Step-down RH improves plums drying behavior, quality attributes, and 3E (energy, environmental, economic) equipment performance by changing its ultrastructure and water distribution
Bibliographic record
Abstract
The energy consumption of China's drying industry accounts for about 12% of the total industrial energy consumption, and material crusting is one of the main reasons for high energy consumption in drying. Studies have shown that increasing the drying medium’s relative humidity (RH) can inhibit material crusting and shorten drying time, yet the underlying mechanism remains unclear. Our energy flow analysis reveals that high RH during the initial drying stages inhibits heat absorption by moisture evaporation from the material surface while increasing the heat transfer rate between the drying medium and the material. Electron probe microscopy observations of plum ultrastructure show that this elevated heat transfer rate disrupts the cell membrane and wall, creating microporous channels for moisture migration. The relaxation time of hydrogen protons further indicates that high RH increases water mobility within the plum. Proton density maps of plums during step-down RH drying demonstrates a more uniform moisture distribution than the conventional continuous dehumidification drying, improving drying efficiency. This approach reduces carbon emissions, shortens the payback period, and increases the total phenolic content and antioxidant activity of dried plums. This study elucidates the mechanism by which relative humidity influences drying efficiency, thereby offering critical insights for the advancement of energy-efficient, high-quality, and environmentally sustainable drying technologies. These advancements hold the potential to significantly contribute to carbon emission reduction within sustainable agro-processing and drying industries.
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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) | 0.002 | 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".