Study on the mechanism and law of temperature, humidity and moisture content on the mechanical properties of molded fiber products
Bibliographic record
Abstract
The change of temperature, humidity and moisture content (MC) will lead to the change of mechanical properties of molded fiber products (MFP). However, it is difficult to decouple the effects of temperature, humidity and MC on the mechanical properties of MFP, and predict the mechanical properties of MFP during the use. In this study, the laws and mechanism of mechanical properties of MFP with ambient temperature, humidity and MC were studied. The results showed that the direct effect of temperature (20−70 °C) on mechanical properties of MFP was insignificant, and the mechanical properties of MFP were mainly changed by MC. The MC was related to ambient temperature and humidity, and the relationship between the three could be described by the modified Guggenheim-Anderson-de Boer (GAB) model (20−70 °C and 30%−90% relative humidity). With the increase of MC, the elastic modulus and fracture strain was increased and decreased linearly, the yield strength and failure strength were presented GaussAmp laws, and the failure strain was presented asymptotic regressed distribution law. Two fracture modes of MFP, brittle fracture and ductile fracture, were revealed by the scanning electron microscopy of the mesoscopic fiber structure of sugarcane bagasse molded fiber products. The mathematical models and the changes of fiber structure were verified by wheat straw molded fiber products and waste paper molded fiber products. This study was contributed to understand the effects and mechanism of the change of temperature, humidity and MC on the mechanical properties of MFP.
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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 | 0.001 |
| 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".