Simulation of mass and heat transfer of Quercus leaf particles during hydrothermal extraction
Bibliographic record
Abstract
Abstract Mass and heat transfer models of Quercus leaf particles during hydrothermal extraction were developed in COMSOL Multiphysics. The main physical properties used in the model were measured. The results showed that the average particle size was 116.7 μm, and after soaking in water for 24 h, the average particle size increased to 127.3 μm. However, the average particle size increased from 116.7 to 132.8 μm after soaking in a 60% (v/v) ethanol solution for 40 min, and decreased to 127.3 μm after 60 min. The Quercus leaf particles have a higher ability to absorb ethanol solution than water. The simulations showed that it took 3.2 s for the ethanol concentration and 0.5 s for the temperature in the particles to tend stably after soaking in a 60% (v/v) ethanol solution at 40°C. From the edge of the particle to the centre, the increasing rates of ethanol concentration and temperature gradually slowed down, while the changing rates of the centre accelerated with the increase in solution concentration and temperature, respectively. When the particles soaked at the lowest concentration, the central concentration first reached stability, and at the lowest temperature, the centre temperature first stabilized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".