Drying of Gelidium sesquipedale (Rhodophyta) Under Microwave Irradiation: Study of Thermal and Kinetic Aspects of the Drying Process
Bibliographic record
Abstract
Microwave technology is currently very interesting because it offers, in many industrial processes, a new alternative energy for drying and treatment of various materials. In this context, the drying of the Gelidium sesquipedale (Rhodophyta), which is a red algae, is carried out under microwave irradiation in the aim to optimize the energy consumption in the drying process and the quality of the extracted products. The drying process of Gelidium sesquipedale under microwave irradiation is carried out by studying the thermal and kinetic aspects of drying under different irradiation conditions. The experiments were performed on samples of different initial masses ranging from 2 to 30 g. The samples are irradiated in an experimental device combining a gravimetric balance and microwave assembly that may impose different power microwave ranging from 50 to 200 W. The analysis of the experimental curves of the drying process shows that the initial mass of the sample has little influence and the applied microwave power has a significant effect on the drying kinetics. The comparative study of the drying of Gelidium sesquipedale by conventional heating and under electromagnetic field shows that the controlling kinetic mechanism depends on the way in which the sample is dried. The action of the electromagnetic field allows faster heat transfer leading to the rapid evacuation of water molecules from the seaweed.
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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".