Lightweight, mechanically robust and scalable cellulose-based foam enabled by organic-inorganic network and air drying
Bibliographic record
Abstract
Lightweight and high-strength cellulose-based foams have gained increasing momentum due to their combination of sustainability and high performance. However, complex modification to cellulose fibers can sacrifice the environmental friendliness and further limit productional scalability. Here, we engineer lightweight yet strong cellulose-based foams made from mechanically treated microfibrillated cellulose (MFC), reinforced by organic-inorganic network, and scaled by surfactant foaming combined with air drying. Consisting of strong coordination and extensive secondary interaction within the organic-inorganic network, the resulting cellulose composite foams achieve both a high compressive modulus of 451.3 kPa and a yield strength of 25.1 kPa at a low density of 32.9 mg cm−3, which exceed other MFC-based foams based on surfactant foaming. In addition, the incorporation of the organic-inorganic network has no negative effect on the scalability, recyclability, or biodegradability of cellulose composites foam, making closed-loop material recycling possible. The life-cycle assessment reveals that replacing petroleum-based foams with our cellulose composite foams result in substantial reductions in carbon emissions. The structural design and manufacturing of our cellulose-based foam can stimulate market interest for cellulose foam and the development of the bioeconomy.
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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".