<scp>4D</scp> printing of mixed vegetable gel based on deformation and discoloration induced by acidification and dehydration
Bibliographic record
Abstract
Abstract This research was aimed at exploring the feasibility of 4D printing of Chinese cabbage puree–carrot powder–xanthan gum mixed gel system using white vinegar and hot air dehydration (HAD) as stimuli to realize double change of shape and color. Firstly, 4D printability of five mixed vegetable gels (MVG) were characterized by water distribution and rheological properties. The results showed that apparent viscosity, G′, G″, G* and yield stress were positively related to carrot powder content, while transverse relaxation time (T2) were negatively related to it. Next, the formula with 15% carrot powder content was chosen as printing ink due to its better 4D printing behavior. The results showed that bending angle reached the maximum (371.01°) at 210 min, and initial green color completely turned brown at 120 min. Finally, complex models such as butterfly and four‐petal flowers were further applied to verify the feasibility of this 4D food printing. Practical Applications This study provides a simple but innovative method for simultaneous deformation and discoloration of 3D printed objects. These procedures would allow chlorophyll‐rich vegetarian dishes show deformation and discoloration before consumption for added interest.
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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".