The impact of cellulosic pulps on thermoforming process: effects on formation time and drainage efficiency
Bibliographic record
Abstract
Abstract The growing environmental and health concerns associated with plastic pollution have driven the search for sustainable alternatives. This study investigated the impact of various types of cellulosic pulps and degree of refining on the production of thermoforming eco-friendly fiber-based materials as alternatives to plastics. By examining the influence pulp and fibers characteristics, the study aimed to correlate these factors with the two process parameters, formation time and drainage efficiency. Trays with a target dry weight of 31 g were produced using slurry consistency of 0.2 % and 0.8 % on an industrial molding machine. In this study, formation times required to achieve the target weight are varied from 0 to 42 s, influenced by pulp type, refining level, and slurry consistency showing that the longest time can affect the quality. Higher refining levels extended formation time, making it crucial to adjust slurry consistency to optimize production efficiency. Formation trials revealed that most pulps followed a logarithmic formation pattern at both consistencies. Dryness and drainage gain varied significantly across pulp types. Hardwood pulps exhibited the highest initial dryness, while alternative fibers like canola had the lowest, making them longer to dry. Recycled and mechanically pulped fibers retained more water due to fines content, further decreasing dewatering. Additionally, increased refining levels decreased both the initial dryness and the gain in dryness over equal drainage times. Since dryness directly influences drying time and energy consumption, optimizing pulp selection and refining strategies are essential for enhancing cost efficiency in thermoformed fiber production.
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How this classification was reachedexpand
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".