Raman intensity and conjugation with participation of ordinary σ‐bonds
Bibliographic record
Abstract
Abstract This review covers Raman investigations over a wide range, but focused on the problem of conjugation with participation of ordinary σ‐bonds. The results show that σ–σ conjugation in a novel class of polymers, polydiakylmetallanes of the type [R2M]n, M = Si, Ge, Sn, studied recently, and σ–π conjugation in some organometallic compounds of Si, Ge and Sn, studied long ago, both manifest themselves in the Raman spectra qualitatively in a similar way to π–π conjugation in polyenes and lead to a significant increase in intensity of the lines, corresponding to stretching vibrations of the conjugated bonds, the effect being strongly dependent on mutual orientation of the latter. It was found that pre‐resonant enhancement of some lines in the Raman spectra of polymetallanes is exhibited only by macromolecules having a planar zig‐zag all‐anti conformation of the backbone. Less ordered conformations do not show this effect, in spite of the proximity of the position of their lowest energy electronic absorption band νe to the exciting line ν. Thus, proximity of νe to ν in the resonance denominator is shown to be necessary but not sufficient for the pre‐resonant increase in Raman intensity to occur. These experimental findings are in good accord with Shorygin's general views on the conjugation phenomenon and with his semi‐classical treatment of the Raman intensity problem, confirming an important role of the derivative ∂νe/∂Qi that reflects the specificity of molecular electronic structure. Copyright © 2001 John Wiley & Sons, Ltd.
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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".