Biodegradable Wastepaper-Based Foam with Ultrahigh Energy-Absorbing, Excellent Thermal Insulation, and Outstanding Cushioning Properties
Bibliographic record
Abstract
A new biodegradable foam with low density (0.065–0.081 g/cm 3 ), high porosity (>92%), low thermal conductivity (0.044 W/mK), excellent mechanical properties, and outstanding cushioning properties was prepared by a simple green method using wastepaper without any chemical pretreatment as the raw material. The porous structure and the reinforced cell skeleton formed by the wastepaper fibers coupled with PVA and gelatin, respectively (“bridge-linking” and “membrane-linking” structure), give the foam excellent coordination between thermal insulation and energy absorption properties, which are typically considered incompatible with each other in conventional cushioning foam materials. Furthermore, by impregnating a shear thickening fluid (STF) into wastepaper-based foam, a biodegradable cushioning packaging material with ultrahigh energy absorption for product delivery in extreme environments, such as parachute-free airdrops, was successfully prepared. The findings demonstrate that when the external impact velocity surpasses the critical shear rate of STF, the increased mass fraction and content of STF can enhance the energy absorption properties of the wastepaper-based foam, significantly enhancing its dynamic cushioning performance (STF can decrease the maximum impact acceleration by up to 85%).
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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) | 0.001 | 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".