High-Resolution Spectra of Carbon Nanoparticles: Laboratory Simulation of the Infrared Emission Features Associated with Polycyclic Aromatic Hydrocarbons
Bibliographic record
Abstract
Using surface-enhanced Raman spectroscopy we have obtained high-resolution spectra of individual molecular components containing 10 2 -10 3 carbon atoms at the surface of carbon nanoparticles. A number of well-defined spectral features occur in the 6.2, 7.6, 7.8, and 8.6 μm wavelength range that replicate those found in the infrared emission spectrum of interstellar and circumstellar sources. This suggests that the molecular components present in laboratory samples are of similar composition to those occurring in space and provides additional insight into the chemical species responsible for infrared emission at these wavelengths. In particular, we find that laboratory spectra are produced by a relatively small number of molecular groups containing C=C bonds. These include large polycyclic aromatic hydrocarbons and polyacetylenic chains. Spectral features characteristic of type A, AB, B, and C infrared emission sources appear in different samples, indicating that the observed wavelength variations in astronomical sources can be attributed to changes in molecular size and composition. We also find that the infrared emission feature detected at ≈8.6 μm in type A and B sources may arise from polyacetylenic species. Spectral profiles of laboratory bands are in good agreement with those of observed astronomical features. The broad background underlying the astronomical bands over the 6-9 μm (1660-1100 cm -1 ) range is also reproduced in laboratory spectra.
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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".