Dehydration mechanisms in electrohydrodynamic drying of plant-based foods
Why this work is in the frame
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Bibliographic record
Abstract
Electrohydrodynamic drying (EHDD) is an energy-efficient and non-thermal technique for dehydrating heat-sensitive biological materials, like fruits, vegetables, or medicinal plants. Although this method has been studied for more than three decades, still little is known about the relative contribution of the different dehydration mechanisms in EHDD. An accurate understanding of the impact of the different EHD-driven mass transfer processes inside the food and its surrounding air is essential for a targeted future optimization and successful upscaling of EHDD technology. Examples of these dehydration mechanisms are convective moisture removal, electroporation of the cell membrane, or electro-osmotic flow in the fruit. In this modeling study, we first identify possible dehydration mechanisms for mass transfer during the EHDD process of plant-based food materials. Using available theoretical models, we then estimate the relative contribution of each dehydration mechanism to the overall mass transfer during the constant rate period and rank them based on their contribution. We show that convective dehydration by ionic wind is the dominant dehydration mechanism, with a contribution of about 93% to the overall water flux for a capillary-porous material. Cell-membrane electroporation is the second important driving force that increases the contribution of the transmembrane water flow to about 6.5% of the total mass flux in fruit tissue. The contribution of all the other water transport mechanisms is only 0.5%. These insights provide a stepping stone towards developing a full physics-based model of the dehydration process by EHD, including the falling rate period.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it