Revisiting the liquid mull technique to derive the infrared optical constants of organic powders from transmission IR spectroscopy
Bibliographic record
Abstract
The determination of the refractive index (n) and extinction coefficient (k) of a compound is a key step for the prediction of its optical properties in different morphological states. For a solid, these optical constants can be obtained experimentally by spectroscopic methods such as ellipsometry and single-angle reflectance spectroscopy. However, in the context of sustaining databases for hyperspectral imaging with the n and k values of hazardous or noxious chemicals, these methods are not always conceivable due to the unobtainability of a single crystal or the hazards associated with heating, ball-milling, grinding and/or pressing the compounds. Hence, exploring a complementary preparation technique holds great interest. To this end, this work revisits the mull technique, which is classically used to perform qualitative FTIR transmission spectroscopy of a powder by trapping it in a mineral oil such as Nujol. By adding gravimetric measurements during sample preparation, this study seeks to provide a quantitative method satisfactory for deriving the absorption coefficient (α) and, consequently, the extinction coefficient (k).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".