Full Spectrum Raman Excitation Mapping Spectroscopy
Bibliographic record
Abstract
A generalization of the Raman scattering (RS) spectrum, the Raman excitation map (REM) is a hyperspectral two-dimensional (2D) data set encoding vibrational spectra, electronic spectra and their coupling. Despite the great potential of REM for optical sensing and characterization with remarkable sensitivity and selectivity, the difficulty of obtaining maps and the length of time required to acquire them has been practically limiting. Here we show, with a simple setup using current optical equipment, that maps can be obtained much more rapidly than before (~ms to ~100 s now vs. ~1000 s to hours before) over a broad excitation range (here ~100 nm is demonstrated, with larger ranges straightforward to obtain), thus taking better advantage of scattering resonance. We obtain maps from different forms of carbon: graphite, graphene, purified single walled carbon nanotubes (SWCNTs) and chirality enriched SWCNTs. The relative speed and simplicity of the technique make REM a practical and sensitive tool for chemical analysis and materials characterization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".