Upcycling Industrial Biomass Wastes Into Aerogels Using Zinc Chloride Salt Hydrates
Bibliographic record
Abstract
Abstract End‐of‐life paper products, including food packages and rejected fibers from the paper industry, are unrecyclable biomass wastes and thus generally landfilled or incinerated. One of the major obstacles to their recycling is the existence of impurities besides cellulose fiber in the biomass wastes. In this study, a fabrication method is investigated to upcycle biomass wastes directly into high‐performance aerogels without separation of impurities. Surprisingly, this study observes that residual impurities participate in cross‐linking reactions for the aerogel formation. In this study, zinc chloride salt hydrate is applied to convert biomass wastes to aerogel via a dissolution‐regeneration process. The fabricated aerogels exhibited high water absorption capacity (15 times its weight), as well as comparable mechanical strength and thermal insulation performance to the reported cellulose aerogels. In addition, the impurities (i.e., calcium‐based inorganic salt) assisted in the cross‐linking of the cellulose network for the aerogel formation. The scanning electron microscopy (SEM) image of the aerogel generated from the rejected fibers showed a honeycomb inner structure. The rejected fiber aerogels also demonstrated a high compressive modulus of 51 MPa and a low thermal conductivity of 0.029 W m−1 K−1. The results for water absorption and thermal insulation suggest excellent potential across various application domains.
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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".