Expanding the Repertoire of Molecular Linkages to Silicon: Si–S, Si–Se, and Si–Te Bonds
Bibliographic record
Abstract
Silicon is the foundation of the electronics industry and is now the basis for a myriad of new hybrid electronics applications, including sensing, silicon nanoparticle-based imaging and light emission, photonics, and applications in solar fuels, among others. From interfacing of biological materials to molecular electronics, the nature of the chemical bond plays important roles in electrical transport and can have profound effects on the electronics of the underlying silicon itself, affecting its work function, among other things. This work describes the chemistry to produce ≡Si-E bonds (E = S, Se, and Te) through very fast microwave heating (10-15 s) and direct thermal heating (hot plate, 2 min) through the reaction of hydrogen-terminated silicon surfaces with dialkyl or diaryl dichalcogenides. The chemistry produces surface-bound ≡Si-SR, ≡Si-SeR, and ≡Si-TeR groups. Although the interfacing of molecules through ≡Si-SR and ≡Si-SeR bonds is known, to the best of our knowledge, the heavier chalcogenide variant, ≡Si-TeR, has not been described previously. The identity of the surface groups was determined by Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and depth profiling with time-of-flight-secondary ionization mass spectrometry (ToF-SIMS). Possible mechanisms are outlined, and the most likely, based upon parallels with well-established molecular literature, involve surface silyl radicals or dangling bonds that react with either the alkyl or aryl dichalcogenide directly, REER, or its homolysis product, the alkyl or aryl chalcogenyl radical, RE· (where E = S, Se, and Te).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".