Effect of Ultrasound on Henna Leaves Drying and Extraction of Lawsone: Experimental and Modeling Study
Bibliographic record
Abstract
The effect of drying temperature and the application of ultrasound on drying kinetics of Lawsonia inermis (henna) leaves and the extraction of lawsone from the dried samples was addressed. Indeed, henna leaves were dried with and without the application of ultrasound (21.7 kHz, 30.8 kW/m3) at 40, 50 and 60 °C with a constant air velocity (1 m/s). As expected, both the increase of temperature and the application of ultrasound decreased the drying time and increased the rate of extraction of the lawsone. The values of the effective diffusion coefficients obtained were used to quantify this influence showing the value increases with higher drying temperature and the application of ultrasound. Moreover, the influence of temperature was quantified by the estimation of the activation energy from an Arrhenius-type equation (46.25 kJ/mol in the case of drying without ultrasound application and 44.06 kJ/mol in the case of ultrasonically-assisted drying). Regarding the influence of studied variables on lawsone extraction yield, the higher is the temperature, the lower is the yield, probably linked with lawsone degradation reaction due to thermal treatment. On the contrary, the application of ultrasound improved the extraction yield mainly at the lower drying temperature tested of 40 °C.
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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".