functionalities. The surface reactions were monitored by transmission mode Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy, and time-of-flight secondary ionization mass spectrometry. Calculation of Si-Hx consumption, a semiquantitative measure of yield of production of surface-bound Si-E bonds (E = S, Se), was carried out via FTIR spectroscopy. Control experiments, sans the BBD diazonium radical initiator, were all negative for any evident incorporation, as determined by FTIR spectroscopy. The functional groups that did react with surface silicon radicals included the dialkyl/diphenyl disulfides, diphenyl diselenide, and 1-octadecanethiol, but not t-butylthiol, diphenyl sulfide/selenide, and di-n-butyl sulfide. Through a comparison with the rich body of literature regarding molecular radicals, and in particular, silyl radicals, reaction mechanisms were proposed for each. Armed with an understanding of the reaction mechanisms, much of the known chemistry within the extensive body of radical-based reactivity has the potential to be harnessed on silicon and could be extended to a range of technologically relevant semiconductor surfaces, such as germanium, carbon, and others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".