Experimental methods in chemical engineering: <scp>Raman</scp> spectroscopy
Bibliographic record
Abstract
Abstract When photons impinge on a substrate, most scatter with the same frequency (elastic scattering or Rayleigh dispersion) and only 10−7 scatter with a different energy (inelastic scattering). This inelastic interaction (Raman scattering) exchanges energy in the region of molecular vibrational transitions for crystalline and amorphous materials. Raman bands in a spectra represent vibrational transitions, like infrared, however the selection rules are different. Typically, the vibrations that are intense in Raman are weak in infrared and vice versa. A remarkable feature of the Raman effect is that it is highly sensitive to nanocrystals, even below 4 nm, which are too small to generate XRD patterns. Plasmonic enhancement, like surface‐enhanced Raman spectroscopy (SERS) boost the Raman signal by 104, providing single‐molecule detection capability. Glass, quartz, and sapphire are transparent to Raman effect (depending on the energy of the incident excitation radiation), which makes it ideal to examine materials under reaction conditions (in‐situ cells and operando reactors that operate over a broad range of temperature, pressures, and environments). Raman spectroscopy emerged in the 1930s; however, infrared spectrometry displaced it. With the advent of powerful lasers in the 1970s, more researchers began to apply Raman routinely. In 2019, the Web of Science indexed 20 400 articles mentioning Raman against 50 000 articles mentioning infrared. Chemical engineers apply Raman less frequently than in material science, physical chemistry, and applied physics, with 887 articles vs 6250, 3700, and 3510 for the other disciplines. A bibliometric analysis identified four research clusters centred on thin films and optics, graphene and nanocomposites, nanoparticles and SERS, and photocatalyst.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.021 |
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".