Evaluating the Aqueous Stability of Alkyl‐/Aryl‐Hydrosilanes by NMR Spectroscopy and GC‐MS
Bibliographic record
Abstract
Hydrosilanes are commonly used as reducing agents or as synthetic precursors for silanols. However, the incorporation of hydrosilanes as carbon bioisosteres is underexplored. In this study, the hydrolytic stability of ten variably substituted hydrosilanes—including monoaryl, monoalkyl, diaryl, dialkyl, alkyl aryl, triaryl, trialkyl, dialkyl aryl, and alkyl diaryl silanes—is investigated using five complementary methods, including 1 H–NMR time‐lapse and GC‐MS experiments, at neutral pH. The 1 H–NMR time‐lapse experiments suggest that monoaryl and monoalkyl silanes are susceptible to hydrolysis, as evidenced by 31% and 22% reduction in starting material, respectively, over 24 h. Other investigated silanes are resistant to hydrolysis in these solvent systems for at least 24 h. The GC‐MS experiments quantitatively support the respective reactivity of these hydrosilanes at pH 7. Lastly, the reactivity of selected hydrosilanes is evaluated at pH 7.4 phosphate‐buffered saline buffer; only monoalkyl silanes degraded in the presence of the added salt content. Overall, the study demonstrates that hydrosilanes exhibit hydrolytic stability at neutral pH, except for monoaryl‐ and monoalkyl‐substituted silanes, which are susceptible to degradation. The results provide insight into the likelihood of the SiH bond surviving in aqueous environments, opening the door for a wider variety of silicon‐containing molecules in drug discovery.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".