Modelling diffusion of fragrances: A radial perspective
Bibliographic record
Abstract
Abstract In the present study, a theoretical model based on Fick's second law for radial diffusion is proposed for modelling the radial diffusion of fragrances. For that, three fragrance systems were studied containing α‐pinene and limonene as pure components, or a mixture of α‐pinene, limonene, linalool, and geranyl acetate. This model combines the UNIFAC group contribution method for the vapour‐liquid equilibrium with the Fickian radial diffusion model. The experimental gas concentrations of the odorant components were measured in a diffusion tube (1D axial diffusion) and quantified using gas chromatography with a flame ionization detector. The numerical solutions were obtained using the general Process Modelling Systems (gPROMS) software version 4.2.0. An equivalence relation between the 1D axial diffusion model and 1D radial diffusion model was developed. Finally, the odour intensity and character of the studied fragrance systems were assessed using Stevens’ power law and the strongest component model, respectively. The obtained results showed good agreement between the numerical simulation and the experimental gas concentration data, suggesting the proposed methodology as an efficient tool to assess the radial diffusion of fragrance systems over time and distance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".