Optimisation of the preparation process of phosphorus-containing organosilicon compounds based on molecular dynamics simulation
Bibliographic record
Abstract
In this paper, the structures of three phosphorus-containing organosilicon compounds, including N,N-di-methylenephosphoric acid n-propylamine (DPPA), N,N-di-methylenephosphoric acid aminopropyldimethylsilanol (DPDS), and N, N-di-methylenephosphoric acid aminopropyldimethylsilylene glycol (DPMS), have been designed by using a molecular dynamics simulation method. And the preparation of three phosphorus-containing organosilicon compounds was accomplished experimentally by using raw materials such as bisphenol A-type epoxy organosilicon, n-propylamine and phosphite. The structures of the above several substances were proved by means of characterization such as Fourier infrared spectroscopy, hydrogen NMR , and epoxy value. Molecular dynamics simulation analysis revealed that the bond lengths of N atoms to Si atoms, N atoms to O atoms, and N atoms to were 3.03 Å, 3.05 Å, and 2.85 Å, respectively. Si did not participate in the addition reaction, but the intermolecular interactions caused a change in the chemical environment of Si, which reduced intermolecular distances and made it easier for the phosphorus groups to aggregate. This study is very important for the development of new preparation strategies of phosphorus-containing organosilicon and the promotion of phosphorus-containing organosilicon industry.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".